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Title
Enabling business growth with happier consumers
…atabricks Customer Stories / Aditya Birla Fashion and Retail Ltd CUSTOMER STORY Enabling business growth with happier consumers 20x faster ML serving for markdown marketing models More value on less infrastr…
Description
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.
…n and Retail Ltd CUSTOMER STORY Enabling business growth with happier consumers 20x faster ML serving for markdown marketing models More value on less infrastructure spend Faster BI reporting Retail success de…
…s are all happening on the Databricks Data + AI Platform. Through the platform, ABFRL has reduced model development times from three to four months previously to half that time, allowing them to scale ML across various use cases, from demand forecasting to…
Company
Aditya Birla Fashion and Retail Ltd
…rity and Trust Ready to get started? Get a Demo Login Contact Us Try Databricks Customer Stories / Aditya Birla Fashion and Retail Ltd CUSTOMER STORY Enabling business growth with happier consumers 20x faster ML se…
Industry
Retail
…ingle business outcome in a highly productive fashion.” Share this post Details Industry : Retail and Consumer Goods Cloud : Azure Product : Databricks SQL , Unity Catalog Ready to get started? Tr…
Problem
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.
…alytics at ABFRL. Gopinath added, “The need of our business is timely insights. Tier one data is supposed to be available for business leaders to analyze before 8:00 AM, but took us much longer. Reporting queries would take minutes — it needed to be in seconds.” Of bigger concern for the team was the inability to support data science use c…
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.
…existing data warehouse, which supports the entire BI and analytics ecosystem, they were able to securely replicate the data model and the analytical ecosystem through Delta tables, Databricks SQL and Unity Catalog. With the Spark Declarative Pipelines framework, they were able to simplify ETL…
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.
…s are all happening on the Databricks Data + AI Platform. Through the platform, ABFRL has reduced model development times from three to four months previously to half that time, allowing them to scale ML across various use cases, from demand forecasting to…
AI capabilities
Predictive Analytics, Natural Language Processing, Large Language Models, Recommendation & Personalization
…is the ability to explore cutting-edge AI in ways that weren’t possible before. The team has already implemented natural language processing for sentiment analysis and topic classification to better understand what trends their consumers are talking about and then pas…
Technology
Databricks Data + AI Platform, Databricks SQL, Unity Catalog, Delta Lake, Spark Declarative Pipelines
…on.” Share this post Details Industry : Retail and Consumer Goods Cloud : Azure Product : Databricks SQL , Unity Catalog Ready to get started? Try Databricks for free Learn more about our product Talk…
Deployment model
Cloud
…oductive fashion.” Share this post Details Industry : Retail and Consumer Goods Cloud : Azure Product : Databricks SQL , Unity Catalog Ready to get started? Try Databricks f…
Deployment options
cloud
…oductive fashion.” Share this post Details Industry : Retail and Consumer Goods Cloud : Azure Product : Databricks SQL , Unity Catalog Ready to get started? Try Databricks f…
Business functions
Supply Chain & Logistics, Marketing, Customer Service & Support
…supply chain through better collaboration with internal and external partners. Insights that used to take six months are now available to review in two to three weeks with the help of Databricks — empowering ABFRL to more accurately adjust production based on actual consume…
Implementation approach
Migrated to the cloud by replicating the existing data warehouse's model through Delta tables, Databricks SQL and Unity Catalog to minimize disruption to the existing BI ecosystem, then adopted Spark Declarative Pipelines to simplify ETL migration.
…he analytical ecosystem through Delta tables, Databricks SQL and Unity Catalog. With the Spark Declarative Pipelines framework, they were able to simplify ETL processes by minimizing the amount of code to be converted and migrated. “ABFRL was able to successfully leverage lakehouse framework and implement some…
Headline outcome
…n and Retail Ltd CUSTOMER STORY Enabling business growth with happier consumers 20x faster ML serving for markdown marketing models More value on less infrastructure spend Faster BI reporting Retail success de…
Use case type
Predictive operations
…times from three to four months previously to half that time, allowing them to scale ML across various use cases, from demand forecasting to store-level SKU assortment and allocation. Another target use case is active merchandising, which involves reviewing how…
