Overview
One of the United States' largest grocery retailers, operating 2,800 stores across 35 states and generating over $140 billion in annual revenue, faced a critical challenge in its digital transformation journey.
Customers struggled to find products that matched their preferences, leading to decreased engagement, lower loyalty, and declining repeat business. With vast amounts of customer, transaction, and behavioral data, they needed a solution to harness this information effectively and create personalized shopping experiences across all channels. The retailer partnered with Rapidops to leverage our expertise in advanced data analytics and AI/ML solutions at an enterprise scale.
Our mission was clear: transform their massive data reserves into actionable insights to enhance customer convenience and drive engagement across all touchpoints.
Services
We implemented a comprehensive AI-powered solution that revolutionized the shopping experience. Our approach began with thorough data-cleaning operations and the development of a sophisticated machine-learning engine.
Within just four weeks, we built a personalized recommendations engine using collaborative and content-based filtering, analyzing petabytes of order history and purchasing behavior data. The system provided real-time product recommendations across more than 30 categories, featuring dynamic suggestions like "Most Popular in this Category" and "Trending in this Category."
We enhanced the search experience using natural language processing and machine learning, eliminating zero-result pages and implementing smart autocomplete functionality. Additionally, we developed an inventory prediction system using machine learning that incorporated multiple data sources, including pick events, reservations, product velocity, seasonality, and supply chain trends.
Core Highlights
How We Did It
In just four weeks, we built a data-cleaning pipeline and a machine learning engine that delivers real-time, personalized upsell and cross-sell recommendations to millions of users on the client’s web and mobile apps. Below is a detailed breakdown of the process:
1. Personalized Recommendations
Impact
Our cross-functional product team devised a product strategy and roadmap and launched the full-fledged version within a few weeks.
We picked cutting-edge open-source technologies and agile practices to deliver a strong technical and process-based foundation that allowed Harris Teeter to grow their eCommerce presence by leaps and bounds.
10%
Growth in daily orders
1.5x
Increase in customer loyalty
30+
Product categories analyzed
1M+
Customer records analyzed
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