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.
An Alco-Bev major wanted to understand the sizes of each of the categories of alcohol such as Whisky, Vodka and so on into the future for a plan strategy for a 5 year period.
The advantage of this methodology is that ROI of key digital channels can be compared and spends optimized in line with these insights, significantly improving overall efficiency.
The problem this CPG brand was faced with was a typical problem- Brand had presence across multiple connect points, paid media, owned media ( website, Facebook page etc) and earned media ( Twitter and other social mentions) but not knowing clearly what works across market clusters, and the interrelations between these connect points.