Industry Perspectives: Audience Segmentation for Engaging High Value Customers

The Industry Perspectives series from Loyalty360 gathers insights from leaders in customer loyalty technology and consulting to give you a bird’s eye view of what is happening today and the best practices that will prepare you for tomorrow. 

In this edition we bring you best practices for segmenting high value customers and engaging this audience. These insights from industry leaders illustrate the growing demand for tailored customer experiences and the importance of leveraging data to better understand your best customer.

Contributors:

 
Within customer loyalty programs, understanding your most valuable customers is more important than ever. Segmentation is one approach that allows brands to differentiate between customer groups, including those considered high-value, and tailor their loyalty efforts accordingly. By identifying these key segments, businesses can offer more relevant rewards, personalized communications, and a deeper connection with their audience.

As customer behaviors and preferences become increasingly complex, segmentation is evolving, incorporating a wider range of data and engagement metrics to help brands stay relevant and drive increased lifetime value. In this article, we explore the methods being used to segment high-value customers and delve into the insights from leading experts in the field on how to do it effectively.

Leveraging Data to Identify High-Value Customers
Identifying high-value customers starts with understanding their behaviors and transactional patterns. These customers are often the most engaged and profitable but recognizing them requires analyzing a wide array of data points. Emily Merkle, SVP of Analytics & Data Science at Phaedon, explains how segmentation relies on analyzing both transactional and engagement data: “Identifying and segmenting high-value customers requires analyzing both transactional data and engagement patterns across multiple metrics. In the travel sector, key indicators include booking frequency, average ticket value, ancillary purchases, and booking lead times, while retail environments focus on annual spend, purchase frequency, basket size, and category diversity.”

Transactional data provides crucial insights into customer behavior—whether they’re frequent shoppers, high spenders, or simply loyal to a particular product category. By examining this data, businesses can identify customer segments that are most likely to generate sustained revenue. Merkle also highlights that predictive models, which combine transactional and engagement data, offer even greater accuracy when identifying high-value customers, enabling businesses to target those who have the potential to generate long-term loyalty.

For Melissa Pringle, VP of Strategy at Ansira, segmentation takes a practical approach, particularly through the use of the RFM (recency, frequency, and monetary value) model. "At Ansira, the most common approach we take to segmentation is using an RFM model that looks at the number of transactions, average transaction value, reward engagement or reward cost, and program duration," she explains.

The RFM model not only helps businesses identify high-value customers but also reveals opportunities for re-engaging customers who may have fallen off the radar. By tracking recency, frequency, and monetary value, businesses can identify customers who may be at risk of churn or those who may need an extra nudge to increase their spending.

Kristen Schrenkeisen, Lead Loyalty Strategist at Bounteous, also points to Member Lifetime Value (LTV) as a key metric for segmentation. “Member lifetime value (LTV) is one of our preferred ways across industries to identify which customers are not only valuable near-term to a brand, but have staying power as brand loyalists,” she says. LTV is an especially powerful metric as it helps brands understand the total revenue a customer is likely to generate over the course of their relationship, allowing businesses to prioritize those who are most likely to continue to contribute value in the years to come.

Real-Time Data and Dynamic Segmentation
As businesses become increasingly data-driven, real-time data is playing a pivotal role in segmenting and engaging high-value customers. The ability to analyze and act upon customer behaviors in real-time opens up new opportunities for personalization and immediate engagement. Kelli Hobbs, VP of Business Development at Valuedynamx, shares how real-time insights enhance segmentation: “Real-time data allows brands to anticipate and respond to customer needs instantaneously, creating seamless and memorable experiences. Real-time insights allow brands to proactively offer exclusive rewards or personalized incentives, ensuring meaningful customer engagement.”

Real-time data enables businesses to respond immediately to customer actions—such as an in-store purchase or website visit—tailoring the experience on the spot. For example, a retailer might send a personalized offer to a high-value customer who has recently browsed a product category or made a large purchase. These real-time interactions can increase customer satisfaction and drive immediate behavior, such as additional purchases or program engagement.

Len Covello, Chief Technology Officer at Engage People, advocates for the use of AI and machine learning to uncover deeper customer insights in real time. “Advanced segmentation techniques leverage AI and machine learning to uncover unique customer behaviors and preferences,” he says.  By applying machine learning algorithms to customer data, businesses can uncover subtle trends and behaviors that inform dynamic segmentation strategies. These AI-powered insights enable brands to constantly refine and adjust their loyalty programs, ensuring that high-value customers receive the most relevant and timely offers.

Behavioral Segmentation: Focusing on Specific Customer Actions
Beyond transactional data, behavioral segmentation focuses on the specific actions that customers take—whether it's engaging with content, redeeming rewards, or referring others. These behaviors are often more telling than traditional demographic information because they reveal a customer's true level of engagement with the brand.

Pringle explains how Ansira uses behavioral data to refine its segmentation: “We also create additional segments to identify high-value customers which allow us to customize messaging, offers, and content aligned with the goals and ideal behaviors we deem most impactful for the business.” By analyzing behaviors such as reward engagement, social media interactions, or purchase patterns, businesses can identify which customers are most engaged with the brand and target them with tailored offers. This behavioral approach allows brands to move beyond broad demographic segmentation and focus on the unique actions that make a customer valuable.

Schrenkeisen agrees that behavior-driven segmentation is essential, particularly when it comes to measuring customer engagement over time. “Retention, LTV growth, and brand advocacy are the three metrics that are most indicative of success amongst high-value loyalty members,” she says. Retention, for instance, reflects whether customers continue to engage with the brand over time, while LTV growth shows whether a customer’s value increases as they remain loyal. Advocacy, or the likelihood that a customer will refer others to the brand, provides another crucial measure of customer loyalty.

Segmenting Based on Emotional Loyalty and Advocacy
Segmenting customers based on emotional loyalty adds another layer of depth to traditional segmentation strategies. Emotional loyalty goes beyond transactional behaviors to focus on the feelings customers have toward a brand. By measuring emotional connection, brands can foster long-lasting relationships that drive both engagement and advocacy.

Schrenkeisen shares how feedback and emotional loyalty help refine segmentation efforts: “Brands can leverage their VIP customer feedback to enhance their programs and make adjustments that retain existing VIP customers, but they can also take it a step further. VIP customer feedback aids in measuring emotional loyalty and tailoring benefits across tiers that maximize the emotional connection to a brand.”

Kelli Hobbs echoes this sentiment, stating, “Customer feedback and sentiment analysis provide valuable insights for loyalty program refinement. By analyzing social media mentions, survey responses, and direct feedback, brands can identify pain points and opportunities for improvement.”  This valuable data allows businesses to refine their segmentation strategies, ensuring that high-value customers are not only engaged but also emotionally invested in the brand.

Effective segmentation is a powerful strategic tool for successful loyalty programs. By utilizing transactional data, behavioral insights, and real-time data, businesses can identify high-value customers and engage them in ways that are personalized and meaningful. As the experts in the industry continue to innovate, the combination of advanced segmentation techniques, AI-driven insights, and emotional loyalty strategies will ensure that brands can maintain long-term, profitable relationships with their best customers.