Customer Segmentation Strategy

Customer Segmentation Strategy: How To Scale Up, Save Time and Convert

Customer Segmentation StrategyEver wonder how you can “scale up” your customer segmentation strategy (without being overwhelmed?)

Are you losing track of your sea of email trigger campaigns?
Wish there was an “Automate” button for your customer email campaigns?

… Then this post is definitely for you.

When you’re creating 1-to-1 messaging to engage customers, what does 1-to-1 personalization look like?

Historically, it’s been going into an email service provider (ESP) and manually building segments that make some intuitive sense: High spenders who live in LA; or females who bought in the sports category in the last 30 days. Then target these segments with some specific messaging: “Damn it feels good to be a VIP in LA!” Then customize this message in any number of ways with variables such as {username} and {user location} and {sale date} and {rewards balance} and {product interest}. And perhaps create a series of emails to give these users multiple touches.

This is a fine way to use ESPs, as they provide deep segmentation and flowchart capabilities to enable an intense amount of manual personalization. But is there a better way to do 1:1 personalization that is quantifiable?

Rethinking Customer Segmentation Strategy for 2017

For example: Within your segment of LA VIPs, have some of them churned? For females who bought in the sports category, what if they’re equally interested in high fashion? Does everyone need information regarding their rewards balance, or their own location, and other things you’re displaying to them?


You could segment further and customize more messages, but this means coming up with a potentially endless amount of micro-segments yourself. This is just about as unscalable as it sounds, not to mention a massive time sink for something that is typically prone to errors.

Just how error-prone is a manual customer segmentation strategy, and why? For many marketers, manual segmentation is turning into a never-ending loop of looking for different segments, instead of really evaluating what stage the customer is in — and what kind of messaging is best for that stage. We’re going to examine a few promotional email blasts for you, and demonstrate what would have happened if our Artificial Intelligence engine (Cortex by ReSci) were given the opportunity to make the decisions instead.

Customer Segmentation Strategy: AI or Normal ESP?

For a campaign over a two-day period, 53,837 users were blasted a promotional message that ended with a .04% conversion rate. That’s ugly, not even factoring in these blasts’ drastic effect on deliverability.

Our Artificial Intelligence engine determined that out of the 53,937 users, 47,238 were in a state of Needing Nurturing and incorrectly received the promotion, while 6,699 were Ready to Buy and correctly received the promotion. Customers who Need Nurturing tend to be less receptive to pushy content, and need more thoughtful engagement.
Now let’s look at the performance of these two groups:

Customer Segmentation Strategy 1

Users who Needed Nurturing converted 9.5x worse than Ready to Buy on a promotional email.

In the graph below, we’re looking at 3,229 sends that are going to the Needs Nurturing customers that are less promotional. The emails are focusing more on engaging content, while leveraging our AI frequency and timing predictions.

Customer Segmentation Strategy 2

The right message at the right time converts 48x better! And even better than the promotional emails to promotion-ready customers. Is this where you should be focusing on 1:1 personalization? Let’s drill down more.

Going back and looking at the original 47,238 again, we can see which users our AI predictively score as Churned or At Risk of Churning, and how they perform relative to Ready to Buy users.

Customer Segmentation Strategy 3

Just a little bit better than the Needs Nurturing group as a whole, but not by much. And surprisingly, promotional content performed equally poorly across both At Risk and Churned. But how did more appropriate “winback” type messaging work with some of these At Risk andChurned users, again with AI-driven frequency and timing predictions?

Customer Segmentation Strategy 4

Customer Segmentation Strategy 5

Churned users converted 24x better! But more importantly, the time-sensitive At Risk customers converted 40x better. And At Risk users are notoriously difficult to win back once they completely churn. The 17% drop off in conversion rate between the two groups shown above is proof of that discrimination.

So how should you take advantage of this and build your customer segmentation strategy?

Run your customer base through a platform that does true data science. Use true user predictive scoring to target your 1:1 personalization based on where your customers are in their lifecycle.

You’re only a few steps away, and it’s easier than you think. E-Commerce companies constantly tell us that Cortex drastically improves their customer segmentation strategy compared to their current ESP. Looking at the numbers it’s simply a more profitable and elegant strategy.

Enter your email here and we’ll send your invitation to our upcoming demo!

“ReSci’s automated lifecycle marketing campaigns increased trial customer engagement and we saw 170% increase in customer conversions to paid memberships.”
-Sean Kane
COO, The Honest Company

“ReSci is the most trusted partner we have. We think of them as an extension of our data science and marketing teams. They saved me more than a day of prep work to create our retention emails each week and we’ve seen a consistent improvement to our conversion rates.”
-Natalie Doyne
Marketing Lead, Target

“We’re seeing an increase in conversion and customer retention as a direct result of our partnership with Retention Science.”
-Tamim Mourad