Retailers are already facing their next inventory crisis—too much stock on hand, with too few buyers.
When retailers come to us to look at our AI-driven lifecycle pricing solutions, the first people we talk with—the “point persons” responsible for overseeing pricing decisions—are usually quick to grasp the real benefits. They’re eternally striving for that ideal markdown “sweet spot” for every SKU—accelerating sell-through while preserving—if not squandering—margin. But without a unified, data-driven markdown strategy, they’re often the first to concede their haphazard, decentralized markdowns are “a mess”.
Anyone who’s purchased anything over the past 8-12 months—from a can of tuna to a new SUV—has shared a universal lament—almost everything seems to be getting more expensive. As of this past March, the Consumer Price Index (CPI), the government’s yardstick for tracking inflation, reported an April figure of 8.3%—a nominal .3 drop from the previous month’s 40-year high.
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.