3 User Personalization Ideas for eCommerce@2x

3 User Personalization Ideas for eCommerce

Despite the variety of business models, there are some challenges most eCommerce businesses face at some point:

  • How to scale via up and cross selling?
  • How to reflect individual user intents in the buying funnel?
  • How to improve conversion rates, CPAs and other KPIs?

In this blog post, we are going to take a look at three use cases revealing how user segmentation and personalization help eCommerce businesses to master these challenges.

User Personalization Use Case: The North Face

For more than 50 years, The North Face has made activewear and outdoor sports gear that exceeds expectations.

North Face’s Assets

The brands invests a lot in influencer marketing and social proofs. Browsing the “Athlete Team” section on the website, users can select an athlete’s profile for inspiration. The visitor checks out the athlete’s motivation, expeditions, videos and – most importantly – the gear they are using tempting users to buy.

Make Followers Become Buyers Via On-Site Messaging

The goal of this use case is to

  • Make new users referred by influencers
  • Become buyers
  • Based on individual user actions and intents
  • Without affecting regular users
  • Increasing conversions and LTV (Lifetime Value)
  • Evaluating the influencer cooperation via user analytics

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

How could The North Face utilize the intent data from its (external) influencers in an efficient yet individual way while using the advantages of an on-site personalization and segmentation? The aim of this campaign also is to create free shipping upsell cross sell opportunities.

Collect and Store Data Using 360 Data Model

Identifiers are used to track and identify individual users across and throughout channels making it possible to adjust and adapt a shared 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. Alex Honnold, one of North Face’s top influencers, will launch an email campaign for his subscribers pointing to the North Face online store while using identifiers for encapsulated email referral links.

The CRM

Then we will import customer data from the online store’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 Alex Honnold’s newsletter before
  • Are considered to be high level customers (current LTV above $1000.00)
  • Are predicted to leave without buying

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: “Went to Checkout”)
  • One or more message copies
  • Additional delivery preferences (5 seconds delay, triggers only at online store)

The final message could look like this:

 

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

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

Why a Message at This Time?

Users at this time

  • Are browsing the online store
  • Are identified
  • Subscribed to Alex Honnold’s newsletter before
  • Are considered to be high level customers (current LTV above $1000.00)
  • Are predicted to leave without buying

User Personalization Use Case: The Home Depot

The Home Depot is one most popular websites for DIY and professional renovation projects alike.

The Home Depot’s Assets

Home Depot’s marketing mainly focuses on content. While providing a library of instructional videos and info on DIY projects, Home Depot subtly and efficiently attracts future buyers of tools and supplies.

Convert Users Into Buyers Via On-Site Messaging

The goal of this use case is to

  • Display suitable upsell products to buy
  • Based on individual user actions and intents
  • Without affecting regular users
  • Increasing conversion rates and/or AOV

How to Display Products Only to Users Who Need Them?

A broad user spectrum makes it difficult to identify and approach users in an individual yet automated way to successfully drive conversions.

Create a User Personalization Campaign to Boost Sales

Driving conversions with tailored upsell offers is a great use case for user personalization. You may easily segment only the right users you would like to approach to while still gaining full control over your campaign (compared to a fully automated recommendation suite).

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.

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

  • Watched informational caulking videos at least two times
  • Browsed the “Bath Projects” section
  • Are predicted to not add any product to the cart

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: “Purchased a Product”)
  • One or more message copies
  • Additional delivery preferences (only trigger messages at “Caulk & Sealants” section)

The final message could look like this:

Why a Message at This Time?

Users at this time

  • Are browsing the section “Caulk & Sealants”
  • Watched informational caulking videos at least two times before
  • Previously browsed the “Bath Projects” section
  • Are predicted to not add any product to the cart

In this scenario, we offered a free shipping plus upsell option only to the right users increasing conversions and AOV.

User Personalization Use Case: Birchbox

At Birchbox users can either shop products at the shop or become a monthly beauty box subscriber. The company previously created buzz with the help of well known influencers such as Emily Schuman (300k+ Instagram followers).

Birchbox’s Assets

Birchbox is tempting indecisive customers to try out products and buy them later. Members have access to 5 beauty samples each month. They also receive points redeemable at the online store when adding full size products to their boxes and get other additional offers regularly.

Make Users Become Subscribers Via On-Site Messaging

The goal of this use case is to

  • Engage users to become a beauty box subscriber
  • Based on individual user actions and intents
  • Without affecting regular users
  • Increasing LTV (Lifetime Value)

How to Identify New Users Who Do Not Subscribe?

A broad user base makes it difficult to identify and approach users in an individual yet automated way to successfully drive subscriptions. Some users might just want to buy items at the shop, others are already subscribers. Birchbox could target new users only who did not add any item to the cart yet.

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.

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

  • Browse Birchbox for the first time
  • And haven’t subscribed yet to the box service
  • Did not add any item to the cart yet
  • Are predicted to leave without subscribing

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: “Has Subscribed”)
  • One or more message copies
  • Additional delivery preferences (10 seconds delay)

The final message could look like this:

Why a Message at This Time?

Users at this time

  • Browse Birchbox for the first time
  • And haven’t subscribed yet to the box service
  • Did not add any item to the cart yet
  • Are predicted to leave without subscribing

Boost Your eCommerce 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 Users engaged and converted?

Decreasing return rates for the smartwatch market@2x

Decreasing return rates for the smartwatch market

In this post, we talk about the problem of customer returns in the Electronics industry, using return rates as a proxy for customer satisfaction. We explore solving this problem by using a data driven marketing automation approach to increase customer satisfaction, via reducing return rates.

Jon Buys a Fitness Band.

It’s January, and Jon’s New Year’s resolutions include fitness goals. Jon buys a fitness band that is more than just a fitness band — it’s a wristband with a fitness tracker, a heart rate monitor, a downloadable app, and a feature, that Jon only vaguely understands, called bluetooth.
Jon knows he’s not the most tech-savvy guy in the world, but he also knows he’s certainly not the least. He scrutinizes the instructions, downloads the app, and makes an account with the company that makes the app. After a few frustrating misfires and some emails to customer support, Jon manages to link the fitness band to the app on his phone. He spends the next week trying to use the fitness band to track his workouts. Jon can see how many steps he’s taken, but he’s having trouble toggling between the step counter and the heart rate monitor. He knows that somewhere on the app his totals should be registered and easy to visualize, but he can’t find them. Jon isn’t sure how to input his fitness goals, and his fitness band keeps giving him notifications about going to bed hours earlier than he’s normally used to.
A week after buying his fitness band, Jon takes it off and puts it back in its box. He’s relieved.
A week after that, Jon seals up the fitness band, throws on a return shipping label, and sends it back to the retailer.
Unfortunately for electronics retailers, the Jon scenario is all too common.
In 2016, US online retailers reported return rates between 20% – 40%. Research by Accenture estimates that 68% of consumer electronics returns are labeled as NTF (no trouble found), and another 27% are due to buyer’s remorse, which can occur for a variety of reasons similar to NTF, including subpar consumer experience and a lack of consumer education. Taken together, these figures suggest that 95% of consumer electronics returns are for reasons other than product defects.
According a study by the National Retail Federation, 49% of retailers now offer free return shipping, and high rates of returns are putting an increased financial burden on electronics retailers that are already operating on razor-thin margins. Unfortunately for retailers, a free return policy is quickly becoming a “must have” to maintain customer satisfaction and ensure repeat business in competitive online market places. So, the only way to decrease costs is to increase customer satisfaction and reduce rates of return.
Jon needs to enjoy his fitness band enough to keep it.
To create a data driven, automated method that ensures an enjoyable purchase, there are two big ideas:

  • Ensure that the customer purchases the right product configuration upfront
  • Proactive personalized outreach when you have reason to believe that the customer is likely to return
  • In either case, predictive data and personalized notifications technology that understands customer behavior holds the key.

There are three aspects to a data driven, automated method:

  1. Data Collection
  2. Predictive Modeling
  3. Consumer Messaging

Data Collection

The data warehousing system needs to collect the customer behavior of the user prior to and after the purchase. If you’re an automated online retailer like Amazon, instrumenting the commerce website and app exhaustively goes a long way in aiding this type of collection. If you’re a Comcast or DirectTV attempting to reduce service cancellations, your customer “behavior” is typically split between website, phone and the DVR. Either way, classifying (and re-classifying continuously) a customer base in real-time into happy vs troubled, requires collection of behavioral data. The less comprehensive the collection, the more noisy and stale the data. Garbage in, garbage out.
With our work in this area, Intempt has successfully reduced return rates for connected devices that collect data and can communicate with one another and the mothership. Here is a quick graphic that describes the way data may be generated and collected:

Capturing profile and behavior data from device, apps and auxiliary systems

In this particular case, a consumer purchased the device online so we were able to track and collect their clickstream data prior to purchase, as well as understand their previous purchase history and satisfaction via prior NPS surveys. Because the device was connected to the internet via bluetooth tether, it could send data back periodically. We were able to understand their behavior pattern with the device post purchase.
Post purchase data collection allowed us to construct a fuller picture of the consumer’s propensity to purchase the device prior to the transaction and their interaction/engagement with the device within the first 1,3,7 days after activating the device. Access to both the deviceID and the email address of the user (entered during purchase and re-entered during activation along with registration info like phone, data-of-birth and gender) via a companion mobile app - provided a way to stitch the consumer and device profile together.
Having this information continuously collected and enriched in our systems allows us to perform the predictive data modeling and targeted messaging aspects.

Predictive Modelling

Predictive Modeling helps to ensure customers’ satisfaction with their purchases and decrease customer returns in a data driven, automated manner.
Before we began solving the customer returns problem by using a data-driven marketing automation, we knew (via customer RMA reasons) that the main reasons for customer returns fall into one of three categories:

  • Unwanted gift (1/2 of all returns)
  • Hardware issue (1/3rd of all returns) – The ideal outcome here is an exchange, not a return. Usually faulty units have battery or screen issues that get resolved in subsequent device builds. Software issue (1/5th of all returns) – These are rarely caused by an actual defect, but by inadequate onboarding and training – a significant focus of optimization.
  • Our returns analysis focused on evaluating customer behavior at the beginning of the user’s time with the product — how the device is used over the first three days, initial week and month to determine when to engage with the customer to shift the retention curve away from a return and into continued usage.

We developed a predictive model to predict likelihood of returns. This predictive model allows us to figure out whom to reach out to and when, as well as to measure the impact of that outreach to close the loop.

Cohort Analysis

Returned vs Retained Cohort Event Comparison; data illustrative, not actual

After observing customer behavior for a user in the first seven days, we built a classification model that could predict whether or not an individual is likely to replace or request a refund for their device, with 80–90% accuracy.
We did this by performing a simple cohort analysis to get a feel for what the retained vs returned user was doing on the device. We stack ranked their events by highest to lowest number of occurrences. Our database has events captured by email, and we’re able to look across devices, apps and support tickets.

Random Forest Model

Confusion Matrix for RF Classification Model; data illustrative, not actual

We then took the variables above and ran them through a random forest model to predict the dependent variable, which predicted whether the user would return the device or not. 70% of sample to train model; 30% of sample to test model.
We’re able to predict with a high degree of accuracy within n days since device activation (n=3 days, 7 days, 30 days) to understand if a user has a high likelihood of return.
So now that we can predict whether or not a consumer will make a return, how do we change that behavior? Messaging strategy and execution is a main focus of Consumer Messaging.

Personalized Customer Notifications

Personalized Customer Notifications help retailers to stay in touch with their customers, support and educate them.
Once the user was classified (3 days post registration) as an at-risk user, we sent them a series of real-time notifications at 7, 14, 21 and 30 day periods to get them to change behavior (and re-classify as not at-risk). Within the at-risk group, users were grouped into two groups, control and treated. 90% of users were treated with notifications and 10% of users are at-risk but not provided a real-time notification.
What this allows us to do is measure lift, which is the difference between the % of users that were at-risk, notified and did not return the device vs. those who were at-risk, not notified and did not return the device. This allowed us to concretely measure the effect of a real-time notification campaign.

R+7, 14, 21, 30 day notification campaign strategy for an at-risk user cohort. ~35% of users who were targeted with messages offering support and education were responsive, and a majority within the ~35% did not initiate a return.

So what does this mean for Jon?

Using the techniques outlined in these posts, Jon would have been targeted as an at risk user as a result of his initial pattern of interactions with the device in the first three days of owning his fitness band. Jon would have been contacted with targeted personalized notifications offering additional support and education regarding the features of his new purchase in the critical first weeks of use. Our work indicates that as a result of this type of targeted messaging, Jon is likely to have converted to a satisfied customer.
Doing this type of collection, modeling and messaging for a single variable (risk vs not-at-risk cohort) is conceivable to do manually, if a company has access to an in-house data scientist and marketer combo. In reality, users are often in a multitude of different states with your products, and there are a requisite number of particular notifications that they will respond to.
Figuring out these micro cohorts of current user behavior, and running a variety of campaigns simultaneously to get them to more valuable cohorts predictably, requires a software tool to work at scale from collecting data, to predictive modeling, to providing notifications.
After years of trial and analysis working in this area, Intempt Technologies has built that tool.

Learn more about Predictive Personalization and how to bring the strategy to life in your company.

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?

3 User Personalization Ideas for Content Marketers@2x

3 User Personalization Ideas for Content Marketers

Content marketing works. Rich and valuable content attracts and guides users through sales funnels without making them feel like you are pitching.

But most content marketers also face certain challenges:

  • How to utilize intent signals from blog posts and videos to drive conversions?
  • How to harvest potential interest before visitors are ready to purchase?
  • How to position content in a way it is supporting user flows and funnel conversions?

Clearly there is more to it than just producing suitable content. Smart Insights lists content marketing followed by big data and AI / machine learning as the most important marketing trends in 2018. If marketers combine content with data they may unleash the full potential of both worlds.

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

User Personalization Use Case: The Home Depot

The Home Depot is one most popular websites for DIY and professional renovation projects alike.

The Home Depot’s Assets

Home Depot’s marketing mainly focuses on content. While providing a library of instructional videos and info on DIY projects, Home Depot subtly and efficiently attracts future buyers of tools and supplies.

Convert Users Into Buyers Via On-Site Messaging

The goal of this use case is to

  • Display suitable upsell products to buy
  • Based on individual user actions and intents
  • Without affecting regular users
  • Increasing conversion rates and/or AOV

How to Display Products Only to Users Who Need Them?

A broad user spectrum makes it difficult to identify and approach users in an individual yet automated way to successfully drive conversions.

Create a User Personalization Campaign to Boost Sales

Driving conversions with tailored upsell offers is a great use case for user personalization. You may easily segment only the right users you would like to approach to while still gaining full control over your campaign (compared to a fully automated recommendation suite).

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.

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

  • Watched informational caulking videos at least two times
  • Browsed the “Bath Projects” section
  • Are predicted to not add any product to the cart

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: “Purchased a Product”)
  • One or more message copies
  • Additional delivery preferences (only trigger messages at “Caulk & Sealants” section)

The final message could look like this:

Why a Message at This Time?

Users at this time

  • Are browsing the section “Caulk & Sealants”
  • Watched informational caulking videos at least two times before
  • Previously browsed the “Bath Projects” section
  • Are predicted to not add any product to the cart

In this scenario, we offered a free shipping plus upsell option only to the right users increasing conversions and AOV.

User Personalization Use Case: Pottery Barn Kids

Pottery Barn Kids offers kids and baby furniture, bedding and toys designed to delight and inspire.

Pottery Barn Kids’ Assets

Hub of the company’s content strategy is the “Tips and Ideas” article section:

Inspiring posts often link back to the store and offer great inspiration, tips and give buying incentives.

Convert Readers Into Buyers Via On-Site Messaging

The goal of this use case is to

  • Display suitable products to buy
  • Based on individual user actions and intents
  • Without affecting regular users
  • Increasing conversion rates and/or AOV

How to Merge Content Marketing With User Intent?

A diverse user base makes it difficult to successfully drive conversions. The right intent needs to match with the right piece of content.

Create a User Personalization Campaign to Boost Sales

Driving conversions with suitable offers based on recently read content is a great use case for user personalization. You may easily segment only the right users you would like to approach to while still gaining full control over your campaign (compared to a fully automated recommendation suite).

We want to target users showing intent to buy furniture. We want to promote desks to these users if they have not added any product yet and if they browse desk related blog posts.

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.

We also may track users across the three domains:

  • Pottery Barn Kids
  • Pottery Barn Teen
  • Pottery Barn

to secure all user behavior is segmented properly.

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

  • Browsed the Pottery Barn Kids furniture section
  • Browsed any post at “Visits Tips and Ideas” related to desks
  • Are predicted to not add any product to the cart

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: “Purchased a Desk”)
  • One or more message copies
  • Additional delivery preferences (only trigger messages on blog posts)

The final message could look like this:

Why a Message at This Time?

Users at this time

  • Browsed the Pottery Barn Kids furniture section
  • Browsed a post at “Visits Tips and Ideas” related to desks
  • Are predicted to not add any product to the cart

In this scenario, we offered a desk only to the users previously showing intent in furniture while increasing conversions and/or AOV.

User Personalization Use Case: World Market

World Market is offering furniture, affordable home decor, imported rugs, curtains, unique gifts, food, wine and more.

World Market’s Assets

World Market are pros at using content as a way to guide their users though the sales funnel.
A content section called “Inspiration” provides tutorials, recipes and photo lookbooks of certain room types while featuring related and assorted products.

Convert Users Into Buyers Via On-Site Messaging

The goal of this use case is to

  • Guide users to another section before they drop off
  • Based on individual user actions and intents
  • Without affecting regular users
  • Increasing conversion rates and/or AOV

How to Only Approach Users Who Are About to Drop Off?

A broad user spectrum makes it difficult to identify and nurture users who are about to drop off while leaving other users undisturbed.

Create a User Personalization Campaign to Nurture Users

Efficiently driving conversions by offering a free shipping only to users who are about to drop off is a great use case for user personalization. You may easily segment only the right users you would like to approach to while still gaining full control over your campaign.

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.

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

  • Already browsed five Halloween items without adding any item to the cart
  • Are predicted to not add any product to the cart

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: “Purchased Any Product”)
  • One or more message copies
  • Additional delivery preferences (5 seconds delay)

The final message could look like this:

Why a Message at This Time?

Users at this time

  • Already browsed five Halloween items without adding any item to the cart
  • Are predicted to not add any product to the cart

In this scenario, we offered a free shipping option only to the right users while keeping them engaged.

Boost Your Content Marketing 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 Users engaged and converted?