This article originally appeared on ET Edge Insights.

Imagine an online brand selling trendy fashion items. One day, a celebrity posts a photo wearing a dress from their collection, and demand skyrockets overnight. But the brand is caught off guard with insufficient stock, leading to lost sales and frustrated customers. This isn’t just a hypothetical scenario; it’s a real challenge that eCommerce businesses face when it comes to predicting what customers want and when they want it.

Every festive season, brands brace for the uncertainty. Will the latest smartphone sell out in hours, or will warehouses be burdened with unsold stock? For a leading electronics retailer last Diwali, inaccurate demand forecasting led to 30% stockout loss on bestsellers, while excess inventory in other categories eroded their margins.

Today, demand forecasting is changing this narrative in eCommerce. Brands are now predicting demand with remarkable accuracy, in no time.

Turning customer data into smarter inventory decisions

In this hyper-competitive eCommerce space, data-driven forecasting isn’t just an advantage—it’s essential for growth. It ensures products are available at the right time and place, a key factor in maintaining market position and expanding reach.

For eCommerce businesses, the most reliable indicator of future demand isn’t guesswork – it comes from analyzing real-time customer data. Every purchase, abandoned cart, and product search provides valuable insights into consumer behaviour.

This data becomes even more vital in quick commerce, where inventory decisions can heavily impact profitability. Quick commerce’s reliance on dark stores—hyperlocal warehouses for rapid deliveries—highlights the importance of precise forecasting. Demand can vary drastically across locations, making it inefficient to stock the same items uniformly. Without accurate forecasting, brands risk stockouts in high-demand areas and excess inventory elsewhere, leading to potential losses.

Predictive models help businesses anticipate demand at a hyperlocal level. By analyzing regional preferences, historical sales trends, and external factors like local events, brands can optimize product placement. This proactive approach gives quick commerce operators a strategic edge, ensuring customers get what they want—instantly.

Demand forecasting maximizes pricing, promotions, and marketing impact

Forecasting helps businesses make smarter pricing and promotion decisions, ensuring they stay competitive while maximizing revenue. Even small price changes can significantly impact demand.

For instance, reducing the price of a best-selling skincare product by ₹50 might increase sales by 20%, but a flat 50% discount could reduce profits without driving enough additional sales. Forecasting models analyze customer price sensitivity across different product categories, helping brands determine the optimal pricing strategy.

Promotions are key factors in driving sales, but not all strategies deliver the same results. Running ‘What if’ scenarios helps businesses decide whether bundling, limited-time discounts, or free shipping will have the best impact. For example, offering a smartphone with discounted wireless earbuds might boost total sales more than simply lowering the phone’s price. Demand forecasting enables brands to test different strategies and predict their impact before implementation.

New product launches can shake up demand for existing ones. If a sportswear brand launches a new line of running shoes, interest in older models may decline. Without accurate forecasting, businesses may end up with excess stock, tying up capital in inventory that won’t move. Analyzing past launches and customer behaviour, businesses can anticipate these shifts and adjust inventory proactively.

Finally, demand forecasting ensures marketing budgets are spent wisely. Instead of spreading ad spend across all products, businesses can focus on high-demand items, seasonal trends, or products with strong promotional potential. If forecasts predict increased demand for fitness gear in January, brands can boost marketing efforts for these products while scaling back on less relevant categories.

Staying ahead of trends when demand shifts unexpectedly

Social media can reshape demand overnight. A single viral post can shift consumer preferences. For example, the sudden shift from skinny jeans to loose-fit styles caught many brands off guard, leaving them with excess inventory of items. Advanced forecasting models address this challenge by continuously analyzing historical trends, market sentiment, and competitor movements. These models detect early signals of demand shifts, enabling brands to adjust inventory before trends fully materialize. This enables brands to ride the wave of emerging trends rather than playing catch-up.

Accurate forecasting reduces delivery time by ensuring products are available in warehouses closest to anticipated demand. Smarter forecasting means fewer inventory imbalances, reducing the need for backorders and costly express shipping. This not only enhances customer satisfaction but also helps brands maintain competitive pricing.

Brands are no longer just reacting to demand—they are shaping it with advanced forecasting. This isn’t just about boosting sales or cutting costs—though it certainly achieves both. It’s about creating a seamless shopping experience where customers find what they want, exactly when they want it. That’s how brands build long-term loyalty in an increasingly fast-paced digital world.

Authored By Prem Bhatia, Co-Founder and CEO, of Graas.