Enabling business growth with happier consumers

Overview

Aditya Birla Fashion and Retail Ltd. (ABFRL) unified data from 4,000 retail locations onto the Databricks Data + AI Platform to power machine learning across markdown pricing, in-store recommendations, detractor segmentation via k-means clustering, and market basket analysis, achieving 20x faster ML serving for markdown models and cutting model development time in half. The company also uses LLMs to auto-generate product descriptions and NLP for sentiment analysis and topic classification of customer feedback.

The challenge

ABFRL needed to deliver insights to regional decision-makers in minutes rather than days or weeks, but had to frequently replicate data from the data warehouse for analytics or AI, creating data silos. Machine learning data had to be manually copied between systems, causing mismatches between what data scientists saw when building predictive models and what the business saw in BI. Tier one data meant to be available for business leaders before 8:00 AM took much longer, and reporting queries that needed to run in seconds instead took minutes.

The solution

ABFRL chose the Databricks Data + AI Platform to unify 20 to 30 different data sources and integrate engineering, data warehousing and ML under a centralized lakehouse architecture. To minimize disruption to their existing BI and analytics ecosystem, they securely replicated the data model through Delta tables, Databricks SQL and Unity Catalog, and used the Spark Declarative Pipelines framework to simplify ETL by minimizing the code to convert and migrate. On the platform, ABFRL built markdown recommendation and demand-forecasting ML models, used k-means clustering to segment brand detractors for automated personalized outreach, used LLMs to enrich product catalog descriptions, and implemented NLP for sentiment analysis and topic classification on voice-of-customer data passed to the call center team.

Reported business value

ABFRL reduced model development times from three to four months down to half that time. Markdown reviews that used to happen only at the end of a three-month season via manual spreadsheets can now happen weekly, biweekly or monthly, leading to higher revenues on lesser discounts. Supply chain insights that used to take six months are now available in two to three weeks. Thousands of AI-generated product descriptions can now be created in a few hours instead of days, and were found to be of better quality than individually authored content.