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.

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?

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?

Predictive user segmentation converts visitors into customers@2x

Predictive User Segmentation Converts Visitors Into Customers

Data, data everywhere.

Data Positively Affects Your Revenue Stream

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.
The challenge is: In many cases consumer intent data outpaces the technological ability to act on it. It is a complex and pricey endeavor often available only to the largest of companies.
Marketing cloud companies have released a slew of complex products that attempt to allow marketers to harness this vast data volume. But customer data often is not continuously modeled so you may not connect properly with your customers.

Behavioral 360 User Segmentation and Micro-Targeting

Traditionally, marketers have lumped audiences into broad groups based on attributes like location or simple product category based intent.
A behavioral 360 user segmentation instead segments your users more precisely based on their actions. The data used for segmentation may be retrieved from several sources:

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

This micro-targeting however requires from marketers to know their audience niches and their needs. This is not always the case nor is it realistic when you are dealing with hundreds of products and categories.

The Rise of Artificial Intelligence (AI) in Marketing

Meanwhile, Artificial Intelligence (AI) is being touted as the next major wave of innovation. And it holds a ton of promise to allow marketers an easier and smarter path to revenue generation.
Machines can learn from past interactions and data, allowing marketers to help consumers not only with what they say they want, but to anticipate their future needs by connecting consumer interactions into one consistent and cross device stream of messaging.

Retroactive and Predictive Segmentation to Your Rescue

Armored with retroactive and predictive user profiles, the AI-enabled marketer instead may easily:

  • Predict each user’s likelihood to perform any action
  • Specify a set of personalized messages to engage with each user
  • Automatically adapt individual user journeys in real time
  • Display the right product, content, message or offer at the right time

How to Implement a Predictive Segmentation?

Online behavior can be shifted towards increased conversation rates by applying predictive segmentation to personalize user journeys.
An example: How does a retail company determine discounting on active shoppers to clear excess inventory? The company typically has no explicit data on the types of customers that react favorably to discounting. It will use its customer database and predictive modeling to identify who to offer discounts to, in real-time.
For AI to power smarter decision making it needs to operate itself on five central principles:

Collect

To learn, the machine must access rich customer data such as demographics and purchasing behavior. Using these variables, the AI based predictive marketing tool builds a statistical model that determines how predictive each variable is in terms of the answer the marketer is trying to learn.

Build

You tell the AI based predictive marketing tool what you would like it to learn for you. For each question, the probability is calculated on the basis of answers up to that point. The machine looks for combinations of attributes that create a high level of certainty about the answer it is seeking.

Learn

Eventually, the probability is weighted one way or the other. You decide how confident they want the machine to be in its answer. You may say that once it’s 95% confident, it can stop.

Tune

The model automatically updates itself with the latest visitor information and ensures continued relevancy.

Notify

Users of your website or mobile app are notified of what is most relevant to increase the likelihood of conversion. Within an AI based predictive marketing tool, you set a behavior goal, execute it on live traffic and track progress.

Predictive Segmentation and Personalization Use Case: Barre3

Let us take a look at a use case revealing how predictive user segmentation and personalization help marketers to drive conversions and other important metrics.
Barre3 created a healthy lifestyle hub. Apart from having studios in 30 of the United States, Canada and the Philippines, they offer workout accessories, apparel and online courses.

Barre3’s Assets

The brand invests a lot in influencer and content marketing alike to boost their services. Guest authors share fitness and health related tips and tricks on Barre3’s blog.

Make Followers Become Subscribers Via On-Site Messaging

The goal of this use case is to

  • Make new users
  • Referred by an influencer
  • Become subscribers to the online classes
  • Based on individual user actions and intents
  • Without affecting regular users
  • Reducing CAC (customer acquisition cost) and LTV (Lifetime Value)
  • Evaluating cooperation with the influencer via user analytics

How to Use External Influencer Data to Approach to the Right Users?

How could Barre3 utilize the intent data from external influencers in an efficient yet individual way while using the advantages of a predictive segmentation and personalization? The aim of this campaign also is tempt users to subscribe by offering a free trial period.

Collect and Store Data Using 360 Data Model

Identifiers are used to track and identify individual users across channels making it possible to adjust and adapt the database at any point of the user journey.

The Tracker

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.

The Influencer

Next, we will import external third party data. Emilie Blanchard, one of Barre3’s influencers, will launch an email campaign for her subscribers pointing to her guest post on the Barre3’s blog:

The CRM

Then we will import customer data from Barre3’s CRM indicating the customer lifetime value (LTV). We then combine and blend the influencer data with the CRM data using identifiers.

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

  • Are identified
  • Subscribed to Emilie Blanchard’s newsletter before
  • Are considered to be high level customers (current LTV above $100.00)
  • Are predicted to leave without subscribing to an online class

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: “User subscribed”)
  • One or more message copies
  • Additional delivery preferences (5 seconds delay, trigger only on blog)

The final message could look like this:

For users influenced by Emilie with a LTV lower than $100.00 we may create another segment:

…to roll out a lower value offer (one instead of three months for free):

Because Itempt is tracking the individual user’s actions, the user’s actual LTV value can be used to

  • Evaluate the influencer’s impact on the brand
  • Adapt additional bonuses
  • Encourage the influencer
  • Override the LTV to keep the database up to date
  • Get customer insights

Why a Message at This Time?

Users at this time

  • Are browsing the blog
  • Are identified
  • Subscribed to Emilie Blanchard’s newsletter before
  • Are considered to be high level or low level customers (current LTV <> $100.00)
  • Are predicted to leave without subscribing

Boost Your Business Via Predictive Segmentation and 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?