We recommended dealership segmentation in order to cluster dealers on the basis of the Dealer Potential Index. We adopted the followed 5-step approach:
Step 1 – Derived relevant parameters for dealership clustering using DR salesforce data. Parameters included calls/visit frequency, productive call rate, journey plan – distance between two calls per day, leave management system, salary, employment tenure, past history of target achievement, dealer to DR mapping, etc.
Step 2 – Derived parameters for dealership clustering using dealer’s transaction data. We assessed parameters including purchase history by-product lines, discounts, inventory stock-out period, propensity to adopt new launches in the past, average size of assortment carried, dealer feedback surveys, post-sale servicing records, locality profile of dealer’s outlet, popular vs. premium assortment ratio, etc.
Step 3 – Used K-means clustering to arrive at 6 clusters across dealerships which were differentiated based on % of sales via premium products, minimum discount slabs, locality profile, and number of DR visits per month (servicing frequency)
Step 4 – Determined Dealer Potential (Target) Sales Index based on scenarios across key sales influencing triggers at each dealer level – for each cluster. We built these scenarios in consultation with the client’s strategy and business leads
Step 5 – Determined the number of DRs and visits per DR based on dealer-wise sales targets. Our client used the results of this analysis for salesforce deployment and to design an incentive plan for the sales team
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