Case Studies – AI Analytics for Text – Retail
Every store is unique—and that’s not necessarily a good thing, especially if you’re a national brand. Why do certain stores succeed? And why do other stores struggle?
The Problem
A major grocery retailer was expanding rapidly, opening new locations across the country. Some stores flourished, while other stores withered on the vine, and it wasn’t clear why.
Why were some stores performing better or worse than others? What makes a store successful? What needed to change at stores that were struggling?
The retailer tried to answer these questions through survey research, but they weren’t getting the full story. They needed big-picture perspective to really understand what makes a great store and how to increase the overall success rate of new stores as they continued to grow.
The Solution
The internet is a cornucopia of data, just waiting to be harvested. So for this challenge, we leveraged publicly available Google Store reviews to understand grocery store success at the brand level and at the individual store level.
We pulled more than 262,000 reviews across the category and used Bellomy AI Analytics for Text to quickly analyze the star ratings and open-ended comments.
- We looked at Google Store reviews for our client’s stores and competitors within each store’s footprint
- We compared problem stores to successful stores, using impact scores to understand what was causing the most damage—and the most delight
- We leveraged our retail-specific AI topic model, which has been manually trained to deliver the most relevant and accurate insights for this industry
The Results
Bellomy AI Analytics for Text took a seemingly endless field of customer opinions and transformed it into bite-sized pieces of actionable information.
- We found common themes for the highest-performing stores across brands
- We clarified what was driving negative reviews for our client’s problem stores
- We provided big-picture context, showing what it would take to be competitive in each market
We pinpointed the specific problems at underperforming stores… which explained why they weren’t doing so great.
Bellomy AI Analytics for Text revealed that the most common complaints included poor customer service, not enough registers, stale and moldy produce, low stock of specific items… things that really take the “super” out of supermarket.
Our findings were highly prescriptive, which allowed the retailer to focus their energy on targeted issues that they knew were causing problems—taking the guesswork of the process.
We also learned that store managers weren’t always leading their teams to success.
Using Bellomy AI Analytics for Text, we compared the number of 5-star reviews for our grocery client vs. their competitors in the footprint. We found that when reviewers mentioned managers/supervisors in our client’s problem stores, only 14% of those were 5-star reviews. On the other hand, for competitors, 52% of reviews mentioning managers were 5-star reviews.
Store supervisors can have a big impact on a customer’s experience—both positive and negative. We provided additional details around these reviews, which helped the retailer address store-level concerns and establish best practices for other new stores, preventing future issues.
Do you have “problem” stores and need to understand why they’re struggling?
We can use Bellomy AI Analytics for Text to analyze publicly available data and learn what it will take to be competitive in specific footprints.