Developing product recommendation platform for customers.
The client wanted to implement an effective mechanism that could assess the nature of individual customers and recommend them the right products, for effective cross-selling and up-selling.
Situation: Lack of accuracy with the current model.
The current model was not proving effective when it came to the accuracy of predictions at an individual product level. The client wanted to implement a much more robust mechanism.
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Big data analytics for clustering store level data and developing actionable insights.
We segmented and clustered the stores based on various factors including demographics, location, categories etc., to develop effective cross-sell and up-sell recommendations. Our robust algorithm was capable of determining the correlation between different products based on customer behavior.
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Improvement in cross-selling and up-selling opportunities resulting in better profitability.
Our solution helped the client in implementing an improved product recommendation platform. This helped them in automating the process of product recommendation to the customers and improving the chances for cross-selling and up-selling.