Interpreting Customer Life Cycles to Increase Lifetime Value
Customer interactions and insights are extremely valuable when it comes to a brand's engagement and decision-making. What if brands could leverage these insights to track sentiment, feedback, influencers, and trends in real time, and create responses that have a direct impact on customer lifetime value?
The last year brought increasing references to customer life cycle management and the analytics that would drive it. This expanded view has provided a framework for capabilities that transcend the transactional, sales funnel orientation of CRM systems. Tools and metrics can now effectively address every aspect of a company's relationship with its prospects and existing customers.
Within this broader context are three important indices that enable us to interpret and manage customer life cycle for existing customers and, as a result, lifetime value.
The customer engagement (CE) metric has typically been viewed within the context of the traditional marketing funnel. Earlier definitions of CE were presented as an estimate of the degree and depth of visitor interaction against a clearly defined set of goals.
Taking the broader, more inclusive view of customer life cycle management, CE can be interpreted as the relative measure of customer interest, baselined against the brand's minimum stated goals for engagement.
Raw data identifying a customer's engagement online is readily available as interpretations of content, form, and context. While this combination can be leveraged right away, the future will evolve to include multiple sources of offline interactions, like conversations on email or phone, location intelligence, and point of sale interaction history.
Engagement indices are a function of minimum thresholds achieved in online activity, measured for three things: session (duration index), visits (loyalty index), and specified actions (participation index).
Referred to as "listening tools" in early stages of evolution, sentiment analysis engines were initially used to make sense of user sentiments within the enormity of content on social media. In their current state, these tools leverage semantic orientation and textual polarity to identify user sentiments The current crop of generic sentiment analysis engines provide an accuracy rate of anywhere between 30 percent and 50 percent sentiment recall.
Some of the notable sentiment analysis engines are investing hugely in semantics research with the goal of improving accuracy.
In the context of managing customer life cycle, sentiment analysis could be tuned to brand-specific polarity and orientation of semantics to improve accuracy. A customer sentiment index, in this context, could be a function of textual phrase polarity to identify positive, negative, querying, or neutral sentiments within an interaction as well as weights assigned to recognize the overall sentiment of an interaction.
Even with the current standards of accuracy, customer sentiment indices can offer valuable assessments of customer relationships with a brand at a given point in time as a moving average.
Renewal predictability of existing customers is based on descriptive assessments of confidence from customer account management. It has been a combination of profiling customer experiences, or has been dependent on objective measures like successfully resolved support/service calls. Analyzing experiences after they have occurred, as in the case of resolved calls, though, leaves little or no room for correction.
Renewal revenues often represent 60 percent to 70 percent of a company's sales and revenue goals. Bringing predictability to this assessment could make a huge difference.
Renewal predictability indices can provide a higher accuracy of renewal predictability on a moving time scale. They provide executive management with insights that could help mitigate risks of underperformance in projected revenues.
While such indices do not replace human interactions and a brand's efforts to engage their customers, they could facilitate trendspotting and predictive analysis that would be standardized to provide quantitative support for descriptive analysis as well as real-time information for proactive decision-making.
In the end, capturing, understanding, and, most importantly, acting upon these indices will help businesses obtain a more comprehensive view of the complete customer life cycle, and better measure and impact all aspects of their relationships with their customers.
Mahendra Penumathsa is the cofounder, president, and chief operating officer of Collabor, a Boston-based product company that innovates and builds online collaboration solutions on Web and mobile. Prior to founding Collabor, he held executive positions at Ion Exchange, JS Media at Mumbai, Franklin Templeton, CKR Associates, and i-Vantage.
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