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How Adidas uses Databricks

60%

“Latency dropped by 60%, cutting average response time from 15.5 seconds to six.”

As published on databricks.com. Captured by usedby on Oct 2, 2026.

What happened

adidas built a RAG chatbot on Databricks that analyzes sentiment across more than 2 million customer product reviews. It lets product, design, marketing and customer service teams, including nontechnical users, get insights from customer feedback.

Summary written by usedby from the source page, in English. The figures are those of Databricks and Adidas, not ours.

  • 98.5%“More efficient prompt engineering and smaller context windows reduced token input size by 98.5%, from 200,000 to just 3,000 tokens per query.”
  • 30–40%“Through GenAI, adidas improved review analysis efficiency by up to 30–40%, cutting down extensive manual workloads across global teams.”
  • 91.67%“91.67% cost savings”

Latency dropped by 60%, cutting average response time from 15.5 seconds to six. More efficient prompt engineering and smaller context windows reduced token input size by 98.5%, from 200,000 to just 3,000 tokens per query.

From the page. databricks.com, captured Oct 2, 2026

The GenAI infrastructure we built is already enabling use cases beyond product reviews,

Rahul Pandey, Senior Solutions Architect, Adidas. Source, captured Oct 2, 2026

What the story claims, and what we checked

We compared the story with its live page on Oct 3, 2026.

  • The figure: 60%CheckedPrinted word for word on the page, near the name of Adidas.
  • The passage quoted aboveCheckedCopied word for word from the page, near the name of Adidas.
  • The numbers in our summaryCheckedEach one is printed on the page.
  • Adidas uses DatabricksCheckedConfirmed line. Latest check across sources: Oct 2, 2026.
  • The result itselfNot checkedWe quote it; we did not measure it.

Same company, same tool or same industry.

259%“How Adidas increased AOV by 259% and revenue per user by 18.5% in one month with Insider One”Adidas uses Insider One. Another tool at Adidas2,000“supporting the decision-making of over 2,000 data users across the company”Barilla uses Databricks. Another customer of Databricks70%“achieving a 70% reduction in ETL pipeline creation time”Columbia uses Databricks. Another customer of Databricks