Cross-sell recommender system generates over $1 million per quarter

For a global food company, LatentView Analytics’ innovative recommendation engine which combined customer segmentation, user-based collaborative filtering and market basket analysis generated more than $1 million per quarter for one of their offline channels, resulting in higher customer satisfaction due to precise recommendations.

Business challenge
The client was facing challenges as they were utilizing their sales team’s capacity to the maximum; as a result, they were selling based on a narrow analysis of previous transactions or ‘gut’ feel. The client chose LatentView Analytics to build a tool that would help increase repeat orders and manage the large data volumes, thus helping them be agile.

Solution
By deploying rule-based segmentation, LatentView Analytics grouped customers based on their segment type and location. Different predictive models were built and tested on each of the customer segments before finalizing on a combination of collaborative filtering and market basket analysis models for better predictions.

Benefits
With the success of this initiative, the client was more confident in developing and deploying enterprise scale analytics solutions to accrue tangible benefits for the business. A successful implementation of a full-blown recommendation system paved the way for more advanced analytics applications for the enterprise.

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