#LIVE Sales Intelligence dashboard : http://www.sales360.us
#Background: In a Business to Business or Business to Consumer ecommerce world, Customer is a business or individual. Typically sales team doesn't have insight about whether customer who is requesting quote is going to purchase or not. Also there is a great deal of uncertainity around up-selling causing sales team to oversell or cause customer churn. This problem can be addressed by using Machine learning and Recommendation systems.
#Goals: Build sales intelligence platform to provide real-time conversion score on each quote and recommend relevant services that customer may buy. Know your customer well.
#Solution: High level Solution is to build a predictive model on the features from customer quotes and also enrich the feature space from facebook and twitter scoring systems. Based on the prediction now run through and generate the recommendations on the services that customer may buy like the coverage options ( A, B, C, D , E and F)
#Data Sources: Insurance quotes publicly available from all state. Facebook Graph API and Twitter API
#Results: Achieved an Accuracy of 0.86 and AUC is 0.79 which is 29% better than non-machine learning Model. Currently i do not have a way to validate recommendations. The good approach would be to monitor the sales growth related to the recommended services explicitly to that customer. May need to tweak little bit based on the results.