Enhancing Customer Shopping Experience

Summary

The Retail client wanted to improve the accuracy of the product recommendations being made to the customers on its ecommerce platform which had a direct impact on its customer engagement and sales metrics. The recommendations required to match the changing customer preferences and the run time to process huge volume of data had to be reduced

Approach

Evaluated past purchase behaviour of every customer through store, website POS and clickstream data and created a customer genome profile with knowledge accumulating with every interaction and activity
AI Picker engine selected the best performing algorithm for each customer based on context and data available from the pool of algorithms (session based, Hybrid filtering and deep learning). Used Multi Bandit Approach each favouring exploitation over exploration to different degrees to roll out recommendations
Filtered and aligned the products recommendation list to business goals and rules by increasing or lowering the weights of products

Key Benefits

At granular category cohort level, the accuracy for Millennials were higher in Electronics and Gadget whereas accuracy was higher in everyday living category for customers with larger household.
Reduced the frequent grocery related and low-cost product recommendations with relevant and diverse recommendations
Tredence helped bridge the gap between stakeholders ensuring multiple integrations and adoption by providing right solutions and processes through consultative problem solving

Results

The product personalizer resulted in 60% increase in purchase rates for both initial and returning sessions.

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Tredence helped a large format retailer to enhance its customer shopping experience on ecommerce portal with personalized product recommendations.

Summary

Approach

Key Benefits

Results

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