- Data Analytics
- Success Stories
Demand forecasts missed its mark
Inventory planning got tougher
Promotions went out of sync
Inventory optimization led to significant savings of millions of dollars
Time-to-insights for sales projection was slashed from 2-3 days to minutes with 600X latency reductions. The data lake also gave insights into the number of price adjustments made in-store.
Data lake allowed promotions across online and in-store entities to be executed in sync, leading to better margin management for retailers.
Most apparel retailers have been quick to see the growing relevance of cross-channel transactions. Regulars at their high street stores, they noticed, first browsed through the online racks. When customers did shop online, they sometimes asked to return outfits at a nearby store. And retailers started to rejig their systems to support this cross-channel shopping.
When retailers treated online and offline channels as separate entities, the core of their supply chain was not prepared to adapt to the new realities of omnichannel retailing, and could not leverage opportunities it had to offer. The real challenge was to find a way for retailers to have a holistic view of transactions across all channels.
Time to insights took 2-3 days
Provided a unified view across channels with real-time analytics
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We built a model that considered the entire inventory - both online and offline. With orders that were placed, orders that were ready for shipment cancelled orders, orders reserved at the store, and returns called out, the ‘available-to-sell’ inventory was published for every store manager’s perusal, and order planning. Order routing, thus, became more intelligent.
We then merged the online and in-store data sets to create one unified data lake that provides a rich source of insights for sales projection - without the additional effort of an operations team working to unify and prepare data for sales planning, week after week.
Several customers bought products online, especially during promotions, but returned some of it thereafter. The retailer relied on the number of products shipped to arrive at the demand metric. The proposed model helped understand demand more holistically. It offered better visibility into orders placed, orders cancelled, and orders shipped – thus leading to better demand forecasting.
Merged online & in-store data to forecast demand effectively & project sales.
Proposed model enabled retailers to understand demand holistically by offering visibility into orders placed, cancelled and shipped.
Gained anytime-anywhere visibility into ‘available-to-sell’ stock, built a single source of insights for sales planning, and improved to-demand forecasting.