AI use cases WildFire implemented in retail

Client: Small online retailer

Problem: A small pharma company wanted to know their customers better. The following were their goals.
– Understand customers better
– Increase upsell

Our solution: We worked with their teams to collect all the customer data. We built machine learning classification models which helped segment their customers based on their spending habits.

Results: These models helped their marketing teams to better focus on the different upsell strategies and new offers to their customers.

Client: Medium size online retailer


Problem: An online retailer was looking to optimize their pricing and promo. They had few different goals.
– Maximizing the profit
– Maximizing brand exposure
– Increasing market share

Our solution: We preprocessed and structured a very large amount of customer and product data. We build 3 different models for the 3 goals above using gradient descent. The models were optimized for increased accuracy using additional data.

Results:The retailer was able to run different campaigns for maximizing profit, brand exposure and increased market share.


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