Customer Value Analytics is the interplay of data, technology, statistics and business processes, focused on improving the top line. It embeds analytics into an organization’s DNA, using data science to probe customer response, understand buying/user context, and using this insight to deliver increased customer and company value. With the advent of Big Data, Analytics now provides an opportunity to “own” the company’s relationship with the customer, from communications to product development and sales. Our successful clients use analytics to understand how well they generate demand and the quality of the customer experience they provide.
One of our customers, a leading insurer had a challenge. 43% + of its millions of customers who purchased policies in previous years had allowed them to lapse. It was not easy to sell the policy again to the same customers. Customer acquisition cost is significantly higher than the customer retention cost in general, and it was impacting the bottom-line of the client significantly. Our client wanted to understand customer behavior, identify at-risk customers and come up with early intervention strategies to retain the customers.
Expedien team helped the client by building a “Customer Analytics” solution that helped the insurer identifying its customers, who were most at risk of canceling their policies, or not renewing, and also insights into why.
Expedien developed a “Customer Analytics” solution integrating customer, policy, claims, social media, call center/agent interaction data for 6+ million of the insurer’s customers to identify which agent and customer behaviors signaled a high risk for policy lapse. We used historical data, and current data to predict the customers by segments such as age, income, ethnicity, geography, gender and so on, who may be at a high risk of lapsing or canceling. Among the insights, the analytics revealed that least interaction with an agent and no or very few medical examination indicate a substantial higher risk of lapsing. Another insight was that the customers who insurer didn’t update on policy benefits periodically were at higher risk of canceling or lapsing the policy.
Once at-risk customers were identified, Insurer came up with an early intervention strategy, and developed specialized scripts and incentives to use in interactions with these policyholders. Customer Analytics also helped insurer’s agents understand a prospect’s risk level from the beginning of the sales process by comparing their profiles to those of existing customers and came up with proactive intervention accordingly.
Outcome: 14% increase in premium revenues in one year, 6% of customers lost in the previous year were recovered, 140% return on investment within first year, Retention of 18% more customers than previous years anticipated next year.