Customer engagement is an important indicator of marketing and value proposition performance. And it takes on increased urgency in a recessionary environment that demands businesses get more value from existing customers (especially given the cost of acquiring new ones). The challenge is how to effectively influence customer engagement. It takes an understanding of how current customers engage and what factors drives those behaviors; factors such as the businessís and its competitorsí actions, natural customer characteristics, and changing category dynamics to name a few.
Understanding Patterns in Customer Engagement is Key
Typically, most businesses try to grow an understanding of current customer engagement through static, point-in-time approaches. They tend to classify present customers in segments based on here-and-now behaviors as revealed by the information captured in their databases. Whatís more telling, however, is to dig into customer behavior longitudinally, to reveal the variations in engagement, the patterns that occur over time. Moreover, first impressions are critical, so understanding the behaviors of customers in the initial stages of their relationship with the brand are key predictors of their future engagement. To that end, thereís considerable value to be gained by studying the behaviors of specific, defined sets of customers Ė cohorts Ė rather than merely the current customer base.
Steps to Improving Customer Engagement
Kick-starting the process of diagnosing patterns and improving customer engagement takes a three-step approach:
Step 1: Diagnose engagement patterns.
Step 2: Understand the drivers of and identify opportunities to improve engagement.
Step 3: Start realizing results and develop a test and learn agenda.
Our experience shows us that the insights generated from Step 1 itself are extremely valuable and can have significant impact. For example:
Recently, an online retailer put Step 1 into play and realized that their customers fell into seven distinct patterns (see Graphic 1).
This not only helped them better understand current customer engagement but dispelled some long-standing myths. For example:
Because significant revenues were produced during the holidays, it was assumed that a large portion of its customer base shopped only during the holidays. Yet, pattern analysis showed these shoppers represented only 7 percent of the customer base, an important revelation which changed the way the retailer allocated efforts towards customers in non-holiday periods.
In a future post, I will provide details about each step in the process and share a white paper explaining more. In the meantime, for more information about customer engagement please refer to our client case study, Improving Brand Engagement or our blog post, Engagement Analytics: Can You Turn Customer Interaction Data Into Business Intelligence?, and if you would like more information about customer engagement pattern analytics, feel free to contact me at (781) 494-9989 x201 or firstname.lastname@example.org.