Customer Segmentation

Modern Customer Segmentation Tactics to Enable Personalization and Supercharge Marketing ROI

The digital landscape is constantly evolving and – now more than ever – it’s critical for marketers to hone their approaches to both identifying their most valuable customers and personalizing the customer experience.

Gartner estimates that 80% of any brands future revenue will come from just 20% of their existing customers. Further, their analysts have predicted that by 2018, companies that have fully committed to personalization will outsell companies that haven’t embraced personalization technology by 20%.

Smart Segmentation

Consumers are increasingly expecting a personalized experience from any brand they engage with. They know that every marketer has access to data that details their individual behaviors and preferences, yet many marketers have not translated that knowledge into a better experience. This starts with smarter segmentation and research shows that more than 40% of marketers feel that they have not improved upon their customer segmentation strategies to keep up with evolving customer expectations.

A customer segment is a group of people who share quantifiable attributes. Traditionally marketers have used RFM modeling to identify their best customers, but Recency, Frequency and Monetary Value are just three of a multitude of behavioral signals that marketers need to consider when segmenting customers. The most savvy marketers are creating customer segments that combine both explicit behaviors and implicit interests.

Explicit behavioral data is the known, quantitative individual attributes of a given customer based on user actions and purchases. Examples of explicit behavioral data points tied to a single customer might include: only purchases items on sale; follows you on Twitter; browses items on your mobile app; opens email at 3PM and 10PM; acquired via Google AdWords; accesses email via Android smartphone and iOS tablet. All of these attributes, and a seemingly infinite amount more, are demonstrated through their unique interactions with your brand, and help construct the story about each customer.

Implicit interest data is the inferred, qualitative individual attributes of a given customer based on browsing and purchase activity. Examples of implicit interest data: likes green; prefers cashmere over cotton; interested in Frye boots; prefers tote handbags to hobo handbags; enjoys DIY home improvement content. These attributes, also collected during the data mining process with sophisticated platforms like Sailthru, yield extremely meaningful insights into your individual customers is and what they want from your brand beyond what their explicit behaviors have indicated.

The combination of these two forms of data will allow you to develop customer segments that enable the delivery of marketing messaging that are increasingly relevant to individual consumers.

Behavioral Predictions

Marketers are now discovering how they can use behavioral predictions to create more effective customer segmentation through microsegment targeting.

This approach uses predictive analysis of specific user groups, or “microsegments,” to target marketing messages more precisely. Sailthru Sightlines, a new behavioral predictions technology, lets marketers pinpoint which customers are likely to buy, how much they will spend, whether they are likely to opt-out and to then tailor their marketing accordingly. By using these predictions as another dimension to customer segmentation marketers can more effectively identify top customers on a daily basis and those who should be suppressed from marketing messages. One client who recently implemented the technology saw email purchase conversion grow by 25% after discovering that the top 5% of customers spent 200% more than the next 5% (so the bottom half of the top 10%).

Find out more about how behavioral predictions can drive better segmentation in our Predictive Intelligence guide.