The Value of Allocation Accuracy

It’s among the most frustrating parts of retailing: You built a strong assortment plan. Great product mix. Good margin. Customer response is better than you had hoped for. You're ready for an outstanding selling season.

Yet the allocation process drops the ball. Too many large dresses in store A while the racks are empty in store B.

This is a recurring problem for many retailers. The sheer magnitude of details required — sales and inventory by SKU by store by day — prevents the allocator from being able to optimize the timely flow of inventory to the right stores.

Technology gains in recent years with big data processing provides an answer to this challenge — it’s known as localization analytics — but too few retailers have been able to take advantage.

At its simplest, applications such as SPI’s Allocation Module use localization analytics to provide predictive analysis of daily sales by SKU and individual store to advise the allocator of the optimal distribution of inventory.

In addition to gaining the advantage of localized data by SKU and store, these tools also are typically able to provide data that's updated daily and, in some situations, in real time, giving the allocator the benefit of recent customer buying behavior and current store needs.

The financial benefit to the retailer is huge. More accurate and timely allocations both increase overall sales due to higher in-stock performance and increase gross margin through reduced markdowns on misplaced inventory. The tables below show two examples of the incremental financial gain for a $500 million retailer.

Example 1: Reflects a gross margin gain of $500,000 simply by reducing the volume of sales sold at markdown by 1 percent.

Example 2: Reflects a $1 million gain in sales and $630,000 gain in gross margin with a 1 percent in-stock improvement.

When combined, these two incremental gains due to improved accuracy of allocation deliver more than $1 million gain in gross margin. It's well worth the cost and effort to take advantage of the technological capabilities of localized analytics.