How to build a modern audience segmentation strategy@2x

How to build a modern audience segmentation strategy

Imagine if you could always be in touch with your customer – at any time and any place. No spam, only real-time interactions that the customer really needs. Sounds complicated?

Just think about a few different cases that a customer can experience:

  • A customer undecided and stuck at the checkout – receives an auto-support chat message.
  • A customer not sure about the pricing plans – receives an explanation email.
  • A customer just entered your hotel or a store – receives a welcome message via SMS.
  • A customer is inactive for a few weeks –  receives a push notification with a win-back offer.

All these real-time interactions might seem challenging to track and orchestrate. However, with modern audience segmentation tools, you can smoothly capture all customer behavior, segment them and trigger personalized messaging campaigns across different data sources.

Let’s dive deeper and explore how you can improve your audience segmentation strategy and provide personalized experiences to increase your customer lifetime value significantly.

Streamline the customer data

Customers are engaging with brands across an increasing number of digital touchpoints. For businesses, the increased amount of customer data presents both an opportunity and a challenge. Detailed activity statistics provide insight into customer interests, tendencies, product usage, market trends, and more. However, using data effectively requires unrestricted access to that data, a system to ensure data quality (consistency and accuracy), and infrastructure to enable product and marketing teams to activate that data.

One of the most efficient ways to streamline the customer data is by implementing audience segmentation. Audience segmentation is the process of identifying cohorts within your customer base and designing user experiences based on their behavior. Customer segmentation is not a novel concept –  businesses have been creating customer segments for years. But the fact is that many of the segmentation concepts that brands have relied upon (for example, .csv exports or manual list builds) are resource and time inefficient. To activate data immediately and deliver real-time experiences,  you need a modern audience segmentation tool.

Building blocks of a modern segmentation engine

Audience segmentation demands specific audience analysis tools, accessible data-sources, and organizational capabilities. Once you have your infrastructure in place, you can start building your segments.

Here are the main building blocks of a modern segmentation engine:

  • Advanced customer data collection
  • Cross-channel identity resolution
  • Real-time audience infrastructure
  • Flexible segment grouping
  • Cross-channel targetingReal-time customer data collection

Advanced and highly targeted audience segments are built on quality customer data. The first step is to ensure that you have the infrastructure to track user events in real-time across different channels. If customers are engaging with your business across multiple digital channels (iOS, Android, website, etc.), you should use a Customer Data Platform (CDP) to collect event data. CDPs gather data from different platforms and merge it into a single customer database. Such infrastructure allows you to build audience segments based on cross-channel engagement.

There few critical points which are required to be considered when evaluating cross-channel data collections:

1. Data quality. Data collection must be accurate to be implemented consistently across platforms. You need to make sure that events are correctly tracked before sending them downstream.

2. Data access. Developers usually do a technical implementation, but marketers and product managers need to easily access and manipulate the data. Business users must have access to high-quality customer data and analyze it without additional technical difficulties.

3. Technical overhead. Implementing event collection can lead to developers’ ongoing technical tasks (updating SDKs, add event modifications, etc.). You need to make sure that engineers are not spending too much time on platform maintenance.

Intempt enables you to activate your customer data with minimal technical implementation required. Intuitive visual event and segment editor minimizes the need for developer involvement and streamlines the campaign building process. Quickly set up your sources and event collections across different channels and start building your audience segments within minutes.

Cross-channel identity resolution

To implement audience segmentation successfully, it’s vital to have a sophisticated identity management solution. When you’re collecting events from different platforms (mobile, web, POS, CRM, etc.), it can become challenging to understand how users engage with your brand. A single user can browse a product on your website, click on a relevant ad, open an email or SMS promoting that product, all before purchasing that product on the mobile app. Without the ability to link those actions to a unified customer profile, some of your systems may have no way of knowing that this purchase has already been made. In this case, customers will continue to receive promotional messaging even after a purchase. Such marketing is not only inefficient but also significantly degrades the user experience. The only solution here is to have an identity management solution like a CDP that can identify cross channel users and unify their profiles into one master user identity.

Example:

The user (let’s name her  Vanessa) visited an online store. Vanessa browsed several dresses, viewed different accessories, registered, but disappointed with the selection, exited the website.

After a few hours, Vanessa decided to give it another shot. She re-opened the homepage, this time, noticed more relevant dress offers.

While on the go, she downloaded the store’s app. Vanessa opened the app, logged in, and started browsing the dress offers immediately from the main screen.

What happened here?

After viewing the “Dress collection” category and accessing several other dress items, Vanessa was assigned to the “Category affinity: Dresses” segment. Once Vanessa re-opened the home page, the content changed to show personalized information for dress items.

The same happened once Vanessa opened the app. During login, she entered her email and was identified as the same customer browsing on the web. The content changed both in the app and web site of Intempt Demo Store.

After interacting with the brand across these two channels, Vanessa was identified as one master user, and this enabled the online store to show personalized information to her. This is the real power of cross channel identity – create accurate customer segments that can allow you to create personalized and seamless experiences across channels.

Real-time audience infrastructure

The more up-to-date your audience segments are, the more valuable they become. Not long ago, segments were mainly updated periodically (through bulk processing or manual .csv upload). However, this approach only works fine if you are analyzing the historical data. If you are receiving real-time data, it increases the risk of delivering out-of-date experiences to your customers.

For example, suppose your marketing campaign’s target audience is only updated once or twice per day. In that case, you may be sending promotional emails to customers that have already converted within the refresh period. Besides, if a customer opts-out of marketing communication, you must make sure that you comply with GDPR or CCPA regulations and stop sending promotional messages immediately after the user expresses their consent.

To adapt to dynamic compliance requirements and deliver messaging relevant to your users in real-time, you need to construct your audience segmentation infrastructure with tools that offer API-based audience updates. Once you implement these foundations,  you can use segmentation to carry out many advanced use cases, such as transactional messaging, cross-channel campaigns, or location-based targeting.

Example:

The user (let’s continue Vanessa’s story) spotted a lovely dress in an online shop, but she was still unsure about the dress size, so she decided to drive to the offline store (same brand).

Vanessa arrived at the store, tried on the dress. It fitted her nicely, but she was still hesitant. At that moment, Vanessa received a push notification to her phone to try a necklace, which would complete her look.

She tried it on and was amazed at how great it looks on her. Instead of buying one item, Vanessa decides to purchase two things – a dress and a necklace.

What happened here?

Vanessa received this timely push notification because she was assigned to the segment „Webroomer: dresses,” which automatically triggered a messaging destination.

Here is how the segment got activated.

Before going to the offline store, Vanessa browsed the online shop and clicked on the “Dresses” category. By doing that, she made a “Browsed “Dress items” event. She also clicked the “Add to cart” button – this was another event in the segment. What is more, Vanessa did not make any purchases, both offline and online – two more events. The last part of the segment is “Entry to Store.” After Vanessa entered the store, she triggered a beacon, and her entry data was sent to the server. The whole segment got activated, and Vanessa received a recommendation to try on the necklace that eventually led to an upsell.

Cross-channel targeting

As customers engage across an increasing number of channels and devices, it’s essential to make sure that your messaging is in-sync across platforms. Cross-channel campaigns leverage data and insights gained in one channel to inform customer experiences in other channels. For example, if a customer favorites a specific product in your mobile app, you can build a cross-channel campaign to deliver that customer contextual offers on Facebook and/or through email.

To build a cross-channel campaign, it’s essential to ensure that you’re collecting data from across sources into a single system and that all customer activity is tied to a unique customer profile. Customer Data Platforms are particularly valuable when building cross-channel campaigns, as they automate data collection from across channels, ensure that that data is consistent according to your data schema, and make it easy for business users to access that data and use it to build audiences without developer support.

Example:

User (let’s name him John) arrived at the hotel and approached the reception. After he checked in, the receptionist offered him a $30 SPA voucher for any selected services. John was delighted and opted for a Thai massage and paid $25 extra to get the service.

What happened here?

Activation of a few different cross-channel events led to the personalized SPA offer.

During the online booking, John checked the box “Add a full day Hotel SPA access.” Later he unchecked the box, but his action was tracked. He had also booked a room (Hotel Booking Web) but had not booked a SPA through any channels. After John entered the hotel, he triggered a beacon, and his entry data was sent to the server. The entire segment was activated. The hotel receptionist received a segment-linked CRM notification to offer John a $30 SPA voucher.

This is how user interaction data made a cross-channel transition from web to proximity and finally to the hotel CRM database. This enabled to provide a delightful offer for a customer who was not expecting it.

Flexible segment grouping

In essence, segmentation is a combination of user-related conditions. It can be a set of events, user attributes, location restrictions, and many more properties to define an audience segment. However, how different conditions are combined is an equally critical part of the modern segmentation engine.

The most basic segment creation logic is simply summing the different conditions. For example:

A (users who clicked on the “Add to cart” button)

B (users who visited checkout page)

C (users who did not finish the transaction within the last hour)

Here is the simple formula:  A + B + C; in other words, all the conditions are equal and mutually required for the user to be assigned to this segment.

However, there is a more advanced way to group the segments. You can apply different operators and even brackets to create highly targeted and flexible segments. Let’s expand on the previous example:

  • A (users who clicked on the “Add to cart” button)
  • B (users who visited checkout page)
  • C (users who did not finish the transaction within the last hour)
  • D (users who visited a category page)
  • E (users who added items to wishlist)
  • F (users who placed the order but did not finish the payment process)

The formula, in this case, can be:

(A + B + C) or (D + E) or F

As we see, segment conditions got expanded, but we are still targeting those users who did not finish their purchase. So both A+B+C or D+E or F groups can be sufficient alone to meet the segment’s criteria and deliver highly targeted cart abandonment emails.

In a modern audience segmentation engine, flexible condition grouping is a must. With Intempt Platform, you can apply these principles to create advanced segmentation logic, filter your audience the way you need, and target only those customers who can be activated by your message.

Build modern segmentation with Intempt

Intempt is a pipelines, audiences, and metrics platform that, at its core, has an advanced segmentation engine. With Intempt, you have all your modern segmentation needs covered:

  • Advanced data collection infrastructure
  • Real-time segment processing and activation through multiple destinations
  • Cross channel targeting
  • Flexible segment grouping
  • Expand possibilities by adding Pipelines and Metrics products to implement even more advanced segmentation use cases

Try building segments for yourself with Intempt’s free trial. Go to app.intempt.com to activate your free platform onboarding. Feel free to test the waters with the comprehensive connector ecosystem that Intempt offers, which extends well beyond the segmentation engine.

3 User Personalization Ideas for Consumer Services@2x

3 User Personalization Ideas for Consumer Services

Over the last years, online based consumer services have become more and more popular: Ticket booking, paid webinars or print-on-demand services are clearly on the rise.

Despite the variety of business models that fall under the label “Consumer Services”, there are some challenges most of these businesses face:

  • Identifying and understanding the individual user intent
  • Addressing to existing customers and prospects in real-time
  • Increasing the user LTV (Lifetime Value)
  • Reducing the CAC (Customer Acquisition Cost)

In this blog post, we are going to take a look at three use cases revealing how user segmentation and personalization help these businesses to drive conversions, sign-ups and other important metrics.

User Personalization Use Case: California Academy of Sciences

The California Academy of Sciences is a public natural history museum in San Francisco housing over 26 million specimens.

The Museum’s Services

The museum’s website reflects the variety of services this institution has to offer. A user may:

  • Book exhibition tickets
  • Book nightlife event tickets
  • Become a member
  • Engage with educational content
  • And much more

Besides its exhibitions, the museum is widely known for its nightlife event making science become accessible for young and old alike in an entertaining manner.

Increase Specific Ticket Bookings Via On-Site Messaging

The goal of this use case is to

  • Drive ticket bookings for the nightlife event
  • Based on individual user actions
  • Without affecting other funnels (such as regular exhibition ticket bookings)

How to Deliver the Right Offer to the Right User?

Several funnels make it difficult to guide the right users to the right spot – based on their intent. Also, the clock is always ticking: If new users get bored or confused, they might drop off after a short while.

Create a User Personalization Campaign to Boost Ticket Bookings

Boosting a single funnel within a multi funnel setup is a perfect use case for user personalization because you may easily segment the right users you would need to approach to.

How do we get there?

Collect and Store Data

Before creating the campaign, we need to install the Intempt tracker on the museum’s website. The tracker is a code snippet similar to Google Analytics collecting and storing each user’s behavior (page views, clicks, form submits and other actions) so we can roll out personalized messages.

In this case, we may pay close attention to data highlighting the user’s intent such a page’s theme.

Create a Behavioral Segment

We then specify a target segment blending past (factual) behavior and future (predicted) behavior. In our case we may target users who

  • Reviewed more than 3 marine-themed pages
  • Have not purchased a nightlife event ticket yet
  • Are predicted to drop off without purchasing a nightlife event ticket

Please note: Segmenting users who are interested in marine themes will make it easier to personalize the messaging later.

This is how our user segment would look like:

A user personalization campaign may be easily created by adding

  • A user segment
  • A campaign goal (tracking the campaign’s success: “Purchased A Nightlife Ticket”)
  • One or more message copies
  • Additional delivery preferences (5 seconds delay, messaging only on weekdays)

The final message could look like this:

Why a Message at This Time?

Users at this time

  • Reviewed more than 3 marine-themed pages
  • Have not purchased a nightlife event ticket yet
  • Are predicted to drop off without purchasing a nightlife event ticket

We can declutter the website from unnecessary messages by delivering only highly personalized messages addressing the user’s specific interest.

User Personalization Use Case: Roger CPA Review

Roger CPA Review is on a mission ensuring the study process for aspiring CPAs is effective, efficient and enjoyable. This educational hub attracts nearly 170.000 users each month.

Roger CPA Review’s Services

The company offers a wide range of educational products such as

  • Select Course Packages
  • Elite Course Packages
  • Premier Course Packages
  • Free Trial Packages

Considering the packages’ high prices (up to $3.000), a purchase becomes a big commitment for first-time users.

Tempt First-Time Users to Sign Up Via On-Site Messaging

The goal of this use case is to lower the bar by tempting

  • First-time users to
  • Sign-up for a free trial package
  • Based on their behavior
  • Without affecting other users

We utilize social power for this campaign: Presenting specific success stories of exams our first-time users were inquiring before helps them to get engaged and sign up.

How to Deliver Engaging Messages to the Right Users?

Several packages and pricing options make it difficult to individually guide users to the best offer. It is hard to convince first-time users to sign up even for a free trial if the product range is high-priced. Also, the clock is always ticking: If new users get bored or confused, they might drop off after a short while.

Create a User Personalization Campaign to Boost Sign-Ups

Boosting sign-ups via tailored and personalized messaging is a perfect use case for user personalization because you may easily segment the right users you would need to approach to.

How do we get there?

Collect and Store Data

Before creating the campaign, we need to install the Intempt tracker on the website. The tracker is a code snippet similar to Google Analytics collecting and storing each user’s behavior (page views, clicks, form submits and other actions) so we can roll out personalized messages.

In this case, we may pay close attention to data highlighting the user’s intent such as the course bundles and the CPA exam structure.

Create a Behavioral Segment

We then specify a target segment blending past (factual) behavior and future (predicted) behavior. In our case we may target users who

  • Visited the website for the first time
  • Reviewed the price of course bundles
  • Checked out the AUD or BEC section
  • Are predicted to drop off without signing up

Please note: Segmenting users who are interested in AUD or BEC will make it easier to personalize the messaging later.

This is how our user segment would look like:

Deliver On-Site Behavioral Messages

A user personalization campaign may be easily created by adding

  • A user segment
  • A campaign goal (tracking the campaign’s success: “Created Free Sign Up”)
  • One or more message copies
  • Additional delivery preferences (display messages only on “Exam Structure” pages)

The final message could look like this:

Why a Message at This Time?

Users at this time

  • Visited the website for the first time
  • Reviewed the price of course bundles
  • Checked out the AUD or BEC section
  • Are predicted to drop off without signing up

Once the visitor is on Justin’s story page, we will send a notification to engage with a free trial by requesting a small commitment (submitting their email address).

User Personalization Use Case: Blurb.com

Blurb is a self-publishing platform allowing creators to publish their own books online.

Blurb’s Services

Authors can promote and sell their book directly

  • On their website
  • On their blog
  • In their social network

using Blurb’s “Direct Sell” option after they have created an account and uploaded their content

Blurb’s homepage is beautifully designed, but users are exposed to two separate discounts (20% off by default and 35% off until November) before they even start exploring the website.

Reduce CAC (Customer Acquisition Cost) Via On-Site Messaging

The goal of this use case is to

  • Display discount messages only to specific users
  • Based on individual user actions
  • Without affecting regular users
  • While Reducing Blurb’s CAC (Customer Acquisition Cost)

How to Offer Discounts Only to Those Who Need Them?

A broad user spectrum makes it difficult to identify and approach users who need a push. Plus: The clock is always ticking. If new users get bored or confused, they might drop off after a short while.

Create a User Personalization Campaign to Boost Sign-Ups

Boosting sign-ups via tailored and personalized discounting is a perfect use case for user personalization because you may easily segment only the right users you would need to nurture.

How do we get there?

Collect and Store Data

Before creating the campaign, we need to install the Intempt tracker on the website. The tracker is a code snippet similar to Google Analytics collecting and storing each user’s behavior (page views, clicks, form submits and other actions) so we can roll out personalized messages.

In this case, we may pay close attention to data related to the Photobook section because the 35% discount targets photobooks and we want to ensure high conversion rates there.

Identify Drop-Off Points With User Analytics

After users chose the photobook service, they need to sign up or login to proceed uploading their content:

This necessary step will most likely lead to a lot of user drop offs.

To back our hypothesis with data, we could use Intempt’s segment viewer to compare two segments:

  • Returning users who visited the sign-up page
  • Returning users who visited the following page

A user segment comparison could look like this:

Based on the user drain we would notice we could confirm the form is a drop off point requiring to take action.

User Analytics vs Pageview Analytics?

Please note user analytics is different from pageview analytics (Google Analytics and alike).
You may create and compare complex segments which include all kinds of user actions such as visit counts, clicks or form submits and you may also include data from third party sources such as:

  • CMS
  • CRM
  • Search engines
  • Email
  • Social
  • Retargeting

Let us once again take a look at the previously created segments:

  • Returning users (visit count)
  • Who visited the sign-up page (pageview)
  • Who visited the following page (pageview)

Not only pageviews are taken into account here for segmentation but also the visit count, allowing deeper insights into any website’s user flows.

Create a Behavioral Segment

Next, we specify a target segment blending past (factual) behavior and future (predicted) behavior. In our case we may target users Who

  • Are returning visitors
  • Did not create an account yet
  • Reached the form step
  • Are predicted to drop off without creating an account

This is how our user segment would look like:

Deliver On-Site Behavioral Messages

A user personalization campaign may be easily created by adding

  • A user segment
  • A campaign goal (tracking the campaign’s success: “Signed Up”)
  • One or more message copies
  • Additional delivery preferences (5 seconds messaging delay)

The final message could look like this:

Why a Message at This Time?

Users at this time

  • Are returning visitors
  • Did not create an account yet
  • Reached the form step
  • Are predicted to drop off without creating an account

In this scenario, we only offered a discount to the right users lowering the CAC while increasing conversions.

Boost Your Consumer Services Business Via User Personalization

Target visitors based on their behaviour across channels while you still retain full control over your campaign.

If you are struggling with long customer journeys and fragmented visitor segments, this approach can be your tool to drive conversions.

Just be personal, in a smart way.

Want to Get Your Business Leveraged?

24 eCommerce experts weigh in on how personalization@2x

24 eCommerce experts weigh in on how personalization

I notice that the way customers are making decisions has changed – for good. They are expecting a brand to establish a relationship before committing to a purchase or a sign up. If a marketer fails to provide that relationship, they leave.

With this change, marketing has also shifted towards personalized experiences. The majority of marketers indentify personalization as a “highly” or “quite” valuable method for improving their conversion rates. 3 out of 4 customers agree on this.

So in what way does Personalization improve Conversion Rates? Why should an ROI-focused marketer consider it?

I asked 24 eCommerce experts: “How can Personalization enhance conversion optimization?”

Here’s what they had to say:

#1. Sam Hurley

IgniteVisibility.com

Personalization is one of the most important aspects of conversion rate optimization. Personalizing ads based on dynamic keyword insertion, personalizing landing pages based on the ads and crafting users experience in a funnel that clearly aligns with the buyer makes all the difference. Even something as simple as location detection can have a big impact.

For example, imagine searching for hotels and landing on a general page. Alternately, you land on a page that has hotels in the area you are looking for. That can have a massive impact on lowering your bounce rate and conversion rate. Or in another scenario, imagine you serve an ad on Facebook. Then, when the user comes to your site you create an entire experience for them based on the Facebook audience you were targeting. Say your audience is women, 35 to 55, who are interested in Sephora.

A personalized experience based on what this demographic enjoys is going to have a big impact on conversions and would be very different than an experience for men, 21 to 35, who like trucks and racing. So in the modern day, your ads and web environment need to be personalized to your audience for the best results.

#2. Sam Hurley

OPTIM-EYEZ

Customer expectations are more demanding than ever before. “81% of consumers want brands to get to know them and understand when to approach them (and when not to).” (Source: Accenture) The more you are able to learn about all your customers on a 1:1 level, the better their experience. Consequently, you will be rewarded with higher conversion rates… It’s a no-brainer. People want to be made to feel special — not like numbers on a spreadsheet. So, just how is this possible in the case of thousands of customers? Artificial Intelligence and machine learning tech allows granular, on-the-fly, automated personalization without the huge additional resource that would otherwise be required manually (if even possible, at volume). The flood of new, AI-based solutions are only testament to demand in our era of everything ‘personal’. Here are just a few examples of personalization techniques to keep visitors engaged and encourage conversion:
  • Dynamically alter your website interface to each visitor’s buyer profile
  • Display the best tailored content to the right visitor at the optimal time
  • Deliver predictive product recommendations, incentives and lead magnets
  • Optimize pricing (in real-time) to suit every customer and their individual preferences

#3. Shep Hyken

Hyken.com

Personalization is simply knowing your customer. Any time you can “speak” directly to your customers, whether it’s through a digital/online channel or in person, recommending what you know they want, or might want based on past-purchase history, you not only have a higher success with conversion, you’re offering them a better customer experience.

#4. Michael Kawula

SocialQuant.net

Though it’s a bit cliché, I’ll say it.

People do business with people they know, like and trust.

One of the best ways to gain that trust is by making the experience as personalized as possible.

Your copy should ALWAYS speak to the customer individually not to the masses.

But it goes much deeper than the copy and with all the tools we have at our fingertips today, we need to assure we’re taking advantage of them.

I’m a huge fan of “live” one-on-one discussions whenever possible. If you have the support, have your phone number easily seen, it gives the warm feeling that support is available. The majority won’t call (trust me we shipped tens of thousands of packages monthly and received little calls).

Install live chat on your website, but please make it non-intrusive.

Nothing is worse than visiting a site and having it pop up immediately. Today software has gotten much smarter and can be much more customized.

Not only will you help with the current visitors concerns while on your site, but more importantly by storing that data, you can make changes to your website by adding videos or copy to the pages that get repetitive questions.

Chatbots are also very interesting and you can automate a personalized assistant to help create that warm feeling.

Lastly, talk to your customers frequently and ask for feedback. The more you can personalize the entire experience from that initial visit to a customer who repeatedly visits, the faster your company will grow.

#5. Nichole Elizabeth DeMere

zest.is

Personalization can mean a lot of things when it comes to conversion optimization. I think it too often means targeting and retargeting ads more precisely, but it’s so much more than that. For me, personalization for conversion has to begin with accurately identifying your ideal clients, and doing thorough research with customer interviews, surveys, conversation logs and recordings so you understand exactly what they want, need and are missing. And then you create your entire brand experience around what will move your ideal client towards their ideal outcome.

But that’s very clinical.

That’s just getting them from point A to point Z, which you need to do, but it’s not all you need to do. Personalization is also about making the experience of working with you personal – building a relationship, a friendship even. The businesses I keep coming back to are those that share my values, interests, passions, sense of humor. That’s something I see brick-and-mortar businesses nailing in ways e-commerce is just beginning to understand.

You might even call it the “Cheers” effect – where everybody knows your name, and when you come in the door, you feel like family. That’s what drives the kind of conversions that keep coming, spreading, and growing businesses.

#6. Jeff Gibbard

jeffgibbard.com

Personalization in marketing is one more way to remove friction for customers. The more personalized suggestions can be to the user (without being creepy) the less work there is for the buyer to do finding what they’re looking for. Further, the impact on conversions should be substantial as relevancy will always convert higher than irrelevancy. The goal as a marketer is to get a potential audience to say “yes,” what better way than to offer products, services and solutions tailored to their specific wants and needs.

#7. Joanna Wiebe

copyhackers.com

One of the first rules of conversion copywriting is this: help your prospect see him/herself on the page. Until now, digital marketers have tried a combination of custom pages / emails and A/B testing to reach that goal – for example, make 4 landing pages for 4 market segments, and test 2 versions of each of those pages. So create 8 distinct pages and pray that 4 of them are generic enough to win more clicks. That was super-common in CRO. But it’s a hack-job for so many reasons, including this: CROs focused only on A/B testing were still optimizing for a generic market segment. We were asking prospects to see themselves in a sort of foggy mirror – “just squint and maybe you’ll see how X product is perfect for you?” With personalization, we get much closer to helping the prospect see himself or herself on the page. Not a generic segment that might look something like they do. If a person arrives on your site after clicking a FB ad targeted at 18 to 25 year old women in the US, we can now personalize the page to serve up the content that’s best for her. That means serving testimonials from American women in the same age group. And serving a headline that speaks to the specific pain she feels – not the general pain X market segment has indicated they may feel. The result is perhaps not quite a perfect reflection of the prospect on the page… but something much closer. Combine that with A/B or multivariate testing as you personalize, and suddenly conversion rate optimization is no longer just trying to win the lottery on demand.

#8. Antonio Grasso

AGrassoBlog

The Customer likes to be part of a brand and like to receive targeted messages instead of generic offers. The Personalization and the Data Analytics backed Hyper-Personalization can boost the Conversion rate because we reach our Customer with an Individual message and he “feel our effort” to reach him without harassing him. A better Customer Experience mean a loyal client.

#9. Nicholas Scalice

earnworthy.com

People buy from brands that they know, like and trust. Part of building that trust is in feeling connected to the brand, and the people behind it. That’s where personalization comes in. By personalizing an online experience, you’re talking to a specific visitor, rather than just talking to a broad audience. This will help the visitor connect on a deeper level and truly understand the value in the product or service that you’re offering. Ultimately, that will make it much easier to win the conversion.

#10. Brian Massey

conversionsciences.com

A better question to ask is, “How does conversion optimization enhance personalization?” Businesses embark on personalization strategies with the assumption that, if we know where a visitor is in their buying cycle, we can provide some information that will move them to the next step. But, the only way we can find out what visitors need to move to the next step is by experimenting. This is what Conversion Optimization offers.

Let’s look at a visitor that has visited the website six times and looked at the same product over a two-week timeframe. We might assume that they are really interested in that product and that the price is too high. A nice discount might push them over the edge to buying. We might also assume that they are waiting to see if the color they like becomes available. In this case we might ask them for their contact information so we can inform them when new products are released.

Which one is it? How do we personalize that page to give the visitor what they need to buy?

A better use of personalization is to serve different experiences that are revealed in tests. Let’s pretend we test adding a video to a webpage. When our test completes, we find that the version of the page without the video generated more revenue than the one with video. However, our analysis reveals that visitors who entered the site on that page converted higher when the video was present than those who visited from another page on the site. Rather than choosing one option for everyone, we personalize the experience, offering video to those who are just entering the site and eliminating it for everyone else.

We never would have known to use that signal and that solution without the testing tools used for conversion optimization.

Poorly informed personalization — I call it purse-onalization — makes conversion optimization harder because it breaks up the website into more and more versions. This makes AB testing more difficult and causes tests to take longer.

Brian Massey is the founder of Conversion Sciences which offers conversion optimization services to businesses serious about the web.

#11. Jamie Turner

60secondmarketer.com

According to one recent study, 77% of those surveyed believe real-time personalization is crucial, but 60% said they struggle to personalize content in real time. That mismatch is actually an opportunity for those businesses willing to take it on. How? If 60% of your competitors are NOT doing something and studies show that personalization can increase conversion rates, then that puts you at a decided advantage … if you take the time to execute it.

The bottom line – we know personalization is important, and we know that most businesses struggle to make it happen. So take the time to personalize your content and you’ll realize a better conversion rate and a better ROI. It’s a win/win opportunity.

#12. Richard Lazazzera

bootstrappingecommerce.com

Personalization makes customers feel special, thought of and considered. It exceeds consumer’s expectations and makes a product stand out from others that are similar. It enhances conversion optimization because it makes consumers more likely to purchase a product they can personalize rather than a generic product. It makes the product more special and unique to them.

#13. Frederick Vallaeys

optmyzr.com

Between AdWords and Facebook Ads, there are constantly new ways to get more granular with targeting. Advertisers can get ever closer to targeting just their highest value prospects and that level of specificity enables more personal ads and offers. Combine better targeting with more personal ads and you’re almost certain to boost your conversion rates. But remember that when you only target bottom of the funnel traffic, you’ll be competing with everyone else for that super valuable (and expensive) traffic so don’t forget to build the top of your funnel so that you can slowly get to know your prospects before you get personal with them.

#14. Matt Janaway

mattjanaway.co.uk

Conversion rate optimisation (CRO) is a process of continual evolution overtime – CRO is never finished! Websites evolve, customer behaviour changes and competitors sometimes get their act together. In the digital age of personalisation and connecting with customers, business owners should be adopting a personalised strategy approach to gain a competitive advantage over their close competition.

Econsultancy suggest a personalised marketing strategy creates happy customers, who wish to purchase more and perhaps most importantly, then become an advocate for your brand or business.

And according to Smart Insights, personalising a customer’s interaction is still not being used across the whole ‘digital’ journey. Email marketing has by far seen the largest adoption as a key personalisation techniques with 72% of business owners now using personalised emails as a key marketing strategy. However, there is still room for businesses to improve the personalised experienced customers receive on their websites which can help to increase conversions and drive more leads.

There are many ways businesses can integrate a more personalised website user experience – here are a few of my top favourites:

  • Location personalisation – with most mobile devices now highlighting the location of a user at all times, location personalisation can be used throughout websites to promote location based offers such as restaurant deals / opening times or targeted user information for example on tourist information websites or contact pages.
  • Demographic personalisation – although this type of personalisation has received mixed results due to the data sometimes being inaccurate, business owners should be using demographic personalisation at a very basic level. For example, when a user logs into their website account i.e Mary, 56 yrs old from London who uses a fashion website, Mary should be shown clothing or products aimed towards her demographic that are based around her buying habits.
  • Stage of customer journey – with every website customer’s journey being different, deploying tactics to personalise each different stage of the buying process is a very undervalued conversion strategy. Abandon checkout software offers the chance for a targeted and personalised email to be sent out to a prospect customer. In addition, simple personalised tactics such as highlighting related products when a customer adds what they want to basket have also proven to increase customer basket values.

#15. Dennis Seymour

Seriousmd.com

I cannot do a “standard” average across industries but for my own results, I can definitely say that personalization helps enhance conversions.

I’ve done this on multiple niches, from my own coaching and course offers on LeapFroggr.com to converting users based on specialty on my startup SeriousMD.com

As I am targeting a more specialized niche within a niche, the better the conversion, the better it is for my ROI as it’s not cheap to find leads. Personalizing the landing page, to the messages they read (I sometimes use chatbots) to making the emails they get once they optin, I could say that I had an increase of at least 50% from normal conversions of just a landing page and just sending traffic to them.

It might not look “big” but that equates to a rather nice LTV for each conversion.

#16. Shanelle Mullin

Shopify.com

Personalization is the real-time individualization of a site to suit each visitor’s unique needs and guide them through a custom funnel. The problem? Most marketers have content personalization, not user experience personalization. Personalization has become synonymous with “the right message at the right time to the right visitor”. While that is certainly part of it, copy is not the be-all-end-all of user experience. Push yourself to dig deeper into the user experience and go beyond the basics.

#17. Kath Pay

holisticemailmarketing.com

Personalisation – true personalisation – is a game changer for email marketing. For years we’ve been saying that to get the best results we need to be relevant, and sending personalised campaigns is one way of achieving this. Moreover, the consumer now not only wants it but expects it.

One of the issues is though that achieving personalisation that will increase conversions is an art. We tend to rely only on the obvious types of personalisation – those that the consumer can overtly see is personalised to them. There is a fine line though between what the consumer sees as a nice overt personalisation campaign and stalking them. It’s crucial we get this right.

To assist is in achieving this fine balance, we can use covert personalisation, which is providing offers, content etc. personalised to the individual or failing that, the segment or persona, in a more subtle fashion, so they just see that what you’re sending to them is relevant and valuable to them, without the association of ‘stalking them’ and potentially scaring them.

Personalisation all too often for an email marketing begins with a tactic or technology – but in my experience where it should start is with our mindset and a solid strategy. The tactics and technology help us to bring the personalisation strategy to life. I recommend that the first step is to change our mindset and start seeing all of our marketing emails as helpful. I call this helpful marketing. If we do this, we can’t help but begin with the consumer and this is where we should start. Read more here.

#18. Jeff Sauer

Analyticscourse.net

Personalization can improve your conversion rate in many ways, with my favorite way of improving conversion rates being segmentation of your audience.

We live in a world of analytics tools and marketing platforms telling us about the “average” user. Average open rates, click through rates, conversion rates, etc. But how many of your prospective customers want to be treated as average? Simply put, average is a myth. Average is the opposite of personalized. It’s a one-size-fits all approach in a world that demands a tailored fit.

It’s up to us to fight back against average, and deliver the experience that our customers expect. How do we do that? Through personalization. By creating content aimed at our customer segments, and giving them the experience they deserve. Instead of being average and checking boxes on marketing tactics, think about it from the perspective of your prospective customer. Think in terms of their experience.

Generic experiences are uninspired, average, and wasteful. We’ve come too far to stop at average. Segment your customers using tools like Google Analytics, customer surveys and marketing automation. Deliver a personalized experience. Get better results. Look cool in front of your bosses, who in turn look cool in front of your bosses bosses.

Stand out from the crowd of average. You can do it. Segment. Be personal. Be awesome.

#19. Sean Si

seo-hacker.com

Personalization, in any form, gives your web pages a unique look and feel to it. It adds a good amount of authenticity, which makes it more presentable and genuine. Brand trust is something that takes a lot of effort to build up and grow, and personalization will help your business stand out from the rest.

Users have a preference towards exclusivity and unique ideas. Using these elements in your business will help increase your conversion rate. Personalization makes your audience feel that they are being prioritized by your business, which helps you sell your product or service that much easier. With today’s tools and software, personalization has become much easier to pull off. Boosting any site’s conversion rate is always a competitive challenge, but simple things like personalization would help your business in the long run.

#20. Jason Quey

TheStorytellerMarketer.com

A business is all about serving your customers problems. Naturally, people want to feel like you really understand their problems.

If you interview them in the right way, customers will tell you everything you need to know to be a better marketer.

I’ve had them tell me everything from how to price a product, who the key influencers are to connect with, and where I should strategically be guest posting.

#21. Matt Thorpe

Webshopmechanic.com

Personalisation is something that many sites don’t even attempt to understand and they should. It could transform their business. Seriously, would you buy a sports car when you need a 4×4? Would you buy chicken when you’re a vegetarian? Would you buy red when you’re favourite colour is blue?

You need to work on collecting data about your customers and using it to get under their skin to find out what makes them tick. At a basic level, just personalise your emails with their name and deliver relevant content. Work towards a personalised experience on your website where you make them feel special, like the store has been made for them. The more granular your targeting, the more relevant the experience, the happier the customer and the more revenue you will drive.

I quote a famous phrase – ‘The riches are in the niches’.

#22. Raphael Paulin-Daigle

Splitbase.com

To me, personalization is a part of conversion optimization.

At the end of the day, the reason why companies are looking at personalization is that they know that if they do it right, it’ll likely increase their sales. Just like anything else in conversion optimization. If there wouldn’t be any business case for it, no one would be doing it!

The key to being successful with personalization is first to understand what problem you’re trying to solve. Don’t do personalization for the sake of it, or just because it’s trendy, it’s a strategy that should be used to solve a specific hypothesis.

For example, let’s say you discover through Conversion Research that people don’t buy your products because they likely don’t relate to how it’s being sold to them. In this case, personalization could be used (and different types of personalization should be tested) to solve this problem and display images or product descriptions that they might relate to better.

I see personalization as a strategy that’s part of a pyramid of conversion optimization:

  1. At the bottom, start by understanding your customers’ behaviors, needs and wants, and know how to interpret your data
  2. Then you should be able to craft hypotheses and master A/B testing
  3. And finally, once you’ve mastered the basics, personalization is the next step to enhance your CRO toolset of strategies.

If you don’t understand how to use data to come up with hypotheses, you won’t know what to personalize to drive results. And if you can’t A/B test, you will likely fail at personalization too.

See personalization as an advanced strategy to test and solve some conversion problems.

#23. Keith Hagen

GoInflow.com

Before you personalize, you need meaningful information about the user. The best way to start to “profile” them with simple self-identification techniques (If someone clicks on “baby strollers” than guess what, they may be expecting…now you know). Start small with giving users more of what they appear to be interested in.

#24. Joseba Umbelina

LUXHABITAT

Personalization is a clear win in terms of optimizing the conversion rate. I believe in offering users products and services based on their past behaviour and trying to predict what they might like, based on similar user’s data. A smart personalization surely enhances the user experience, increases user’s satisfaction and in the end, leads to more conversions, improving the ROI of our marketing campaigns.

Web Analytics vs User Analytics- From Page Views to User Journeys@2x

Web Analytics vs User Analytics: From Page Views to User Journeys

As marketers, we have data at our fingertips. The industry has given us a broad set of data warehousing and analytics tools. In many cases, these tools work well at providing us aggregated data about which visitor acquisition channels are working.

We see reports on paid, organic and referral channels as well as our cost and conversions.

But what about the user data? It’s no secret that harnessing large volumes of data quickly and accurately during these phases:

  • Pre-purchase (attract users)
  • Conversion path (engage users)
  • Retention (grow user base)

will positively affect your revenue stream.

Web Analytics Reconsidered?

The problem is that web analytics tools such as Google Analytics don’t focus on user journeys. They focus on sessions while prioritizing them over a detailed user journey.

Yes, there are user flows. But still web analytics largely is based on page views, sessions and fixed properties when creating traffic segments:

…whereas a user focussed segment in Intempt could look like this:

Web Analytics’ Focus on Sessions Affect KPI Calculations and Insights

This focus on sessions also leads to other problems. Not many marketers are aware of the fact Google Analytics and other web analytics tools use sessions to calculate KPIs that matter to them, e.g. the goal conversion rate:

Google Analytics calculates the goal conversion rate as follows:

Goal conversion rate = Number of unique goal achievements per session / Number of sessions

…whereas most likely you would rather like to investigate this conversion rate:

Goal conversion rate = Number of unique goal achievements per user / Number of users

A lack of visibility into the user’s footprints leads fuzzy understanding and vague idea what is going on your website.

In this post, I will explain how marketers may use user analytics to tackle the complexity of a variety of user interactions to get better insights and to make more successful decisions.

The User Journey – More Than Just Pageviews

Gaining deeper insights into your user behavior helps you to eliminate drop-offs and drive conversions.

Web analytics such as Google Analytics may give you raw ideas, but you would need to look at a granular level to really draw decisions that factually matter.

Why? Here are some reasons.

Most Users Convert After a Few Sessions

Depending on the nature of your business, the average number of sessions leading to a conversion naturally varies. Most marketers would agree though they rarely see conversions happening on the first visit.

Harvesting data from all user sessions combined instead of a single session allows marketers to see user journeys holistically.

Users Are Not Always Identified Properly

Users may sign in on a website, come back later as a logged-out user and complete a purchase. Users may also visit more than one domain within the same brand experience. Full user journey insights require to to collect data across multiple domains.

A 360 degree tracking retrieving data from all angles is required to provide detailed user behavior based insights:

Users Create Complex Journeys

Users often follow their own complex way towards a conversion point. Web analytics tools such as Google Analytics may not be able to reflect this complexity in their funnel visualizations:

Web Analytics’ Shift Towards User Analytics?

Web analytics explains the game, user analytics explains the players. Traditional web analytics tools have attempted to respond to this challenge of understanding users.

But all behavior must be tracked and associated with a single identified user. Web analytics systems were not designed to do so without significant aftermarket customization and they also ignore most user data generated aside from the website.

As mentioned above, you may use user flows (in Google Analytics) to get closer to a user focussed perspective. You may also sent custom events to the Google server if a user performs an action that is different from a page view (e.g. a button click).

While this all is possible, it needs to be implemented properly and constantly monitored making it become a hassle.

Combine Web Analytics With User Analytics For Best Results

Pairing your web analytics with user analytics allows you to share different perspective for best possible insights. But what features does an user analytics system need to have?

Let us take a look at some requirements:

Full Resolution – Broad Capture

All user data needs to be captured at scale and granularly (broad and deep) without sampling.

Web analytics’ segments are based on simple attributes like location, browser or session duration. A behavioral 360 user segmentation instead may use data from all angles to deliver best possible insights:

SITE BEHAVIOR / PURCHASE VARIABLES ENVIRONMENT VARIABLES REFERRER VARIABLES TEMPORAL VARIABLES CRM VARIABLES
Customer/prospect IP address Referring Domain Time of day LTV data
New/ Return visitor Country of origin Campaign ID Day of the week Purchase History
Previous visit patterns Time zone Affiliate Recency AOV data
Previous product interests Operating system PPC Frequency
Searches Browser type Organic search
Previous online purchases Screen resolution
Previous campaign exposure Device
Revenue

Autotrack – Deep Capture

Autotrack is opposite to manual tracking you may be dealing with currently. Any user analytics platform should track as much user data as possible (such as page views, clicks, form fills, etc.) without the need of manually setting up these events.

Retroactive – Looking Back in Time

Unlike Google Analytics and other web analytics platforms, user analytics should allow to use all previously collected data to create funnels post hoc at any time. Tracking doesn’t start when you define the funnel – it starts when the tracker is installed.

User Journey Viewer – Greeting John Doe

Why should we look at one user journey instead of a segment? Picking and spot checking certain users may inspire you to formulate powerful hypotheses that lead to actions.

Try Intempt’s User Analytics

Intempt employs machine learning to chomp through large swaths of data, analyze your user’s unique fingerprint and build a profile that not only targets users in real time but also predicts their future behavior.

All in one platform – without writing a single line of code.

Want to Gain Better Insights into your Users?

Understand and Increase Your Average Order Value (AOV) and Customer Lifetime Value (LTV)@2x

Understand and Increase Your Average Order Value (AOV) and Customer Lifetime Value (LTV)

Introduction

No doubt, there is some confusion around the topics average order value (AOV) and customer lifetime value (LTV/CLTV/CLV). In this post I would like to shed some light on it and clear the mist.

You will also learn how to look up the average order value (AOV) in Google Analytics and why you might want to change the way you look at these numbers to make them become more relevant for your business.

Second, I would like to show how to improve these metrics using the models we discussed before.

Please note: This post requires some basic understanding of Google Analytics. However, I have linked further information so you can easily look up these topics.

Let us get started!
The Average Order Value (AOV)

No doubt: For most marketers conversion rates are by far the most important KPIs. For ecommerce, average order value (AOV) is critical. Let’s dive into it

What is the Average Order Value?

Let us look at the underlying formula:

Average Order Value = Revenue / Number of transactions

The AOV measures the average total of all orders over a certain period of time. Like any other metric, the AOV only may become a KPI if you find it is meaningful for your business. Based on this number you may evaluate your marketing (ad) budgets and your business health.

Common Mistakes When Interpreting the AOV

However, there are some common mistakes when interpreting the AOV. Let us take a closer look…

AOV Reflects Sales Per Order, Not Sales Per User

A customer may purchase multiple times at your shop, but for an AOV calculation each order is counted separately.

Let me give you an example. Let us assume we were recording these orders for an online shop over a given period of time:

  • User 1: $10 purchase
  • User 2: $100 purchase
  • User 1: $100 purchase
  • User 2: $10 purchase
  • User 3: $10 purchase

The default AOV in this case would be:

$230 (sum of all order amounts = revenue) / 5 (number of orders) = $46.00

The average order value per user however would be:

$230 (sum of all order amounts = revenue) / 3 (number of users) = $76.67

$46.00 vs $76.67 is quiet a difference. It really depends on what you would want to look at (we will get back to this a later when discussing the lifetime value metric below).

AOV Is NOT The Revenue

Don’t take your AOV as the base for your revenue (and gross profit or gross margin) calculation.

Why? Because AOV doesn’t tell you anything about the specifics or nature of your order values (including margins and customer returns).

You may say: “What can I use this metric then for?”. Let us look at another example: An online retailer having

  • 3 t-shirts in stock
  • priced at $10, $20, and $30
  • with an Average Order Value (AOV) of $12

wants to understand his business. The AOV metric leads to the following conclusions:

  • majority of users are likely not purchasing multiple items per order
  • low priced items represent the majority of sales

If the low priced t-shirts have the best margin: fine! But what if the 30$ t-shirt offers the highest margin instead? In this case the retailer might would want to increase the AOV to increase ROI / ROAS.

But it really depends on your current situation. Boosting the AOV doesn’t mean necessarily to increase ROI.

Median vs Arithmetic Mean For AOV Calculation

Please also note the average order value (AOV) does not use the median but the arithmetic mean as a calculation method.

Hence, if your order totals vary a lot due to a broad product range and diverse customer segments, you might want to take this into account to not be mislead when calculating your ad spends and marketing efforts.

Let me give you an example. Let us say we see a row 5 orders over a given period of time:

  • Order 1 Value: $1000
  • Order 2 Value: $10
  • Order 3 Value: $10
  • Order 4 Value: $100
  • Order 5 Value: $5
  • Order 6 Value: $10
  • Order 7 Value: $5

The AOV (using the arithmetic mean) in this case would be:

$1140 / 7 = $162.86

whereas the AOV using the median would be:

{5, 5, 10, 10, 10, 100, 1000} = $10.00,

taking the middle (=median) value from a sorted row of all order values. As you can see, a single high value order can easily distort the AOV result when using the arithmetic mean.

The median value is harder to calculate but may give you better and more reliable insights and understandings in some cases.

For operational calculations (e.g. Google Analytics), usually the arithmetic mean is used, but you may easily calculate the median AOV using Excel or any other calculation tool.

Average Order Value (AOV) in Google Analytics

To see your average order value (AOV) in Google Analytics, you first need to enable Enhanced Ecommerce Data.

Afterwards you may retrieve your website’s AOV by navigating to

  1. Conversions
  2. Ecommerce
  3. Overview

AOV Google Analytics

You may easily create a segment in Google Analytics to recalculate the AOV outlined above based on the sessions segment you would like to look at (I am using US users only in the screenshot above).

You may also check out the average order value (AOV) sorted by traffic source (such as organic, paid or social traffic). Simply navigate to:

  1. Acquisition
  2. All Traffic
  3. Source/Medium

and select “Ecommerce” at the top menu:

Does the AOV Help You To Understand Your Business?

It depends.

Let us combine users and orders from the above example. Over a given period of time we were recording these orders for an online shop:

  • User 1, first order: $10 purchase
  • User 2, first order: $100 purchase
  • User 1, second order: $100 purchase
  • User 2, second order: $10 purchase
  • User 3, first order: $10 purchase

Let us assume the shop sells two products:

  • Product A costs $10
  • Product B costs $100

The AOV in this case is

$230 (sum of all order amounts, revenue) / 5 (number of orders) = $46.00

while the median AOV is

{10, 10, 10, 100, 100} = $10.00

Knowing the pricing structure of the shop, we can already conclude by those KPI’s that

  • more people take low value orders than high value orders (hence the median AOV of $10.00)
  • but still high vs low value orders are somewhat balanced (hence the default AOV of $46.00)

Calculating Ad Budget Via Average Order Value

Let us further assume the shop owner would like to spend 10% of the revenue for advertising:

  • a purchase is worth $46 on average
  • $46 / 10 = $4.60 ad budget per purchase
  • ($46.00 * 5) / 10 = $23.00 total ad budget for time period given

Calculating Ad Budget Via Customer Lifecycle

While this is fine, we also have made another observation: This particular shop has returning customers (user 1 & 2).

Assuming that user 3 soon also may become a returning customer spending additional $100, shouldn’t the shop owner take this into account?

What if the shop owner calculates the ad spend as follows:

  • a user buys for $110 on average
  • $110 / 10 = $11.00 ad budget per user
  • $11 * 3 = $33.00 total ad budget for time period given

In this case the shop owner spends more money on ads while expecting to generate more revenue due to returning customers purchasing more than one time.

The Lifetime Value (LTV/CLTV/CLV)

The calculation above takes the whole user (or customer) lifecycle into account (a row of transactional and other events generated by a user over time) while an AOV centered approach focuses on the purchase event itself.

What is the Lifetime Value?

The investor David Skok thinks of the customer lifetime value (LTV) as something that goes beyond the pure cost of acquiring a customer.

Skok believes that over-investing in the initial transaction momentum is a common mistake made by many online businesses leading to a potential failure because the long lasting relationship between a company and a customer is overseen.

Skok highlights that if the costs of acquiring a customer exceed the LTV, than this business model is likely to fail:

Source

…whereas the opposite proportion signifies a healthy business setup:

Source

How to Calculate the LTV?

Calculating the LTV can be tricky but helps you to leverage your business in a sustainable way. If you look up a formula for LTV, you may stumble upon such impressive creatures:

Source

However, you may not need such a complex formula to get to some results. Let us first calculate the LTV based on the setup we discussed before:

  • User 1, first order: $10 purchase
  • User 2, first order: $100 purchase
  • User 1, second order: $100 purchase
  • User 2, second order: $10 purchase
  • User 3, first order: $10 purchase

Calculating the AOV

The first thing you need to calculate is our good ol’ AOV. In this case we already did this before:

$230 (sum of all order amounts, revenue) / 5 (number of orders) = $46.00

Calculating the Purchase Frequency (PF)

Next, we need to determine the purchase frequency (PF): How many times on average does a customer buy over a given time period?

The formula is simple:

PF = Number of orders / Number of users

…in our case:

5 / 3 = 1.67

Calculating the Average Customer Value (ACV)

Then we need to find out the average customer value:

AOV * PF = ACV

…in our case:

$46.00 * 1.67 = $76.67

Does this number looks familiar? Right so! We discussed it already right at the beginning of this blog post when highlighting that AOV reflects sales per order, not sales per user.

Simply double check this value by multiplying the ACV with the amount of users to see if matches the overall revenue: $76.67 * 3 = $230. Our calculation is correct!

Calculating the LTV

As a last step, you would need to take the user lifetime span and multiply it with the ACV.

According to SweetTooth, the

“(…) average lifetime of a customer is how many years (weeks, or months) your average customer will stay and purchase with you before going dormant and stop. The one true and effective way to understand and see this is by looking at your historical data. To do this, you can view the average time between customer purchases.

Once this time period has been established, and then a customer goes more than two standard deviations past that time period, it can then be safe to assume they are no longer a customer. Therefore, the average time a customer goes before reaching that point, is your store’s average lifespan (t).“

Let us say, the user lifetime span in our case is 3 (weeks). The LTV for this user then would be:

$76.67 * 3 = $230.00

…matching exactly our overall revenue for the given time period (1 week in this case).

Which Way To Go: AOV or LTV?

AOV alone is more often used for one-time purchases (products have to be profitable on the first sale). Your marketing spends simply need to pay off right from the start, as there are no future transactions to be expected. In other words, AOV and LTV are the same value.

An LTV always comes into play if users or customers are likely to create more than one transaction on a website.

Let us look at some examples:

AOV LTV
A shop sells vaults and users will likely create a one-time purchase and never return (they might refer other customers though) A shop sells lipsticks and users will continue making purchases (as long as they like the product and the shop)

Source

Depending on your business, you might prefer one method over the other when calculating your marketing spends and overall performance.

How to Improve Your AOV or LTV?

After we discussed the AOV and LTV KPIs in detail, it is time to look at marketing measures and technologies that help you to improve this metric and leverage your business.

Whether you are new to AOV / LTV calculation or a pro already, I bet you (at least sometimes) struggle with finding the right tools to do the job…this isn’t 2000 where only a few and very basic technologies were available to drive conversions.

In 2018 it is complicated:

Source

There are simply too many tools on the market to gain an overview and marketers can get very busy staying tuned.

Marketing Strategies To Increase AOV and LTV

Many marketers get overwhelmed when asked to pick the right tools for their business. So let us step back for a second and look at common strategies used for increasing AOV and LTV:

  • Cross-selling (bundle related products)
  • Up-selling (market higher-end products)
  • Add free shipping thresholds
  • Add discount thresholds
  • Implement loyalty programs

A great way of increasing AOV and LTV via cross- and up-selling is to use recommendation engines that recommend suitable products to new and existing customers tempting them to add more products to their carts.

Recommendation Engines: Your Best Friend When Boosting AOV & LTV?

Recommendation engines (also known as recommender systems) are on the market for quite some time. Originating from the retail industry delivering personalized product recommendations, the techniques and methods behind this powerful marketing tool soon became utilized in many other environments.

Amazon’s “You might also like (…)” or “Other customers bought as well (…)” suggestions are based on recommendation algorithms acting as a salesmen who automatically identifies the customer taste and then drives conversions via tailored product recommendations.

Recommendation engines showed up in ecommerce first, but nowadays you may also find them in other sectors such as media (YouTube or Amazon Prime, “Recommended Videos”), social networks (Facebook, “People you might know”) or even search engines (Google, “Visually similar images”).

Over time, recommendation systems and engines became more sophisticated. Today systems are available for almost any working environment:

Source

Benefits of Recommendation Systems

There are some strong advantages recommendation engines have over other marketing strategies and their related tools:

  • Simplicity: Just install the engine, the rest is done almost automatically (data collection and recommendations).
  • Reduced Workload: In most cases, installation via a plugin or a script is equally easy to perform.
  • Lift: You may increase AOV and LTV with ease, depending on your visitor base and your business.
  • Versatility: Many engines and suites allow you to choose the right algorithm for your website.
  • Customer Compliance: If not displayed intrusively, product recommendations may increase user compliance due to new products they would have not discovered otherwise.
  • Tailored User Base: If your user base doesn’t change much, the algorithm becomes more and more accurate over time being specifically connected to your business

Drawbacks of Recommendation Systems

While I generally recommend to use recommendation engines [sic] for any kind of business, there are some drawbacks:

  • Sample Size Noise: Adding more items to your catalogue may increase the noise and reduce recommendation quality, especially if items are rarely picked.
  • Missing Trust: Users may not trust recommendations that are created automatically.
  • Cold Start Problem: You need enough observations (users pick items) to display valuable results. Recommendation quality improves over time.
  • Narrow View: The database used by recommendation engines often is isolated (e.g. restricted to the website) not taking user actions into account from other sources.
  • Lacking Persistence: In some situations, it may be better to re-show recommendations or let users re-rate items than showing new items.
  • Lack of Control: Lack of control because recommendation systems usually run automatically on your website.
  • Missing Insights: You may not get enough valuable insights in how your visitor base behaves.
  • Missing Segmentation: Rolling out recommendations for your whole audience regardless of its user segments leads to broad results.
  • Slow Adaptation: If your user base changes or new traffic sources open up, the system needs time to reiterate and test.

Ground Control To Major Tom, Or: Going Beyond Recommendations?

Source

Recommendation systems help you to get a broad direction on your market. Look at it as your first step into machine learning, data driven marketing. However, you are still targeting your whole website audience instead of individual user flows.

No doubt: Increasing AOV and LTV can become a tough challenge. Many marketers therefore focus on the whole traffic and simple user actions (such as browsing a product) and tend to forget about the big picture.

Optimize For Individual Users, Not For an Audience

What matters most is giving the right response to a specific user at the right time.

Segment Your Users Via a Personalization System

The central idea of a personalization system such as Intempt is to

  • Segment your identified users based on tracked website behaviour and third party data
  • Change your website individually in real time
  • Grow by increasing KPIs that matter to you (e.g. AOV & LTV)

This system is completely customizable, so you may recreate simple behavioral campaigns including recommendations or abandoned cart notifications, but you may also go far beyond by personalizing your user experience across channels using all user data available.

Cover Any User’s Lifecycle

With a user personalization engine such as Intempt you may cover all phases of individual user lifecycles:

Pre-Purchase – Attract (Day 0)

  • Which user segment should I target with personalized offers so they make their first purchase?
  • How can I re-merchandize products based on browse behavior to make the shopping experience relevant on the first visit (use case below)?

In the Conversion Path – Enage (Day 1 – n)

  • Where do people get stuck on my website so I can move them along?

Retention – Grow (Day n+1)

  • Which of my prospects are on the fence? What can I do with those?
  • Does my website automatically personalize to users who I sent emails to and have CRM data on (use case below)?

Advantages of a Personalization System Over a Recommendation System

  • Small Segment Size: Because user personalization takes all user data into account (including email or CRM), the segment size used in a campaign can be small
  • User Compliance: You might or might not run personalization campaigns that are visible to your users.
  • Broad View: Data from all angles is used to create rich user profiles.
  • Full Control: You may adapt you campaign events, segments and setups at any time.
  • Missing Insights: You may get valuable insights in how your visitor base behaves even without running a campaign. All collected data remains persistent.
  • Rich Segmentation: Precisely targeting user segments leads to better results with precise impacts.
  • Fast Adaptation: User personalization takes all user actions into account, not just browsing specific product pages. Because the database is richer, the adaption is fast.

Pre-Purchase Personalization Use Case: The Drop

On Shopify, users get to see category pages displaying products in a default sort order (based on the Shopify settings), e.g. ‘Best selling’ or ‘Newest’.

But visitors are unique: Some (even subconsciously) pick rather expensive products, others browse only cheap items. Then there are users who browse both kinds of products, so no behaviour pattern is visible from a price point of view.

What if you could automatically alter the sort order based on the user’s previous browsing behaviour so it is more likely users convert?

You may say: ‘Users can change the sort order at any time’.

While this is true, wouldn’t it be better to already deliver the right sort order to the right user to increase the chance of boosting the AOV of a first-time user?

Especially when considering the fact that many users don’t use the setting options a shop system offers to them?

What is The Drop?

To dive into on-site behavioral merchandising, let us take a look at this Shopify shop from one of our clients:

 

 

It is a typical apparel shop with a large catalog containing hundreds of products. In other words: An ideal candidate for our campaign.

Change The Default Category Page Sort Order

As you might know, Shopify allows to display collection and category pages with a preset of different sort orders:

  • Manually
  • Best selling
  • Highest price
  • Lowest price
  • (…)

Let us define the user’s behavior that matches our campaign segment first:

If

  • a first-time user
  • coming from Google
  • browses three high priced products in a row

I would consider this visitor to be interested in high priced items.

Then

  • I would like to display category pages
  • with a sort order different from the default sort order: Highest Price on top
  • instead of New on top
  • To help the user getting oriented

To this, we need to override the menu item links by adding a sort order query.

A default Shopify category link looks like this:

my_shop_url.com/collections/my_collection

whereas a ‘treated’ link with a sort order highest to lowest price would look like this:

my_shop_url.com/collections/my_collection?_=pf&sort=price-descending

Returning users and users from other traffic sources such as social, paid or other referral websites I explicitly would want to exclude from this campaign because I would not want to disturb their user lifecycle or run other campaigns on these segments.

Create an On-Site Behavioral Merchandising Campaign in Intempt

With Intempt it is easy to deliver personalized category or collection pages based on the actual behaviour of a user. It doesn’t take more than 10 minutes to create such a campaign.

You don’t need to collect a lot of user data before running the campaign nor trust in any machine learning logic. You are still in full control of the strategy while the campaign runs automatically.

Install Your Tracker

To set up such a campaign, you would first need to install the Intempt tracker – a code snippet similar to Google Analytics – collecting each user’s behaviour.

Create Your Event

Afterwards you may create an event to identify high priced items. Simply tell Intempt what the price and a product page is by adding some additional code to your website.

Create Your Segment

Now you can easily create a user segment. The segment in our case could look like this:

  • A first time visitor coming from Google browsing
  • A t-shirt
  • Above $100
  • More than two times
  • Within last 30 seconds (recency)

 

 

Note that the first user property is fixed. The Intempt tracker stores all information available from the HTTP header, such as the referral website host, in this case Google.

The other four properties are behavioral properties segmenting the user (browsing) behavior.

Information about the product such as the product category or the vendor can be retrieved

  • directly via the page URL (if it contains this information) or
  • by using a script submitting this data to Intempt

whenever a user browses a category or a product page.

Data from the referer (Google) is combined with data from the actual website and merged into a powerful personalization campaign.

Set Up Your Campaign

The last step is to create a campaign. The campaign is used to define the entry segment outlined above but also a goal event for evaluating the success. You may also add additional rules when the campaign should trigger.

Create Your Event Listener

Let us now get back to your online store.

You are tracking your user behavior. If this behavior matches a certain pattern (segment) the Intempt server is sending back a signal to the script running on your website.

An additional script running on your store (an event listener) now overrides every category link as soon as the signal arrives.

A default visitor would have this experience when browsing the t-shirt section:

 

 

…while a first time user coming from Google and browsing at least three t-shirts above a $100 in a row would see this:

 

Retention Personalization Use Case: Farfetch

What is Farfetch.com?

Founded in 2008, Farfetch is an online luxury fashion retail platform that sells products from over 700 boutiques and brands from around the world. It already served more than one million customers and is valued one billion USD.

 

 

Products sold on Farfetch are often high priced and target fashion enthusiasts and fashionistas worldwide.

Increase Farfetch’s Average Customer LTV Via Email Marketing and On-Site Behavioral Messaging

To increase the average customer LTV generated on Farfetch, the platform would first create an email marketing campaign sending out engaging different newsletters to three user segments based on previous purchase behavior and resulting LTV:

  • High end customer (spent > 5000 USD)
  • Mid range customer (spent > 2000 USD & < 5000 USD)
  • Low end customer (spent < 2000 USD)

Each segment will receive its own newsletter template containing suitable information for this group of subscribers.

Whenever a user (who browsed Farfetch via an email campaign link prior) returns to the site, this user would get to see a personalized engaging offer depending on the previous purchase behavior of this visitor.

How do we get there?

Email Campaign Setup

The three segments are created by retrieving sales data from the shop system and storing the results in a email list field.

The email templates also include links to Farfetch with an unique ID attached (using a mail merge field). This ID is tracked by Intempt’s platform whenever a reader clicks on the link attached in the email, allowing a precise segmentation, attribution and data synchronization across platforms.

Create Behavioural Segments

To display personalized offers, Farfetch would create three segments in Intempt (mirroring the email campaign):

 

  1. An identified user (tracked via the ID attached to the email link)
  2. Who is either labelled (via a Mailchimp list or CRM file import) as a high end customer, mid range customer or low end customer
  3. Visited Farfetch more than one time (is a returning user)

In order to identify users properly on the website that have been approached via email before, the email campaign list needs to be uploaded to Intempt using a CSV file.

 

A high end customer segment in Intempt’s segment editor:

Campaign time

Farfetch already added a grey bar to their website displaying offers and announcements (located at the top below the main menu).

Any user who does not fully meet the campaign requirements:

  • Is part of a segment (high end, mid range, low end)
  • Has clicked on a link encapsulated in the newsletter and browsed Farfetch
  • Has later browsed Farfetch again

 

would get to see default product pages:

 

Instead, a high end user who clicked on an attached email link once and returned to Farfetch later would get to see this offer:

 

…whereas a mid range user doing the same steps would get to see this:

 

…and finally a low end user meeting all campaign conditions would see this:

 

Please note Intempt can dynamically override any existing page content. Except from the tracker installation no further IT involvement is required, which is why Farfetch would use the existing bar.

It is possible to adapt the segments within Intempt at any time because MailChimp and Intempt share the same database and use user specific IDs.

As an example, you may in- or decrease the spend threshold of the three segments (“high end”, “mid range” and “low end”) simply by changing the segment criterias directly in Intempt.

You may also dynamically override the imported database based on each user’s action. If a user classified as “high end” does a “low end” purchase (e.g. < 2000 USD), then this user could be labelled as “low end” automatically by tracking the cart value at the checkout (using Intempt’s trackcharge feature).

If Farfetch’s email marketer then re-uploads this list from Intempt to MailChimp, the database will be up-to-date mapping the actual and individual visitor behaviour when creating the next email campaign.

User Personalization Helps You To Increase AOV or LTV

You may increase first-time AOVs by delivering personalized websites helping new users to navigate and browse to the right products they are likely to buy.

Stitching behavioral strategies together by combining channels such as email and on-site helps you to boost your average customer LTVs. Target users based on their behavior across channels while you still retain full control over your campaign.

Just be personal, in a smart way.

Want To Increase Your AOV or LTV?

Understand and Improve Your Conversion@2x

Understand and Improve Your Conversion Rates Via Google Analytics, Split Testing and User Personalization

Conversion Rate – The Unknown Species

You might have recognized there is lot of confusion around the conversion rate topic. In this post I would like to shed some light on it and clear the mist.

You will learn how Google Analytics calculates different conversion rates and why you might want to change the way you look at these numbers to make them become more relevant to your business.

Second, I would like to show how to improve the conversion rate using the model we discussed before.

Please note: This post requires some basic understanding of Google Analytics. However, I have linked further information so you can easily look up these topics.

Ready? Let us dive in…

Conversion Rate Basics

Like any other KPI, conversion rate is nothing else but a metric.

A metric is a calculation of several measures (e.g. number of sessions, users or goal completions). Any metric or even a measure may become a KPI as soon as you define it as such making relevant for your business.

The main reason for the confusion around the conversion rate definition is that the measures used for calculating this metric vary from case to case. Let me give you an example.

If you look up conversion rate in Wikipedia, you are redirected to a conversion rate definition for conversion marketing. Why? Likely because one may apply this metric to many channels and it is hard to define a single definition.

As an example, Google Ads defines a conversion rate for their own channel as follows:

Conversion rate = Number of goal achievements / Number of total ad clicks

So this definition is completely bound to Google’s ad service.

But even when we look at the formula presented in the above article about conversion marketing (focussing on conversion rate optimization (CRO) and on-site measures), it gets confusing:

Conversion rate = Number of goal achievements / Number of visitors

What’s wrong with this formula?

An on-site conversion rate doesn’t necessarily has to be based on the number of visitors. In fact, widespread Google Analytics uses the number of sessions (visits) instead of users (unique visitors) when calculating goal or ecommerce conversion rates. Minor difference, major impact – we will get to this a little later.

Same for the goal achievements: Do you want to count a goal achievement only once or how often it has been reached during a session or by a user?

The most basic definition of a conversion rate could be:

Conversion rate = Number of goal achievements per entity / Number of potential goal achieving entities

This isn’t academic talk. The reason why many people struggle with defining and interpreting a meaningful conversion rate for their business is because they don’t question the measures their conversion rate metric is based on:

  • What do I define as a (conversion) goal?
  • What entity do I want to look at that may potentially reach this goal?

Let us take a look at Google Analytics using Google’s terminology and then discuss the potential pitfalls afterwards.

Goal Conversion Rate in Google Analytics

Most CRO owners usually use Google Analytics’ goal conversion rates to measure their marketing performance.

Google Analytics calculates the goal conversion rate as follows:

Goal conversion rate = Number of unique goal achievements per session / Number of sessions

Number of Unique Goal Achievements

In most cases marketers have set up URI goals, meaning a goal is completed once a certain URI is reached during a session (visit). This URI usually is a checkout or thank you page.

Hint: If you are not familiar with how to set up a goal in Google Analytics, check out my post on process step optimization.

If a goal is reached multiple times during a session (visit), it still is counted only once. Let me illustrate this:

  • User A
    • Session A: Page A > Page B > Goal > Page C > Page D
    • Session B: Page A > Page B > Goal > Page C > Goal

Two goal completions are registered for both sessions, even though in session B the goal was reached two times.

The goal conversion rate in the above example would be:

Number of unique goal achievements per session: 2 / Number of sessions: 2 = 100%.

Be careful: If you use a URI path as a goal, the number of unique pageviews for this page might differ from the number of goal completions. Why?

The reason is if you set up your goal URI using head match or regular expression match, you might include more than one page in your goal definition, e.g.:

/thankyou
/thankyou/confirmation

could be counted as one goal, depending on your goal setup. So please make sure your goal is set up properly according to your requirements.

Number of Sessions (Visits)

As mentioned earlier, Google Analytics doesn’t use users (unique visitors) to calculate the goal conversion rate but the number of sessions during which a goal was completed.

As a reminder, a session is closed after 30 minutes of inactivity or at midnight (I am not covering any additional exceptions here).

That said, be careful when looking at the goal conversion rate in Google Analytics. Let me illustrate this:

  • User A
    • Session A: Page A > Page B > Goal > Page C > Page D
    • Session B: Page A > Page B > Goal > Page C > Goal
  • User B
    • Session D: Page A > Page B > Page A > Page C > Page D
    • Session E: Page A > Page B > Page D > Page C > Page D
    • Session F: Page A > Page B > Goal > Page C > Page D
  • User C
    • Session G: Page A > Page B > Page D > Page C > Page D
    • Session H: Page A > Page B > Page D > Page C > Page D

How many goal completions are tracked by Google Analytics in this case? Correct: three.

  • In total the goal has been reached four times, but session B is counted as one goal completion even though the goal was reached two times during this session
  • The goal has been reached by two users, but it is counted three times because User A reached the goal twice during two separate sessions.

The goal conversion rate in the above example would be:

Number of unique goal achievements per session: 3 / Number of sessions: 7 = 43%.

As you can see it can get tricky when relying on Google’s way of interpreting measures when calculating its goal conversion rate. We will discuss a little later when Google Analytics’ default conversion rate might match your business and when not.

Funnel Conversion Rate in Google Analytics

Hint: If you are not familiar with it, check out my post on process step optimization.

Consequently, Google Analytics uses the same formula for its funnel conversion rate:

Funnel conversion rate = Number of unique goal achievements per session / Number of funnel step 1 sessions

The only difference is that you have funnel steps included and the number of sessions is taken from the first funnel step instead of the whole page to calculate the funnel conversion rate.

eCommerce Conversion Rate in Google Analytics

If you are running a website where visitors purchase products or items, you likely would want to look at the ecommerce conversion rate instead of the goal conversion rate in Google Analytics.

If you are not familiar with it, check out Google’s help page for enhanced ecommerce data in Google Analytics to set it up – it is easy to set up.

Let us look at the formula Google uses for ecommerce:

eCommerce Conversion rate = Number of goal achievements per session / Number of sessions

In Google Analytics, goal achievements for ecommerce are called transactions. You might have recognized it: This time the goal achievements are missing the term ‘unique’.

In contrast to the conversion rate metrics we discussed before, this time multiple goal completions (transactions) can be counted within one session.

Let us have a look:

  • User A
    • Session A: Page A > Page B > Transaction > Page C > Page D
    • Session B: Page A > Page B > Transaction > Page C > Transaction
  • User B
    • Session D: Page A > Page B > Page A > Page C > Page D
    • Session E: Page A > Page B > Page D > Page C > Page D
    • Session F: Page A > Page B > Transaction > Page C > Page D
  • User C
    • Session G: Page A > Page B > Page D > Page C > Page D
    • Session H: Page A > Page B > Page D > Page C > Page D

How many transactions are tracked by Google Analytics in this case? Correct: four. Every transaction is counted, regardless if it happened multiple times during a session (visit) or just once.

The ecommerce conversion rate in the above example would be:

Number of goal achievements per session: 4 / Number of sessions: 7 = 57%.

Be careful: Because transactions are counted every time they happen, unintended counts might occur:

  • A user refreshes the confirmation page
  • A user bookmarks the confirmation page and opens it later
  • A user closes the browser, opens it later and the confirmation page gets reloaded

Unfortunately, you can not prevent Google Analytics from tracking these “false” transactions.

Instead, you would need to either:

  • Prevent the confirmation page from reloading or
  • Prevent the ecommerce Google Analytics script from firing more than one time

Segmented Conversion Rates in Google Analytics

You may easily create a segment in Google Analytics to recalculate the conversion rates outlined above based on the sessions segment you would like to look at.

If you want to only look at the ecommerce conversion rate for US users, you may create a segment filtering out users from other regions.

There is one exception though: You may unfortunately not apply segments to a conversion funnel visualization. If you want to look at the funnel conversion rate for a specific segment (e.g. a specific traffic source), you would need to create a filtered view from where you can call the funnel visualization.

Do Google Analytics’ Conversion Rates Really Help You To Understand Your Business?

As outlined, there are many ways to look at the measures that are used to calculate conversion rates.

eCommerce aside, Google Analytics calculates the metric as follows:

Conversion rate = Number of unique goal achievements per session / Number of sessions

For some cases this might be fine, but in many cases it isn’t helpful. Why? Let me use the example above once again:

  • User A
    • Session A: Page A > Page B > Goal > Page C > Page D
    • Session B: Page A > Page B > Goal > Page C > Goal
  • User B
    • Session D: Page A > Page B > Page A > Page C > Page D
    • Session E: Page A > Page B > Page D > Page C > Page D
    • Session F: Page A > Page B > Goal > Page C > Page D
  • User C
    • Session G: Page A > Page B > Page D > Page C > Page D
    • Session H: Page A > Page B > Page D > Page C > Page D

Let us iterate through some possible conversion rates…

Goal Conversion Rate 1 (Session Goal / Sessions – Google Analytics Default)

Google’s goal conversion rate in the above example would be:

Number of unique goal achievements per session: 3 / Number of sessions: 7 = 43%.

Goal Conversion Rate 2 (User Goal / Users)

While this is correct, you might rather want to look at the percentage of users (unique visitors) achieving a certain goal in total (per user):

Number of unique goal achievements per user: 2 / Number of users: 3 = 67%.

Choose Wisely When Calculating Your Conversion Rate

43% vs 67% goal conversion rate – holy moly! Let us look at some use cases to better understand this correlation…

Use Case: SaaS Website

If

  • it takes many sessions for users to sign up on a SaaS website
  • but finally almost every user signs up

then

  • Google Analytics’ default goal conversion rate will be very low
  • whereas the user focussed conversion rate will be very high
Use Case II: Ecommerce Website

If

  • it takes only a few sessions for users to purchase a product on an ecommerce website
  • but hardly any user purchases the product

then

  • Google Analytics’ default goal conversion rate will be very low
  • and the user focused conversion rate will be equally low

Quo Vadis Conversion Rate?

As you could see, in some cases session and user based conversion rates match, in others they don’t.

You might say: “This isn’t very convincing, show me some realistic scenario!”. Right so. Let us do quick calculation. Let us say we have an online services business with one time subscriptions set as a goal (per month, on average):

  • 10000 users
  • 3 sessions per user
  • 2% of all users reach the goal at least once
  • 10% of all sessions reach the goal at least once

Google Analytics’ goal conversion rate in this case would be:

Number of unique goal achievements per sessions: 3000 / Number of sessions: 30000 = 10%

Interpretation: 10% of all sessions lead to a subscribe. Is this relevant in this case? Somewhat. It tells you that some users have subscribed more than one time. You might want to investigate why. Perhaps there is some error in the subscribe funnel?

A user oriented goal conversion rate instead would be:

Number of unique goal achievements per user: 200 / Number of users: 10000 = 2%

Interpretation: Every fiftieth user subscribed. This is very likely what you would be more interested in in this case. You would want to optimize your website so more users reaching the website subscribe.

You might ask: “What should I do?” The unsatisfying question is: it depends. You might want to ask yourself:

  • Is the goal something I want users (visitors) to do every time they visit?
  • Is the goal something I want many users (visitors) to do?

If you want to focus on the former, choose session goals and sessions (visits) as a measure, otherwise user goals and users (unique visitors).

Goals suitable for a session (visit) oriented view:

  • Services Usage
  • Logins
  • All ecommerce purchases

Goals suitable for a user (unique visitor) oriented view:

  • App downloads
  • Lead generation
  • Sign-Ups
  • First time ecommerce purchases (customer count)
  • A/B testing

Also keep in mind many analytics and optimization tools such as Heap, Optimizely and also Intempt use users as a base measure, not sessions. If you want to sync with these tools using Google Analytics, you might want to focus on users instead of sessions, depending on the context.

How To Create a User View In Google Analytics?

If you want to calculate user related data instead of looking at sessions in Google Analytics, it gets a bit tricky. First let us go to “Audience Overview” where you get to see sessions and users (I am using a real account here from one of our clients, so I’ve blurred data):

Next, you would need to create a segment, based on your goal. If it is non ecommerce, you simply can create an advanced segment filtering out users who have completed the goal at least once:

For ecommerce, you may alternatively use the buit-in segment “Sessions with Transactions”:

After you have enabled this filter, you may calculate the user based goal or transaction conversion rate:

What would the conversion rate be in this case? Let us use again the formula discussed above:

Number of unique goal achievements per user: 370 / Number of users: 31584 = 1.17%

Let us compare: In this case

  • The user focused conversion rate is 1.17%,
  • Google Analytics’ goal conversion rate is 0.97%
  • Google Analytics’ ecommerce conversion rate is 0.92%

So the user conversion is way better than it might appear at first glance.

How to Improve Your Conversion Rate?

After we discussed the conversion rate metric or KPI in detail, it is time to look at measures and technologies that help you to improve this metric and leverage your business.

Whether you are new to conversion rate optimization or a pro already, I bet you (at least sometimes) struggle with finding the right tools to do the job…this isn’t 2000 where only a few and very basic technologies were available to drive conversions.

In 2018 it is…complicated (to say the least):

Source

Marketing Strategies – the Crux of CRO?

Many marketers get overwhelmed when asked to pick the right tools for their business. So let us step back for a second and look at common strategies used for conversion rate optimization:

  • Split Testing
  • Recommendations
  • Abandoned Cart Notifications
  • Ratings and Reviews
  • Email Personalization
  • Chat / Click-to-Call
  • Automated Guides
  • Re-Targeting

No matter if you sell consumer services or products, there is certainly always room to improve your website / app so users are more likely convert.

Conversion Rate Optimization – Strategize First

Because conversion rate optimization is complex, it often helps me to systematically strategize first. I simply split the user flow into:

POSITIVE ASPECTS NEGATIVE ASPECTS
Engage your visitors, e.g. create tempting call-to-actions Remove design hurdles and solve or overcome usability issues

…and think about strategies that could fit for my website. Let us sort the strategies outlined above accordingly:

POSITIVE ASPECTS NEGATIVE ASPECTS
Split Testing Split Testing
Recommendations Abandoned Cart Notifications
Ratings and Reviews Chat / Click-to-Call
Email Personalization Automated Guides
Automated Guides Re-Targeting

If we look at the table closely, we may find some strategies match with both aspects, which is why they are more versatile when getting hands on the conversion rate optimization.

My general recommendation is to focus on these strategies instead of cosmetic changes or at least start incorporating them at an early stage.

While e.g. abandoned cart solutions might initially boost conversions and be easier to set up, they don’t help creating sustainable and long lasting assets that withstand the test of time (e.g. when your user base changes).

One sustainable conversion rate optimization strategy is to run split tests.

Your Swiss Army Knife For Conversion Rate Optimization: Split Testing?

Split testing is on the market for long. In fact, Google did their first A/B testing in 2000 aiming at finding out the optimum number of results to display on a page.

In case you are not familiar with the concept: A/B testing compares two variants of a single variable (e.g. “Does call-to-action A or B engage more people to convert?”). The testing helps you to adapt and redesign your website based on user preferences and variant performance.

Multivariate testing simply is an enhancement of this idea, allowing you to test more than two variants at the same time. Both A/B and multivariate testing are labelled “split testing” describing the general method behind this technology.

Benefits of Split Testing

There are numerous advantages split testing has over other marketing strategies and their related tools:

  • Flexibility: You may work on all aspects of your website: design, content or structural changes
  • Control: You may exactly control the testing and changes
  • Lift: You may create great conversion rate lifts through split testing
  • Insights: You may get valuable insights in how your visitor base behaves
  • Versatility: You may work on CRO positive and negative aspects for all kinds of businesses

Drawbacks of Split Testing

While I generally recommend to use split testing for any kind of business, there are some drawbacks:

  • Sample Size: You might not be able to reach statistical significance in case of low traffic.
  • Work Intensity: Tests may require involvement of other departments (Design,R&D) slowing down the workflow while increasing costs heavily.
  • Saturation Point: You may initially see a reasonable conversion lift when testing drastic variants. Minor changes however may not show statistical significance.
  • Missing Segmentation: Testing against your whole audience regardless of its user segments leads to broad results. Applied changes miss out user segments that would have converted better with other variants.
  • Missing ROI: As you progress, lower lifts and increased design work question the ROI.
  • Constant Testing: Tested data only applies to one test, you cannot reuse it. Test rounds must be planned thoroughly if the goal is to measure interactions between isolated elements.

Want To Go Beyond Split Testing?

Split testing helps you to get a broad direction on your market and your message. However, you are still optimizing with an ideal audience in mind instead of individual user flows.

What does that mean?

Because conversion rate optimization is complex, many marketers focus on the whole traffic and tend to forget about the big picture.

Optimize For Individual Users, Not For an Audience

User personalization means to put marketing back on its feet.

What matters most is giving the right response to a specific user at the right time. Not the audience you optimize for nor the tools you use should restrict this response, but rather where your user is at during the individual decision making process.

Let me explain this…

Who Are My Users?

The above question can be answered in a simple and a sophisticated way.

The usual perspective is to look at a generic website audience, meaning: “Who are my users – so I can deliver the best possible website to all of them?”

However, smart marketers are thinking differently: “Who are my users today – what do they need right now, in this moment?”

Segment Your Users Via a Personalization System

The central idea of a personalization system such as Intempt is to

  • Segment your users based on
    • Tracked website behaviour and
    • Third party data
  • Change your website individually in real time
  • Grow by increasing your conversion rate

The user data may come in from multiple sources:

  • CMS
  • CRM
  • Search engines
  • Email
  • Social
  • Retargeting

This system is equally flexible. You may address positive and negative CRO aspects:

  • Deliver engaging call-to-actions only to the right users
  • Deliver helpful messages if users struggle and are about to drop off
  • Change your website whenever a specific user shows up

Let me outline the advantages of a personalization system over a split testing system:

  • Sample Size: Even with low traffic you may create powerful user segmented campaigns.
  • Work Intensity: If you use on-site messages (see below) the required involvement of other team members is minimal.
  • Saturation Point: Precisely create as many behavioral campaigns as you like.
  • Segmented Users: You may precisely target specific user segments instead of your whole website audience.
  • Retrospective Database: You may always go back in time to understand your visitor behavior and create new campaigns from this database.
  • Predicted Campaigns: You may also predict your user’s next steps and take action using collected data from previous users (see below).

Let me show you how using a client use case.

User Personalization Use Cases: Kukun

One of our clients, Kukun, is an online services provider helping house owners to estimate their home remodeling projects, research best loan options, find professionals and much more.

Each of these services result in a separate conversion funnel, and almost every user has a specific objective:

  • Estimating the cost of a project
  • Seeking inspiration around home remodeling projects
  • Applying for a loan
  • Finding professionals in the neighborhood
  • Joining Kukun as a professional and get listed on the website
  • Contacting Kukun for a business cooperation

The Kukun website requires to guide users carefully. We do this by segmenting them based on intents they show via their behavior.

Kukun Behavioral Split Content Campaign

Kukun’s project estimator is a multi form tool that displays an estimate after users have entered all information on their project.

When users get to see their results, they are asked to calculate their project recoup:

We aimed at displaying a different version of this page to users who previously showed intent in applying for a loan to drive conversions for this funnel.

These users – and only these users – should get to see a block element engaging them to apply for a loan:

Intempt can dynamically override any existing page content. Except from the tracker installation no further IT involvement is required.

How did we get there?

Installing The Intempt Tracker

Before creating the campaign, we installed the Intempt tracker on Kukun’s website. The tracker is a code snippet similar to Google Analytics collecting each visitor’s behaviour.

Create a Behavioral Segment

We first defined a behavioral segment in Intempt matching our campaign goal:

  • The user reached the end of the estimator and
  • Browsed at least one loan related page before

Setting The Campaign Up

We copied the page element’s code from Kukun’s website and adapted it matching users interested in loans. This code then was added to the campaign.

Whenever all campaign conditions are met and a user is segmented, the Intempt server sends the adapted element to Kukun’s website.

Kukun Predictive Behavioral Campaign

For this campaign we aimed at displaying an engaging message to users who are predicted to not finish an estimate based on:

  • The actual user behaviour
  • All previously collected user behaviour data

to drive additional conversions for this funnel:

How did we get there?

Create a Predictive Segment

We first defined a predictive segment in Intempt matching our campaign goal:

  • The user will very likely not begin an estimate

Setting The Campaign Up

We then created three variant texts to split test them:

These variants can be investigated later using Intempt’s campaign analytics:

Optimize Conversion Rates Via User Personalization

Target visitors based on their behaviour across channels while you still retain full control over your campaign.

If you are struggling with long customer journeys and fragmented visitor segments, this approach can be your tool to drive conversions. Just be personal, in a smart way.

Want To Get Your Conversions Boosted?

Improve Your Search Results With Structured Data @2x

Improve Your Search Results With Structured Data (on Shopify or Other eCommerce Platforms)

The SEO Big Three

If you are running an online shop, you are likely already optimizing your website for search engines such as Google or Bing.

In 99% of these cases this means to

  • Research the right keywords for your content
  • Improve the speed of your website
  • Create backlinks to your website

While this simple recipe remains valid until today, there are little known tweaks to tempt more people to click on your organic search results and to drive conversions.

Boost Your SEO With Structured eCommerce Data

One of them is to use structured data to help search engines to understand your website better and display your search results in a better way.

You might say: ‘I am using meta titles and descriptions, what else can I do? Aren’t the search engines doing the rest?’. Well…no. Let me explain why.

How To Make People Click

If you are looking up the term ‘Pie Recipe’ in Google, you already get an idea what this bulky thing named ‘Structured Data’ is about:

If you take a closer look, you might recognize some search results look different: You can see the average rating, but also the amount of reviews and even the amount of calories. These search results are richer and therefore likely tempt more people to click as they already display information upfront.

Because these results are richer, they are called rich search results. Why is this important for eCommerce? Let us look up digital cameras:

These search results also look more appealing than average because the underlying pages use structured data to tell Google what the page semantically is about:

  • Is it a product or a blog page?
  • Is the product in stock?
  • Does the product have a price?
  • Does the product have a rating?

Machine based engines like Google became ‘smarter’ over the years, but the core principles haven’t changed much. Google shows up on your page, crawls its content and then tries to find out if your page is relevant for its users.

Structured data is a way to give search engines an insight in the semantics and the context of your page using their language. It is a relatively new feature that became more and more important for eCommerce SEO.

Why? Because research has shown that pages rank better if people find them useful, e.g. when looking up a product. But there is more to it: You may even segment and target visitors coming through Google in a more efficient yet personal way (see below).

An example: Google might dismiss your page if visitors drop off quickly after they browsed your page finding out the product they were looking for is unavailable. Google and other search engines simply don’t like it when a page doesn’t match with the visitor’s expectation.

Giving search engine users as much information upfront as possible helps you to:

  • Reduce visitor exits
  • Increase your search engine rank

In this tutorial I would like to dive into all aspects of structured data SEO and how it can help you to drive more people to your online shop. We will adapt some code. However, you don’t need to have much coding background for this tutorial.

Keep in mind: While this may improve your ranking and / or organic click-through-rate, you are still focusing on one isolated channel here rather than improving each customer’s journey (we will get to this a little later).

Supported Structured Data Types For ECommerce

Unfortunately there is no single standard for structured data (yet), but you can use one of these common schemas:

  • JSON-LD
  • Microdata
  • RDFa

Google recommends JSON-LD but supports all other schemas as well. In this tutorial I am using microdata, supported by schema.org. If you are new to structured data, this is the easiest way to markup your page content in a structured and machine readable way.

Please note there is no guarantee a search engine will use the structured data provided by you to display your search result as a rich result. However, because search engines are interested in providing useful information to their users, it is somewhat likely.

I am covering microdata and Shopify here, but you can adapt this pattern for almost any other eCommerce platform such as Magento or WooCommerce. Shopify themes can be very different, too. You might even have to ‘transpose’ this tutorial to your theme.

Test Your Online Shop’s Structured Data

Before we are going to look at your code, you might want to check if your platform already supports structured data. In most cases it does, but especially Shopify themes often lack of a proper support making tweaks necessary.

You can test your markup by using Google’s Structured Data Testing Tool to see if Google (and any other search engine) can read the structured data properly. Just enter a product page and you will get to see which structured data objects can be retrieved:

On the right side you can see the actual HTML code from the page you looked up. On the left side you see all structured data items Google can identify. In case of a product page, at least product name and price should appear. If not, you might want to adapt your theme code.

There are many properties available for a product using microdata, the most important you might want to use are:

  • brand
  • category
  • color
  • depth
  • height
  • logo
  • manufacturer
  • material
  • review
  • Sku
  • offers
  • description
  • image
  • name

Please note that a property itself can have properties. I am going to explain this below.

Using Structured Data for Shopify SEO

I am using Shopify here as an example. If you don’t know Shopify yet, it is an e-commerce platform for online stores and retail point-of-sale systems making it very easy to set up your own online shop in minutes. It offers a great online community as well as hundred of shop themes and plugins.

If you are using another platform, you may skip the next step and just open your product page theme code in your CMS.

Where To Find Structured Data in Shopify?

Adapting code in Shopify can be tricky, as there are many themes available. However, Shopify relies on some standards and files are organized in a certain way.

I am using a Shopify store from one of our clients here. To get to the product template, please do the following steps after logging into your store:

  • Click on ‘Online Store’
  • Click on ‘Customize’ on the right (theme section)
  • Click on ‘Edit Code’ on the left bottom (‘Theme actions’ panel)
  • Select ‘product-template.liquid’

The product-template.liquid file contains all major information on how to display a product page in Shopify.

How to Adapt And Tweak Your Shopify Theme?

Microdata is the easiest way of structuring your page data because you simply can add extra tags and attributes to your HTML elements. You are literally labelling these elements, e.g. by saying ‘this element represents a price section’ or ‘this element contains the product name’.

A product section in Shopify usually looks like this:

<div class=”Product Section”>
<header>
<h1 class=”Title”>{{ product.title }}</h1>
</header>
<div class="Product Content">
[...]
</div>
</div>

You first need make sure the whole product section containing all relevant product information is marked up as a product. In my case the theme added the microdata correctly, but in case it is missing you would need to add it to the top-level div container:

<div class=”Product Section” itemscope itemtype="http://schema.org/Product">
[...]
</div>

By adding itemscope and itemtype you define an area where properties of an object (e.g. ‘Product’) are valid. After we have defined the product scope, we can add more properties to elements nested in the top-level container:

<div class=”Product Section” itemscope itemtype=”http://schema.org/Product”>
<header>
<h1 class=”Title” itemprop=”name”>{{ product.title }}</h1>
[...]
</div>

Now we have declared what the product title is. Please note you can only use properties that are valid for a certain schema, all other properties are ignored by Google and other search engines. Therefore it is recommended to again run a test after you have adapted your code to verify your changes are interpreted correctly.

If you want to markup the product price, you need to define an itemscope for the type Offer before, as the price is a property of ‘Offer’. You can easily nest scopes, but please be careful to follow schema’s structure.

The price section without markup looks like this in my case:

<div>`` <span id="ProductPrice-{{ section_id }}" class="product__price{% if current_variant.compare_at_price > current_variant.price %} on-sale{% endif %}" content="{{ current_variant.price | divided_by: 100.00 }}">
{{ current_variant.price | money }}
</span>
</div>

To make the price readable for search engines, I would need to add the following scopes:

<div itemprop="offers" itemscope itemtype="http://schema.org/Offer">
<span id="ProductPrice-{{ section_id }}" itemprop="price" class="product__price{% if current_variant.compare_at_price > current_variant.price %} on-sale{% endif %}" content="{{ current_variant.price | divided_by: 100.00 }}">
{{ current_variant.price | money }}
</span>
</div>

As you can see, an element can be both, a property of a item (“offers”) and an itemtype scope (“Offer”).

Your Search Results Drive Traffic – All Good?

You use SEO to drive traffic to your website. While this is definitely a good strategy, you are only optimizing your organic traffic channel instead of individual user flows.

What does that mean? Because multi-channel marketing is complex, many marketers focus on improving their existing channels and tend to forget about the big picture. They strengthen channels which prove to be successful, but: Is it really about the channels? Shouldn’t the individual visitor be the center of any marketing effort?

Optimize Your User Flows, Not Your Channels

Visitor personalization means to put marketing back on its feet. What matters most is giving the right response to a specific user at the right time. Not the search engines you optimize for nor the tools you use should restrict this response, but rather where your user is at during the individual decision making process.

Let me explain this…

Where Are My Users?

The above question can be answered in a simple and a sophisticated way.

The usual perspective is to look at channels that drive (high quality) traffic, meaning: “Where are my users – so I can approach them?”

However, smart marketers are thinking differently: “Where are my users – in their heads, in this moment?”

In most cases you would want to address your users on your website, right there when they are actually browsing and exploring your products. Why? Because your website usually is the spot where sales are closed while your several channels such as search engines, email, social, retargeting etc. lead to it.

Don’t get me wrong: To address users on your website you still need those channels for two reasons:

  • Quantity: To drive traffic.
  • Quality: To collect data from this traffic.

The central idea of a personalization system such as Intempt is to segment users based on data coming in from multiple sources like search engines, email, social or retargeting to then guide these users on your website (like shop owners do when you are in a physical store).

And you may even predict your user’s next steps and take action using collected data from previous users (see below).

Many marketers believe if visitors click on an organic search result this means their SEO works and the specific page ranks high.

While this isn’t wrong to believe, they still focus too much on the channel instead of their actual visitors. Let me give you an example: We already touched on the price point before when discussing the structured data template. If your product range is small, it is comparably easy for your visitors to understand the pricing structure and available options your shop offers.

However, the larger the product catalog gets, the trickier it is for them to find products that match with their expectations so they would buy.

How to Guide Your Visitors So They Don’t Get Lost?

This applies especially if someone is a first time visitor coming from a search engine and not being familiar with your website. It is very likely this visitor will never return if no pleasing products are displayed within the first 2 minutes.

If you analyzed your website traffic, you might have experienced this already: visitors coming from search engines such as Google behave differently compared to visitors coming from social networks, paid ads or referred websites (e.g. a blog).

One of the reasons is organic visitors only get to see a tiny snippet of the actual landing page whereas a visitor influenced by a blog post might be already familiar with the landing page’s content.It is crucial to display products to visitors coming from search engines they are likely to buy within this short time frame to drive conversions, especially when it comes to market segments like fashion, jewelry or home decoration where visitors often show indecisive purchase behaviour.

There is a correlation between huge product catalogues and a indecisive buying patterns. When starting their businesses, many retailers don’t know which products their visitors will buy, so they add as many products as possible to their catalogues (this applies especially to dropshipping). This strategy however fuels a indecisive purchase behaviour even more and increases the likelihood for visitors to get lost in the catalogue and to drop off and never come back.

A popular method to guide visitors in the right direction is to display recommended products. Based on previous purchase patterns, the current customer journey is compared against these patterns.

While this is definitely a good way to approach the problem outlined above, it requires a huge database of existing customer journeys to be effective. Also it doesn’t distinguish between first time and returning visitors, so first timers only get to see the default recommendation.

Wouldn’t it be great to offer personalized catalogs per visitor right off the bat?

Deliver Personalized Category Pages On a Per Visitor Basis

On Shopify, users get to see category pages displaying products in a default sort order (based on your Shopify settings), e.g. ‘Best selling’ or ‘Newest’.

But visitors are unique: Some (even subconsciously) pick rather expensive products, others browse only cheap items. Then there are visitors who browse both kinds of products, so no behaviour pattern is visible from a price point of view.

What if you could automatically alter the sort order based on the visitor’s previous browsing behaviour so it is more likely visitors convert?

This is what on-site behavioral merchandising is about. It simply means to:

  • Track your visitors behaviour on your website
  • Segment them based on this behaviour
  • Change your website individually in real time
  • Grow by increasing engagement and revenue

You may say: ‘Visitors can change the sort order at any time’. While this is true, wouldn’t it be better to already deliver the right sort order to the right visitor based on the visitor’s behaviour?
Especially when considering the fact that many visitors don’t use the setting options a shop system offers to them?

The Online Shop

To dive into on-site behavioral merchandising, let us take a look at this Shopify shop from one of our clients:

It is a typical apparel shop with a large catalog containing hundreds of products. In other words: An ideal candidate for our campaign.

Change The Default Category Page Sort Order

As you might know, Shopify allows to display collection and category pages with a preset of different sort orders:

  • Manually
  • Best selling
  • Product title A-Z
  • Product title Z-A
  • Highest price
  • Lowest price
  • Newest
  • Oldest

Let us define the visitor’s behavior that matches our campaign segment first:

If a first-time visitor coming from Google browses three high priced products in a row, I would consider this visitor to be interested in high priced items and therefore would like to display category pages with a sort order different from the default sort order: Highest Price on top instead of New on top. This will help the visitor to get oriented.

Returning visitors and visitors from other traffic sources such as social, paid or other referral websites I explicitly would want to exclude from this campaign because I would not want to disturb their visitor journey or run other campaigns on these segments

To this, we need to override the menu item links by adding a sort order query.

A default Shopify category link would look like this:

my_shop_url.com/collections/my_collection

whereas a ‘treated’ link with a sort order highest to lowest price would look like this:

my_shop_url.com/collections/my_collection?_=pf&sort=price-descending

Create An On-Site Behavioral Merchandising Campaign on Intempt

With Intempt it is easy to deliver personalized category or collection pages based on the actual behaviour of a visitor. It doesn’t take more than 10 minutes to create such a campaign.

You don’t need to collect a lot of visitor data before running the campaign nor trust in any machine learning logic. You are still in full control of the strategy while the campaign runs automatically.

Install Your Tracker

To set up such a campaign, you would first need to install the Intempt tracker – a code snippet similar to Google Analytics – collecting each visitor’s behaviour.

Create Your Event

Afterwards you may create an event to identify high priced items. Similar to the structured data examples outlined above, you would simply need to tell Intempt what the price and a product page is by adding some additional code to your website.

Create Your Segment

Now you can easily create a visitor segment. The segment in our case could look like this:

  • A visitor coming from Google browsing
  • A t-shirt
  • Above $100
  • More than two times
  • Within last 30 seconds (recency)

Note that the first visitor property is fixed. The Intempt tracker stores all information available from the HTTP header, such as the referral website host, in this case Google.

The other four properties are behavioral properties segmenting the visitor (browsing) behavior.

Information about the product such as the product category or the vendor can be retrieved

  • directly via the page URL (if it contains this information) or
  • by using a script submitting this data to Intempt

whenever a visitor browses a category or a product page.

Data from the referer (Google) is combined with data from the actual website and merged into a powerful personalization campaign.

This remains is a very broad segment we are just using for this tutorial. You might would want to attach a certain product category to it or add additional constraints. Intempt is versatile, you can add as many conditions as you like when creating a segment. Or as many segments as you like.

Set Up Your Campaign

The last step is to create a campaign. The campaign is used to define the entry segment outlined above but also a goal event for evaluating the success. You may also add additional rules when the campaign should trigger.

Create Your Event Listener

Let us now get back to your online store.

You are tracking your visitors behaviour. If this behaviour matches a certain pattern (segment) the Intempt server is sending back a signal to the script running on your website.

An additional script running on your store (an event listener) now overrides every category link as soon as the signal arrives.

A default visitor would have this experience when browsing the t-shirt section:

…while a visitor coming from Google and browsing at least three t-shirts with a price above $100 in a row would see this:

Online Shop Treated User

On-Site Behavioral Merchandising Helps You To Increase Revenue

Combining off- and on-page strategies help you to ignite and influence visitor journeys and increase revenue while gaining you full control over your campaign.

If you are struggling with large product catalogues and fragmented visitor segments, this approach can be your swiss knife in marketing. Just be personal, in a smart way.

Ready To Boost Your Sales Via On-Site Behavioral Merchandising?

Get Your Visitors Engaged With Drip Email Campaigns Using MailChimp@2x

Get Your Visitors Engaged With Drip Email Campaigns Using MailChimp

Email Marketing: The Good, The Bad And The Ugly

The way marketers use emails to engage with their customers has changed a lot over the years while the basics surprisingly remained the same: email as a technique hasn’t developed much since 1990.

However, everything around this old rusty machine has grown and became more sophisticated and complex: advanced segmentation, integration of social media, heavily automated workflows and lead management help many companies to personalize their email marketing.

Today it is possible to create personalized EDM workflows that convert – even with a small budget. Is it complex? Hell yeah! Setting up such campaigns can be tricky and time consuming, especially when calculating their ROI. That said, Marketers aren’t working their lists enough!

Why not use the power of today’s technologies to additionally approach your (future) customers in an efficient yet personalized way? Simply combine your operational and strategic skills (understanding your customer base) with a systematic approach (drip campaigning) to receive the best outcome.

Leverage Your Email Marketing Campaigns

For B2C, marketers usually approach existing customers to tempt them to buy again or try out new products. These campaigns are based on the customer’s previous purchase behaviour. In some cases emails can also be used to retarget visitors who abandoned their cart before they checked out.

In other cases, however, you might want to segment your users even further, e.g. if you sell expensive products like electronics which require to nurture potential customers long before they actually buy. The sales funnel gets longer and complex.

This is what we are aiming here at with a behavioral drip campaign using MailChimp.

Why Using MailChimp For Email Marketing?

If you work for a small to mid-range company and / or you are new to email marketing, you either already use MailChimp or you likely have heard about it.

While there are certainly more sophisticated tools available on the market, MailChimp really did a great good job by simplifying the whole email marketing process.

In this article, I would like you to encourage to get your existing email campaigning to the next level. If you are new to MailChimp and curious, you still might want to read on to see what is possible.

What Is A Drip Campaign?

In a nutshell, drip campaigning means to create an automated set of emails (or responses, if we don’t restrict it to emails making it become even more powerful, see below) that are sent based on a visitor’s behaviour, e.g. when someone signs up for a newsletter or revisits your website.

You might have heard from a company of the same name, and this blog post here is using the generic method behind drip.com.

If you are not familiar with the concept yet, you might want to check out this well written introduction explaining drip campaigning in detail. In most cases, drip campaigns are used to onboard new customers, organize returns or provide educational content.

But there is more to it: Why not sending out emails to potential customers if their previous actions indicated they are really interested in the product but still need a push? To do so, you

  • Need to integrate a lead quality indicator in your MailChimp setup
  • Create an email campaign in MailChimp triggering if this lead quality indicator matches your campaign’s goals

Why would you do this? As outlined above, it is more efficient to not send the same email to all your prospects after they have taken the first step by submitting their email but rather diversify your emails based on the next steps these prospects take on their journey.

Set Up A Drip Campaign In MailChimp

Over the next few paragraphs, I am going to show you in detail how to properly set up a drip campaign in MailChimp.

Personalize Your MailChimp List

First you need to segment your MailChimp list by adding a hidden field indicating the lead quality.

While this is a strategy originating from B2B, it can be easily adapted for B2C helping you to improve your email campaigns outcome. You may also just use the lead qualification strategy alone without connecting a drip email campaign later.

A lead quality indicator helps you to understand where your visitor / lead / customer is at the moment, seen from your individual marketing perspective. You are ‘ranking’ all entries in your MailChimp list using statuses like ‘1’, ‘2’, ‘3’ and so on or rather descriptive ones like ‘Cold’, ‘Warm’ and ‘Customer’ helping your team members to understand your campaign setup.

Sounds familiar? Well, this is actually the same approach many CRM tools but also ecommerce platforms follow. The trick here is: We will change the lead quality of a visitor automatically and individually in MailChimp based on the visitor’s behaviour so it always matches the visitor’s current state. We then can take action whenever a visitor is ‘ready’ for a certain email.

Similar to CRM tools, in some cases you might also want to change the lead quality state manually, e.g. if a sales or customer support person believes a contact should not be approached. This can be done easily in MailChimp as well by manually adapting your list.

However, in this tutorial I am focussing on the automated part of it. Just keep in mind your setup might require some manual ongoing tweaks.

Your lead qualification can be based on anything you find meaningful for your business:

  • Fixed properties: gender, age, origin, operating system
  • Behavioural properties: registered, number of purchases, completed orders etc.

Here we are just focussing on email signup as this tutorial covers MailChimp only.

Open A Field Section In MailChimp

Okay, so let us get started. Assuming you have a contact list built and uploaded to MailChimp, we now can create an additional field for this list containing the lead quality indicator:

  • Go to your MailChimp list
  • Click on Settings
  • List fields and |MERGE| tags

Create New Field In MailChimp

  • Click on ‘Add A Field’

  • Select field type ‘Text’

  • Create a field label, e.g. ‘Lead Type’
  • Make field invisible for the visitor
  • Create a field tag, e.g. ‘LTYPE’
  • Click on ‘Save Changes’

Define Your Behavioral Visitor Flow

Now you have created a hidden field indicating the lead quality or type, it is time to check your behavioral campaign workflow. A sample behavioral visitor flow for an ecommerce business selling expensive electronics could look like this:

Newsletter Subscription
(Lead Type: Cold)

Automated email after 1 week

Download Product Information
(Lead Type: Warm)

Automated email after 2 days

Product Purchase
(Lead Type: Customer)

As you can see, the lead quality changes based on your visitor’s specific actions. You may of course also add even more rules and quality types to better segment and target your visitors.

You may ask: ‘Why should I not simply segment my visitors based on their behaviour and roll out a campaign?’ The reason is: By using lead quality indicators, you can address your visitors throughout campaigns and user flows. This helps you to really personalize your email strategy, because several campaigns and / or actions may change the lead quality of a visitor.

However, while this may improve your email campaigning, keep in mind you are still focussing on one isolated channel here rather than improving each customer’s journey (we will get to this a little later).

Attach An Individual Lead Type To Each Visitor

In order to address your visitors based on their behaviour, you need to attach an individual lead quality type to each contact.

You can do this on your website by using either standard MailChimp forms or any other services like Unbounce. All of these tools use a simple hidden HMTL input, e.g.:

`<input type=’hidden’ name=’LTYPE’ value=’Warm’>`

to submit this value and adapt the MailChimp list.

Create A Personalized Drip Campaign With MailChimp

After you adapted your email list, it is time to create your actual drip campaign. As mentioned before this setup is very flexible: Whenever a campaign or a visitor’s behaviour is changing the lead quality type of this visitor, you may roll out a campaign addressing to this specific visitor.

To do so, just follow these steps:

  • Go into Campaign View
  • And Click ‘Create Campaign’

  • Click on ‘Email’

  • Select ‘Automated’
  • Click on ‘Custom’

  • Click on ‘Edit’ in Campaign View, Trigger Section

  • Click on ‘Change Trigger’

  • Select ‘List management’
  • Click on ‘Changed list field to value’

  • Select the previously created list field ‘Lead Type’

  • Select the lead type you want this to trigger on (.e.g. ‘Warm’)
  • Click on ‘Update Trigger’

Now you need to set up the rest of your campaign by attaching an email template and activating the campaign.

Want to get more engagement? Simply personalize your email template further by subtly referring to the visitor’s behaviour that changed the lead type, e.g. if someone downloaded a product information, you may refer to this in your template.

Your Drip Email Campaigns Rock – All Good?

You use drip email campaigns to engage with your visitors, based on their behaviour. While this is definitely a good strategy, you are only optimizing your email channel instead of individual user flows.

What does that mean? Because multi-channel marketing is complex, many marketers focus on improving their existing channels and tend to forget about the big picture. They strengthen channels which prove to be successful, but: Is it really about the channels? Shouldn’t the individual user be the center of any marketing effort?

Optimize Your User Flows, Not Your Channels

Visitor/User personalization means to put marketing back on its feet. What matters most is giving the right response to a specific user at the right time. Not the tools you use should restrict this response, but rather where your user is at during the individual decision making process.

Let me explain this…

Where Are My Users?

The above question can be answered in a simple and a sophisticated way.

The usual perspective is to look at channels that drive (high quality) traffic, meaning: “Where are my users – so I can approach them?”

However, smart marketers think differently: “Where are my users – in their heads, in this moment?”

In many cases it is better to address your users on your website, right there when they are actually browsing and exploring your products. Why? Because your website usually is the spot where sales are closed while your several channels such as email, social, retargeting etc. lead to it.

Don’t get me wrong: To address users on your website you still need those channels for two reasons:

  • Quantity: To drive traffic.
  • Quality: To collect data from this traffic.

The central idea of a personalization system such as Intempt is to segment users based on data coming in from multiple sources like email, social or retargeting to then guide these users on your website like shop owners do when you are in a physical store.

Only market to the right users – at the right time. Sounds complicated? You can create behavioral campaigns very easy. Let me show you how…

Email Campaigning Combined With On-Site Behavioral Messaging: Farfetch.com (Use Case)

What is Farfetch.com?

Founded in 2008, Farfetch is an online luxury fashion retail platform that sells products from over 700 boutiques and brands from around the world. It already served more than one million customers and is valued one billion USD.

Products sold on Farfetch are often high priced and target fashion enthusiasts and fashionistas worldwide.

Increase Farfetch’s Revenue Via Email Marketing and On-Site Behavioral Messaging

To increase the revenue generated on Farfetch, the platform would first create an email marketing campaign sending out engaging different newsletters to three customer segments based on previous purchase behavior and resulting lifetime value:

  • High end customer (spent > 5000 USD)
  • Mid range customer (spent > 2000 USD & < 5000 USD)
  • Low end customer (spent < 2000 USD)

Each segment will receive its own newsletter template containing suitable information for this group of subscribers.

Whenever a visitor (who browsed Farfetch via an email campaign link prior) returns to the site, this visitor would get to see a personalized engaging offer depending on the previous purchase behavior of this visitor.

How do we get there?

Email Campaign Setup

The three segments mentioned above are created by retrieving sales data from the shop system and storing the results in a email list field as shown in the tutorial above.

The email templates also include links to Farfetch with an unique ID attached (using a mail merge field). This ID is tracked by Intempt’s platform whenever a reader clicks on the link attached in the email, allowing a precise segmentation, attribution and data synchronization across platforms.

Create Behavioural Segments

To display personalized offers, Farfetch would create three segments in Intempt (mirroring the email campaign):

  1. An identified visitor (tracked via the ID attached to the email link
  2. Who is either labelled (via a Mailchimp list or CRM file import) as a
    1. High End customer
    2. Mid range customer
    3. Low end customer
  3. Visited Farfetch more than one time (is a returning visitor)

In order to identify visitors properly on the website that have been approached via email before, the email campaign list needs to be uploaded to Intempt using a CSV file.

Campaign time

Farfetch already added a grey bar to their website displaying offers and announcements (located at the top below the main menu).

Any visitor who does not fully meet the campaign requirements:

  • Is part of a segment (high end, mid range, low end)
  • Has clicked on a link encapsulated in the newsletter and browsed Farfetch
  • Has later browsed Farfetch again

would get to see default product pages:

Instead, a high end visitor who clicked on an attached email link once and returned to Farfetch later would get to see this offer:

…whereas a mid range visitor doing the same steps would get to see this:

…and finally a low end visitor meeting all campaign conditions would see this:

Please note Intempt can dynamically override any existing page content. Except from the tracker installation no further IT involvement is required, which is why Farfetch would use the existing bar.

It is possible to adapt the segments within Intempt at any time because MailChimp and Intempt share the same database and use visitor specific IDs. As an example, you may in- or decrease the spend threshold of the three segments (“high end”, “mid range” and “low end”) simply by changing the segment criterias directly in Intempt.

You may also dynamically override the imported database based on each visitor’s action. If a visitor classified as “high end” does a “low end” purchase (e.g. < 2000 USD), then this visitor could be labelled as “low end” automatically by tracking the cart value at the checkout (using Intempt’s trackcharge feature).

If Farfetch’s email marketer then re-uploads this list from Intempt to MailChimp, the database will be up-to-date mapping the actual and individual visitor behaviour when creating the next email campaign.

Behavioral Messaging Helps You To Increase Engagement And Revenue

Stitching behavioral strategies together by combining channels such as email and on-site help you to target visitors based on their behaviour across channels while you still retain full control over your campaign.

If you are struggling with long customer journeys and fragmented visitor segments, this approach can be your swiss army knife to drive conversions.

Just be personal, in a smart way.

Want To Get Your Email Marketing Boosted With Minimal Effort?

Personalization For Consumer Services- Calculate Your Return On Investment (ROI)@2x

Personalization For Consumer Services: Calculate Your Return On Investment (ROI)

Unlike many other optimization tools, on-site behavioural merchandising and messaging helps you to specifically and precisely address and achieve your individual business goals.

Increase Revenue Or Growth?

In this blog post I would like to discuss these goals using typical business scenarios and how to calculate your return on investment (ROI) once you start personalizing your website.
First you need to define goals. You likely want to increase revenue and profit, but in some cases you might want to generate growth instead (e.g. when introducing a new service). Intempt’s engine can help you leverage all of these goals.

Getting Visitors Into Your Conversion Funnel

Usually marketers are tempted to start optimizing their online business by dragging more visitors into their conversion funnels (quantity). As a next step, they optimize their website for all traffic, with an ideal audience in mind (quality).
If you reached this point, the next logical step is to diversify your on-site user experiences. Why? Because you likely have diversified your traffic sources and now it is time to do the same on your website based on your visitors behaviour.

Use Our Calculator To Calculate Your ROI For Business Services

You can easily calculate your personal return on investment (ROI) when using Intempt by using our ROI calculator. ROI simply means the gain from an investment minus the cost of investment divided through the cost of investment.
Enter all your current website data (yellow cells) to see your personal ROI and your expected outcome using Intempt (green cells):

  • Time period (it is recommended to start with 12 months)
  • Your unique visitors per month
  • Your average revenue per user (ARPU)
  • Your current conversion rate (CR)
  • Your current ad spend per month

Please note this data doesn’t need to be 100% accurate. You may simply get an idea how personalization helps you to gain more revenue and profit:

Calculate Your Consumer Services ROI

As mentioned above, in some cases you might want to trade one key performance indicator (KPI) for another.
Let us assume you would want to focus on increasing user accounts because you just have launched a new service. In this case you might run a personalized growth campaign, offering a special plan to hesitant visitors based on their past or (predicted) future behaviour.
This campaign however might affect and decrease your conversion rate (CR) slightly, but your return on investment (ROI) still will be higher.
The ROI calculator is based on conversation rate (CR), the most common key performance indicator (KPI) for marketers. If you have other goals in mind and would like to see how Intempt pays off, simply get in touch with us and we will send you a report based on your individual business goals.

Calculate Your Consumer Services ROI

Calculation: How Personalization Affects Your MRR

Your monthly recurring revenue (MRR) refers to your total number of paying customers multiplied by the average revenue per user (ARPU). If you increase conversion rate with Intempt, this will automatically increase your MRR.
If a SaaS company having 80000 unique visitors per month (with 10000 visitors generated through paid ads costing $1000) and an average revenue per user (ARPU) of $200 would increase its conversion rate (CR) from 0.1% to 0.11% with Intempt (team plan), the monthly recurring revenue (MRR) would increase from $16,000 to $16,800 for this month.

Calculation: How Personalization Affects Your ROI

However, likely more important to you is your return on investment (ROI).
If you take a period of one year using the data above, the SaaS company originally would have gained $192,000 in revenue and $180,000 in profit while spending $12,000 on advertising.
With Intempt increasing the conversion rate to 0.11% while costing $500,00 per month (team plan), the revenue would be $201,600 and the profit $183,600 while spending $18,000 on ads and personalization.
This means a plus of $3600 or 2% in profit for one year.

You Spend Money To Get Visitors. Now Get Them Converted.

Analyze And Leverage Your Goals
Understand why and where your visitors don’t reach conversion goals and get them converted.

Increase Your ROI

Increase your conversion rate, revenue and profit while decreasing your CPA via rule-based and predictive on-site behavioural campaigns.

Create Powerful Personalization Campaigns

asily segment and target your audiences, create content and stylings matching your website, run powerful campaigns and get insights via personalization analytics.

Our Customer Success Team Is Always Available

After deployment, a dedicated customer success team will assist you with creating and monitoring your campaigns.

Would This Pay Off? Calculate Your Revenue And Profit!

Calculate Your Consumer Services ROI

How Behavioral Segmentation Increased Kukun’s Conversion Rate By 9%@2x

How Behavioral Segmentation Increased Kukun’s Conversion Rate By 9%

Kukun, a remodeling online service, partnered with Intempt to create a 9% lift in conversion rate. How did we get there?

The Client: Kukun

Renovating a house can be a painful and complex process. Home owners have to deal with many aspects, such as defining the right scope of project and dealing with contractors or loan providers.

Raf Howery, a serial entrepreneur, created Kukun in 2014 to address this issue and help people to save time and money.

Kukun is an online services provider helping house owners to estimate their home remodeling projects, research best loan options, find professionals and much more.

Since the start, 260,000 remodeling projects have been calculated on Kukun totalling almost 3,5 billion USD.

“My Kukun Co-Founder, Jean-Louis Ledanois, and I have built an amazing tag team based on our combined experience and vision. He helped me to see the consumer needs of his generation and I helped him see how to avoid common business pitfalls, build a strong team quickly, and reduce mistakes, all while saving money as we grew the business.” Raf Howery, Kukun Founder & CEO

Challenges: How To Create A Personalized Web Page?

Hundreds of thousands of people visit Kukun every month. Segmenting and targeting these visitors user personalization is a big challenge because Kukun offers many services to them.

Each of these services result in a separate conversion funnel, and almost every visitor has a specific objective:

  • Estimating the cost of a project
  • Seeking inspiration around home remodeling projects
  • Applying for a loan
  • Finding professionals in the neighborhood
  • Joining Kukun as a professional and get listed on the website
  • Contacting Kukun for a business cooperation

Raf was looking to deliver personalized web pages to each visitor via behavioral segmentation. But how to approach all of these unique visitors in an individual yet efficient way?

“Some visitors are completing home remodeling estimates. Others are finding professionals to begin work on projects – Intempt allows us to anticipate the needs of each visitor and notify them with timely and relevant info.” Raf Howery, Kukun Founder & CEO

Solutions: Real Time Analytics, Onsite Targeting & Personalized Content

Raf paired other website optimization measures with a personalization strategy and experienced a 9% lift in conversion rate in less than four weeks. How did this transition happen?

First, Raf suggested to begin with the project estimator, Kukun’s key asset: A multi step form displaying project cost estimates if visitors fill in all information required.
Raf’s objectives were:

  • Encourage visitors to start a project estimate
  • Retarget visitors who started a project estimate but did not complete it
  • Retarget visitors who already completed a project estimate to try out other tools

“The Intempt platform allows our marketing team to create personalized customer interactions on an individual level.” Raf Howery, Kukun Founder & CEO

Our customer success team then provided a fresh perspective on the website whereas Kukun shared its insights on its visitor base. Both parties hypothesized that the estimator’s complexity lead to less finished estimates than potentially possible.

We installed Intempt’s cross-domain tracker on all Kukun websites to identify potential funnel weaknesses on the client’s website via real time analytics.

Intempt’s machine learning (prediction) engine is visitor oriented: Besides pageviews it also tracks all other visitor actions such as clicks, form submits and cart values, helping to gain better insights into the visitor’s behaviour or even predict future visitor actions.

In addition, Raf also allowed us to use third party data from services such as Google Analytics for this onsite targeting analysis.

Based on our findings we created campaigns addressing specific visitor segments discussed above via personalized content and real time notifications.

“Data-driven notifications and real time analytics help us continuously grow KPI’s that matter.” Raf Howery, Kukun Founder & CEO

Success: 9% Conversion Rate Lift

With low effort, Kukun could grow its visitor base and leverage its business with a 9% lift in conversion rate.