As a multinational consumer goods manufacturing company serving millions of retail customers, Reckitt Benckiser Group (RB) struggled with the complexity of forecasting demand, with large volumes of different types of data across many disjointed pipelines. Today, Azure Databricks provides RB with a Unified Data Analytics Platform that enables its data teams to deliver ML-powered insights to the business, improving the support of neighborhood grocery stores through predictive analytics, product placement, and business forecasting.
RB distributes their products to consumers across 60+ countries. One of their key market segments is called traditional trade or neighborhood grocery stores. This market is highly fragmented and consists of millions of small mom and pop stores, mostly in emerging markets in Asia, Africa, and South America. To serve this market, they have a team of over 16,000 reps who visit these stores with the goal of helping store owners select the best products to meet the unique needs of their markets.
Data is one of the most critical assets they have to improve demand forecasting. However, RB struggled with large volumes of different types of data across many disjointed pipelines — making it difficult for them to efficiently extract insights to help the sellers on the streets operate efficiently and drive more business.
Azure Databricks provides RB with a Unified Data Analytics Platform that has fostered a scalable and collaborative environment across data science and engineering, allowing data teams to more quickly innovate and deliver ML-powered insights to the business.
With Databricks, RB has seen significant performance gains and cost management improvements which have allowed them to scale their business and uncover new opportunities faster.
Databricks is the key enabler for us to experiment fast and then scale quickly — that’s how the platform is adding value to the business and helping us grow.”
– Atif Ahmed, Director of Advanced Analytics, RB
Technical Talk at Spark + AI Summit EU 2019