Title: "Optimizing Customer-Sales Executive Allotment based on Multifaceted Data"
Description: I have a complex task at hand where I need to allocate customers to the most appropriate sales executives. Both customer and sales executive data are a mix of categorical and numerical values, making this problem challenging. To achieve this, I need guidance on designing an effective approach.
Problem Details:
Customer Data: This dataset contains various attributes characterizing our customers. These attributes can include demographics, past purchase history, geographical location, RFM segment, industry, etc. We can discuss specific features in detail later.
Sales Executive Data: This dataset provides information about our sales executives, including their experience, history of the kind of customers they have worked with and other relevant attributes.
Objective: My objective is to devise a robust algorithm or model that can automate the process of assigning customers to sales executives, ensuring that each customer is matched with the most suitable sales executive based on a combination of their features and the experience of the sales executive.
I've tried clustering algorithm for the same. where i was clustering the customers with the most similarities and mapping that cluster to the most suitable a sales executive who has the most experience of working with those kind of customers. but the clusters were not relevant and there are multiple rows in the data for a managers since it's the managers history of customers.