Enhancing E-commerce Experiences

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.

5%

Increase in #
of orders

0.2x

Increase in customer
loyalty

16+

Product categories
analyzed

0PB+

Customer and transaction data analyzed

Key
Takeaways

  • Processed terabytes of customer data and shopping behaviors to construct an AI recommendation model.
  • Developed distributed data pipelines on Azure Cloud for ETL, enrichment, and prediction.
  • Achieved a measurable ROI by boosting basket size through suggested products.
  • Created model training and tuning mechanisms using user-generated signals and feedback.
Power BI insights
Power BI insights
Power BI insights
Power BI insights
Power BI insights

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.

Solution

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.

  1. Personalized Recommendations

    After cleaning and analyzing petabytes of data, like order history and purchasing behavior, Rapidops employed advanced algorithms like collaborative and content-based filtering to develop a personalized recommendations engine that met customers’ unique preferences and needs.

    personalized_recommendations
    personalized_recommendations
    personalized_recommendations
  2. Curated Product Listings

  3. Dynamic Search Results

  4. Inventory Predictions

How we
did it

Analytics & AI/ML

  • Real time data analysis
  • Predictive analytics
  • Deep learning
  • Neural Networks
  • 2D & 3D Mapping
  • Generative Adversarial Networks

Strategy

  • Product Strategy
  • Data Strategy
  • Analytics & AI/ML Strategy
  • Roadmapping

Technologies

  • PySpark
  • Solr
  • Cockroach
  • Docker
  • Kubernetes
  • Python
  • FastAPI
  • Spacy
  • NLTK
  • Gensim
  • Pandas
  • PyTorch
  • Requests

Engineering

  • Native mobile on IOS & Android
  • Web app development
  • Automation framework & manual QA
  • REST API backend
  • Front end development
  • ADA Accessible UI
  • IoT development with Raspberry Pi