How Grupo Casas Bahia Automated Customer Feedback Analysis 14x Faster with Databricks
Grupo Casas Bahia is one of Brazil’s largest omnichannel retailers, serving over 100 million customers through more than 1,000 stores and a national logistics network. The company deployed Databricks Agent Bricks with Meta’s Llama 3.3 70B model to automate the classification of customer reviews from six distinct channels. Monthly review classification jumped from 1,500 to 33,500, model accuracy reached 90%, and the company saves over 4,000 person-hours annually — equivalent to nearly R$480,000.
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Challenge
Manual classification of customer reviews across six feedback channels could only process 100 comments per hour, forcing analysts to rely on small samples while thousands of daily reviews went unanalyzed, leaving departments without timely insight into customer pain points.
Solution
Grupo Casas Bahia deployed Databricks Agent Bricks with Meta’s Llama 3.3 70B model via Databricks AI Functions, using Unity Catalog for multi-source data governance to automatically classify 33,500 reviews per month across 91 problem types.
Full Story
In Brazilian retail, where consumer protection agencies like Procon and platforms like Reclame Aqui give customers a loud public voice, the ability to act on feedback quickly is a competitive necessity. Grupo Casas Bahia receives reviews across six channels — NPS surveys, CES scores, Reclame Aqui, app stores, Procon complaints, and Consumidor.gov — and each channel speaks a different language in terms of format, tone, and urgency. Understanding what’s actually breaking for customers required reading all of it.
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