Retailers in the back-to-school space experienced a once-a-century year in 2020, and as the season comes to a climax, companies selling school-related products, like apparel and electronics, are using the learnings from the last year to better forecast the actual demand, determine assortment and optimize inventory levels. They are taking sales and other financial statistics from only the most immediate past periods and applying AI and machine learning to optimize their assortments and sharpen their pricing strategies.
During the last 12 months, fashion retailers heavily invested in tightening assortments and improving their inventory efficiency. While these investments are needed, all the benefits come undone if a customer cannot find their size. Worse, tightening assortments exacerbates the size stock-out problem, damages customer satisfaction, and creates significant financial pain unless size allocation is accurate. A 20% size misallocation drops margin dollars by 50% if markdown pricing is uniform across all sizes.
Fashion retailers have invested heavily to drive tighter assortments, dynamic allocations, and omnichannel inventory efficiency. But if a customer can't find her size, all that work is moot. In fact, the tighter the inventory, the more it exacerbates the size stock-out problem.
Given the recent sales disruptions and store closings due to COVID-19, David Barach, Vice President of Marketing & Pricing Analytics, provides key elements for retailers to consider in the pricing space during this period to help manage the situation now and prepare for what’s next.
Best of breed retailers are operationalizing inventory fluidity to both reduce lost sales and raise margins in how they fulfill demand. However, doing this without an understanding of future price effects risks offsetting margin and sales gains. David Barach provides a short overview how retailers can capitalize on this with antuit.ai, while avoiding a quagmire being overwhelmed by individual detail decisions. Watch the 1-minute summary video.