How Retailers Predict Demand Using Unified Sales and Inventory Data

BJ

Bristi Jha

19/2/2026

Average Reading Time: 5 minutes

In today’s competitive retail environment, just responding to demand is no longer sufficient. The most successful retailers leverage integrated sales and inventory data to predict their consumers' demands, which leads to the existence of fewer out-of-stock and overstocked items while increasing their total revenues and customer satisfaction.

A fragmented view of sales, inventory, loyalty, and channel data leads to poor forecasting and slow operations. Indian retailers such as Reliance Retail, Tata’s Croma, and Shoppers Stop are reversing this trend by implementing integrated data platforms that aggregate all sales and inventory data, making it possible for accurate predictive demand analysis.

1. Reliance Retail

Reliance Retail offers multiple segments of retail businesses such as supermarkets (Reliance Fresh); digital e-commerce; and specialty stores and that requires one source of truth for demand forecasting to exist across segments.

Critical Elements of Reliance’s Solution

  • They collect POS data from stores, online orders, and marketplace sales are ingested into a central database.
  • The data from the inventory in the warehouses and stores is also linked to the same platform, so there is no delay in the demand signals.
  • The advanced forecasting tools use the same data to predict the future demand based on categories, regions, and seasons.

Forecasts based on unified data help Reliance Retail reduce both stockouts during peak seasons and excess inventory in slow categories. It improves turnover and profitability.

2. Tata Croma

Croma is the consumer electronics division of Tata which has a high-value involved in high-value and fluctuating demand for products like smartphones, laptops, and various types of smart home devices. Given the diverse range of product lifecycles, it is essential to have accurate demand forecasts.

How Croma Uses Unified Data

  • As a unified data repository, Croma is using one central analytic data platform to collect all of its sales data from its website, app, and store. 
  • They are using daily stock movements as well as supplier lead times in their forecasting models.
  • Consumer interest in products (wishlist creation and category browsing) as well as the historical purchases will also help define demand signals for Croma.

Utilizing the unified data set, Croma can do much more than just make forecast projections. They will be able to identify early trends in consumer interest, allowing Croma to adjust inventories, promotions, etc., prior to demand fully developing.

3. Shoppers Stop

As one of India's respected department store chains, Shoppers Stop has stores, apps and market places where it sells products to customers. Apparel and lifestyle have distinct demand patterns that vary with season, fashion trends, and promotions. Shoppers Stop's demand strategy has a number of different methods for forecasting and maintaining stock levels and promotions can also impact sales.

Shoppers Stop’s Demand Strategy

  • All channels have the same analytical engine so they will all use the same sales & inventory data.
  • Instead of a broad store-wide forecast, each category or product will be forecasted separately based on history.
  • Sale periods, type of promotion (sale,  buy one get one free), and brand level promotions can all be correlated to historical sales.

The end result of Shoppers Stop utilizing a single source of data to make better decisions in planning what assortments to buy, when to replenish stock, which promotions are best to run, is an improvement in both customer satisfaction and profitability.

How DataSense Accelerates Demand Forecasting

With such a platform as DataSense, retailers are enabled to shift focus from reactive planning to predictive accuracy through:

  • Ingesting all real-time and batch data from stores, apps, marketplaces, warehouses, loyalty systems into one trusted place.
  • Providing a single point of truth for all forecast inputs (sales, inventory, returns, lead time) and outputs (predicted demand, replenishment recommendations) which will be used throughout the entire data ecosystem. 
  • Planners can therefore have greater confidence in their ability to rely on that data for planning purposes by reducing reconciliation cycles and assisting with quicker decision-making.
  • Forecast outputs are operationalized, directly driving planning, buying, store, and supply chain execution systems.

DataSense helps retailers leverage data volume to strategic foresight by not treating data as a set of logs.

Conclusion

Demand forecasting relies on data and combining all sales and inventory signals into one cohesive picture. The experience of retailers such as Reliance Retail, Tata Croma and Shoppers Stop shows that when you link sales and inventory together, your ability to forecast demand will greatly improve which will enable better stock availability, decrease in clearance and markdown expense, increase in effectiveness of promotional planning, and a quicker response to new trends.

The combination of data will allow for more informed decisions and enhanced demand forecasting, thus DataSense turning the art of demand forecasting into a science.