case

Use A.I. for lead qualification and product portfolio development

Background

Our client is active in business-to-business sales of products and services to Small and Medium Enterprises (SME). More than 10.000 Stock Keeping Units (SKUs) are offered and sold in a cross-channel sales mix that consists of sales representatives, telesales and online sales.

Objectives of the cooperation

Lead qualification had always been the task of a sales representative and our client wanted to invest in A.I. technology. Therefore we investigated the historic database and built a machine learning algorithm that takes the available lead data as input for predicting and qualifying leads.
Next to the lead qualification at individual SME level, we also developed an algorithm using recommendation engines to determine an ideal product portfolio. This “ideal” shopping basket was used to offer personalised leads.

Results

As a result, the lead to customer ratio improved systematically and the customer lifetime value was increased by more than 20%.

 

Technology Data wrangling I Machine learning I Python I Cluster analysis

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