Challenge & Pain Points:
Data-driven decision-making and execution are key to the underwriter’s success. In today’s world, the models driving the execution of the business must be dynamic and based on basic units. Execution models towards decreasing costs and increasing revenue, should be built upon these units. Unit economics is the discipline of measuring profitability on a per unit basis to facilitate this approach and to achieve profitable scale up. It is rooted in the in-depth understanding of the units, which are the customers in the retail insurance business. A practical approach is the accurate estimation of Customer Lifetime Value (CLV) and Customer Acquisition Cost (CAC), as well as their covariance.
Traditional actuarial approaches treat profit as a pure function of cost of risk and a simple margin over cost which are almost always modeled for single products and a single coverage period. However, earnings generated by a customer are neither limited to a product nor to a single policy term. Customers may cancel policies and churn, or renew and even buy other products over a sustained period of time spanning multiple years, which are possibilities overlooked by the traditional actuary. Hence, profitability per customer is a function of the ratio of CLV to CAC. The higher the ratio is, the higher the profitability. Including various operating costs, such as retention, maintenance, etc., in the picture will give an analyst or decision maker the CLV/CAC ratio threshold for smart decision making. At the end of the day, existing customers with subthreshold ratio, as well as missed (or churned) customers with high ratio will return as a loss, either through wrong acquisitions or missed opportunities, respectively.
Insursight.ai Solution:
These underlying mechanics of unit economics make it a perfect subject matter for AI. Both CLV and CAC need to be predicted. Who is likely to buy what (acquisition, up-sell, cross-sell), where (distribution channels) and how? Who is likely to churn or renew and when (average lifetime)? And if possible, what is the expected cost of retaining a customer belonging to a specific segment? Is it worth it?
Traditional approaches are based on simple statistics of past data. The aggregate model uses average past revenue per customer to come up with individual CLV estimates, overlooking inter-customer covariance. The cohort model attempts to address this weakness of the aggregate model by manually defining cohorts, providing single CLV estimates for each cohort, skipping per customer assessment. In fact, there are inter-customer covariates that can provide invaluable insight into personal traits; such covariates are just not evident at first sight, hence are impossible capture by manual rule-based groupings. That’s where AI gets into play. Modern AI approaches utilize high-dimensional predictive models fit to whole past data, as well as external factors such as seasonal dependence, stock-market indices, etc., to predict individualized CLV and even CAC. State-of-the-art learning strategies even allow us to push the limits of what can be done with available data. Unlike early predictive models such as GLM or models based on specific probability distributions, these models are capable of learning not only the nonlinear relations between covariates but also the covariates themselves and/or complex, non-parametric probabilistic models.