use-case

Product Affinity and Customer Transaction Prediction

Objective

Most of the organisations deal with many potential customers for multiple set of products/transactions. With limited sales resources at their disposal, it is difficult to pursue each potential customer for all product transactions. Not only is it too costly and inefficient but also results in poor customer experience as the customers are bombarded with product recommendations that they are not interested in.

Our Solution

Organisations identify, predict and rank potential customers in order of their probability/likelihood to take a certain product or undertake a particular transaction. Episense builds a number of predictive models from Ensemble methods to Deep Neural Networks to predict in real time on actual field data to select the best working model for your case.

Benefits

This results in focused and optimised marketing efforts, with the sales and marketing team selling only those products to only those customers which they are most likely to buy. A major benefit of the resultant process is an even customer experience, better sales hit ratio and an increased brand value. Through time, we have perfected these models with feature engineering and hyperparameter tuning to achieve better results than the market’s current methods.

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