The salesmen of a large consumer product company were facing problems. They dealt with hundreds of SKUs, and found it difficult to scroll through hundreds of SKUs on their handheld devices, and then sell additional SKUs from the same list. RainMan was commissioned to identify a list of SKUs for each store with a high probability of purchase. The objective was to predict all the sales within a certain number, say 30, if a store typically purchased 15 SKUs.
RainMan used a probability scoring method to list SKUs from high probability to low probability for each store. The model also eliminated SKUs with a low probability of being ordered. This allowed salesmen to focus on relatively few but high probability SKUs and get better conversions.
On validating the model, the algorithm showed a high predictive score -out of the SKUs bought, 90% were correctly predicted. Enabling better cross-sells and up-sells resulted in a significant lift for the additional number of new SKUs sold, which in turn had a high correlation with the improved morale of the sales force.
The results were that over 90% of the sales calls were successful and of this about 70% picked the product that was specified for that specific client. This led to an overall improvement of sales of close to 20% and as the costs were the same as the earlier period, the additional margin was added to the bottom line.
Promotions are an important part of consumer goods marketing. The client - a consumer goods major needed to identify the most effective consumer promotions both value promotions (price discounts offered), and volume promotions (free gifts) from the many it had deployed, so that a result bound promotion strategy could be drafted.
Pricing is one of the key decision for a brand or a retailer. A number of factors need to be taken into account while setting the price. One key resource available to the marketer is past data of sales at a daily level (from own showrooms), or weekly data from Multi-Brand Outlets (MBOs).