Retail|Customer Service|Increase Efficiency

Grupo Casas BahiaCustomer feedback analysis

Classifying 100 comments took an hour, forcing sampling. Llama 3.3 now sorts every review by issue type to prioritize fixes.

Nov 25, 2025|2 months ago

Key results

Annual Operational Savings
~R$480k
Annual Time Savings
4,000+ hours
Saved Per 1k Comments
9+ hours

The company

Grupo Casas Bahia logo

Grupo Casas Bahia

grupocasasbahia.com.br

Omnichannel retailer of electronics, furniture, and appliances in Brazil.

IndustryRetail
LocationSão Caetano do Sul, SP, Brazil
Employees10K-50K
Founded2010

Result highlights

  • ~R$480,000 annual operational savings
  • 4,000+ person-hours saved annually
  • 9+ hours saved per 1,000 comments
  • Monthly review processing increased from 1,500 to 33,500
  • 90% accuracy in customer journey identification
  • 14x gain in analytical efficiency

The story

One of Brazil’s largest omnichannel retailers serves over 100 million customers through more than 1,000 stores and a massive national logistics network.

Processing thousands of daily reviews from diverse channels created a bottleneck, with staff requiring an hour to manually classify just 100 comments. This labor-intensive process forced reliance on small data samples that left significant gaps in understanding customer dissatisfaction.

The team consolidated feedback from internal and external sources into a secure lakehouse to create a unified view of the customer voice. They deployed a custom prompt for Meta’s Llama 3.3 model to automate the classification of comments by journey stage and specific problem type. The resulting structured data feeds daily dashboards that enable product and UX teams to prioritize fixes based on real-time insights.

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