Transformers - Colloquy

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30 C O L L O Q U Y / Volume 18, Issue 1, 2010 A N A LY T I C S R E P O R T The Profitably Engaged Customer An analytic guide to measuring and leveraging the value of customer engagement WHEN IT COMES TO THE CUSTOMER-BRAND interactions known in the industry as “customer engagement,” many companies fixate on but one interaction: the purchase. This end-game engagement drives the companies’ ultimate metrics, including sales volume, market share, and ROI. Such concentration on bottom-line metrics, however, overlooks the power of customer engagements that lead to the purchase, as well as the posttransaction engagements that define customer lifetime value. Yet, engagement not only predicts those bottom-line metrics but also influences them. In our work with companies in several verticals, my team has shown that engagement drives increased customer value, B Y J O H N Y O U N G opening the gateway to greater sales, more significant share of wallet, and higher customer retention levels. And through their Relationship Chain methodology, my colleagues at COLLOQUY have demonstrated that opening this gateway is especially relevant to organizations with loyalty programs and their repertoire of multiple tools for communicating, interacting, and inviting involvement with their members. The impact of engagement is both measurable and actionable. By better understanding and increasing engagement, marketers can costeffectively drive lift in profit and customer retention, as well as foster other benefits. Employing tried-andtrue statistical techniques to correlate engagement with customer value creates a framework for assessing engagement’s influence and importance, and for designing initiatives and establishing budgets appropriate to those assessments. To best measure and leverage the power of engagement, consider employing this workflow: Identify your significant points of engagement. Inventory the various engagement levers that might matter to your business. These levers can be numerous and diverse. Common examples include: • Email permission/opt-ins and subsequent email activity, including open and click rates • Using a web-based optionpreference center • Reward redemption and other loyalty-program activity • Website activity, including visits, pages visited, information/application downloads, recency, and transactions • Direct-mail response • Call center interactions • Survey participation and results, including satisfaction and recommendation ratings • Social media interaction around the brand—including on-line communities and product rating/review sites • Referral activity As you see, engagement encompasses activity that can be measured both quantitatively and qualitatively through both online and offline channels—understanding, of course, that some activities are more directly or easily measured than others. Take stock of available customer engagement data. Identify the engagement data available from your company’s data sources, including the web, call centers, loyalty platforms, POS systems, surveys, social media platforms, and so on. Identify data gaps and decide what other data to acquire and, as appropriate, to not acquire. Exercise judgment and directional benefitcost analysis to decide what missing data is or is not necessary to your analysis. When taking stock, note that though engagement typically carries a positive connotation, to support comprehensive decision-making you must also gather and measure instances of negative engagement, including email unsubscribes, complaints, uncomplimentary product reviews, product returns, and so on. Build the quantitative framework. To begin linking engagement and return, build a baseline customer value model to gauge customer revenue potential and set marketinginvestment levels if such a model isn’t already in place. Value models— typically regression-based—can predict customer spending levels over a future time period which may vary, though one year is common. Value models usually comprise two stages. The stage-one model uses logistic regression to estimate the likelihood that customers will purchase from the company over the future time period. The stage-two
model uses linear regression to estimate the amount of spending over that period. Both model scores are then combined or multiplied to create a score that represents the customers’ expected spending with the company. For example, if customer A has modeled likelihood to purchase of 0.5 and modeled spending of $200, then her expected spending (or customer value) is $100. Value models draw their inputs largely from prior purchase behavior— various measures of Recency, Frequency and Monetary (RFM). These data are usually available from your company’s marketing database or analytic data mart. In addition, demographic data not already available in the database is easily overlaid on customer records and can incrementally improve value model scores. Overlay the engagement measurements. To the baseline customer value model, add engagement measures to quantify their impact on customer value as additional predictors of customer spending. Having controlled or accounted for the other determinants of customer value in the baseline model, the expanded model will measure customer-value sensitivity to changes in various engagement levers. For example, overlaying website visitation frequency over the baseline customer model allows you to correlate visit rates with customer value, and quantify the value of various levels of web engagement. Such correlation is analogous to the concept of elasticity of demand, which links price changes to consumption changes. Apply the engagement learnings. Couple relevant cost data with engagement value measures to calculate return on engagement investment (ROEI). This intelligence helps you more wisely allocate marketing dollars to increasing customer engagement by answering A Measure of Engagement A case study A large hotel chain with a well-established loyalty program was struggling to maintain market share. Internal data also indicated that the organization was not faring as well as desired on engagement measures such as reward redemption, email penetration, and online activity. The company wanted to gain a more comprehensive understanding of how well it was engaging with members and the importance of engagement to its business results. Engagement data was combined with member spending data to examine the correlation between various engagement metrics and member spending. An engagement score—which measures the strength or depth of engagement—was constructed. Engagement scores, when plotted against future spending, clearly showed the effect of increasing engagement levels. The results of the engagement modeling, as shown on the display easel to your right with average spending increasing on the y-axis, and engagement index increasing on the x-axis, revealed these engagement drivers, in descending order of influence: • Breadth of brand stays • Award redemption • Online activity • Booking or adjusting a stay online • Completing a post-stay or call-center survey • Selection of a preferred points-earning partner • Granting email permission • Clicking on a received email The relative influence or importance of the engagement drivers gave the organization information it needed to begin to develop marketing strategies and tactics to cost-effectively increase customer engagement. —John Young such practical questions as how much to spend to encourage members to redeem rewards or to set their preferences in the customer preference center. And you can correlate engagement activities with other profitable engagements. As an example, if program redeemers typically also subscribe to your email newsletter, and both groups show strong ROEI, you can target email subscribers who have not yet redeemed. Future engagement The engagement scorecard is an excellent tool for tracking engagement over time, and its view into the relative importance of engagement levers enables you to adjust strategies if the levers change. For example, declines in low-impact engagement activities may be safely ignored, while declines in highimpact activities warrant immediate attention. The models also allow you to define what levels of change should trigger your attention. With the data in place, you can also monitor engagement trends at an individual level, by segmenting customer groups based on their overall scorecarded engagement levels. Typically four engagement segments are employed—for instance, Low, Moderate, High, Very High. Engagement trend data augments existing attrition-warning systems, enabling you to identify customers migrating between the engagement segments and intervene when necessary—with high-value customers exhibiting declining engagement, for example. Similarly, you can identify and subsequently acknowl edge and reward customers exhibiting increasing engagement levels. Continuously revisit engagement and adjust your strategies as needed. By measuring and acting on the value of all elements of engagement, you enrich and encourage the cycle of customer interactions, helping you reach that bottom-line engagement— the purchase transaction—more efficiently, and repeatedly. John Young is Senior Vice President, Strategic & Analytic Consulting Group, Epsilon. For information on COLLOQUY’s Relationship Chain model, visit www.colloquy.com/engagement www.colloquy.com 31
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