The customer is a partnership organization between two automobile dealerships and one of the leading US automobile manufacturing companies. The company focuses on using the data collected from past enrollents to provide analytics-driven value add for the parent organization.
The customer reached out to Brillio to reap the benefits of our experience in analytics and the digitization space to execute dealer’s lifetime value estimation exercise. This aided in classifying high lifetime value and successfully strategized steps to shift dealers from low value to high value segments.
The objectivewas to develop a machine-learning based solution to identify the $ lifetime value for every dealer and to strategize how to move dealers from low value segments to high value bucket.
In assignments of this nature, the key challenge lies in gathering the right data and deciding on a precise method to compute the objective metrics, in this case, dealer lifetime value.
There were ~2000 dealers and yearly enrolment data was taken for a history of ~20 years. Another challenge was to sift through an overwhelming mass of data and information to find a practical answer to a hitherto intractable problem.
Step 1 Calculation of Adoption Rate
From the legacy enrolments data available we calculated net enrolment over the years , then estimated adoption rate for each dealer.
Step 2 Forecasting & Extrapolating
From the data available we extrapolated the data for next 5 years and projected to find the incremental value for each dealer.
Step 3 Bucketing
Using the diffusion of innovation theory we created three adoption buckets (Low, Medium, High) on the basis of declining average adoption rates per dealer over the years in the ratio of (30%-40%-30%) respectively, then to find the $ lifetime value we completed revenue extrapolation.
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