Enhancing E-commerce Experiences

How we used AI to transform customer experiences for a leading U.S. Grocery Retailer

Our client is one of United States’ largest grocery retailers, serving more than 11
million customers daily in stores and online. With 2,800 stores in 35 states, our
client operates two dozen grocery retail brands, 34 manufacturing locations,
and 44 distribution facilities, totaling over $140 billion in annual revenue.

Key Takeaways

Analyzed terabytes of customer data and shopping behaviors to build an AI recommendation model.
Implemented distributed data pipelines for ETL, enrichment and prediction in Azure cloud.
Delivered measurable ROI in terms of increase in basket size with suggested products.
Built model training and tuning with user-generated signals and feedback.

The Challenge

Our client observed a common issue among customers:difficulty finding products matching their preferences.Consequently, customer interaction and interest decreased, resulting in reduced engagement, lower loyalty, and a decline in repeat business. This inadequate search experience led to customer frustration, dissatisfaction, and potential loss of sales. Additionally, they faced the challenge of fully grasping the vast amounts of data they possessed, like customer, transaction, and behavioral data, to generate better insights and enhance convenience for each customer across all channels.

The Solution

While our client had some internal resources dedicated to this objective, they recognized the need for industry experts + to get the job done. Rapidops has deep experience in designing and delivering advanced data analytics and AI/ML solutions at an enterprise scale, and the client felt strongly that this partnership would lead to the initiative's success. At Rapidops, we believe any AI/ML initiative's success strongly depends on the available data's quality, depth, and breadth. That's why we started by understanding our client's business goals, customer needs, and available data to determine the data quality and design data cleaning operations.

Within a span of four weeks, we were able to design data cleaning operations as well as a machine learning engine that delivered real-time recommendations and personalized upsell and cross-sell suggestions to millions of customers utilizing the client's web or mobile apps. Each time a customer interacted with the client's products, it sent feedback to the engine, continuously improving the performance and accuracy of the recommendations and shopping experience.