When businesses consider how to interact with a given customer, they have many choices of the next action to take. Identifying the best of these actions for each customer can drive improved customer satisfaction, ensure the maximum likelihood of a sale occurring and maximize the profit generated by this customer. This allows the company to respond directly to the customer instead of following a generic script and potentially alienating the customer.
Correctly identifying the best action to next take with a customer is key to ensuring high customer satisfaction and response to these actions.
Which action should next be taken.
Building the training data requires merging information from the CRM database to understand the current state of the customer and the sales activity for that customer to the marketing database to understand what offers the customer has seen, the frequency of the communication and the customer’s response to that contact. All this data needs to be cleaned, joined and transformed into valuable ML features before going into model training. This pre-modeling prep process can be frustrating and time consuming. We are here to help.
Build machine learning models to predict the segment as a function of the independent variables. Use model interpretability packages to evaluate the impact of the independent variables on the prediction.