Solving the $50 Billion Problem of Unsold Inventory
This piece by
Kathy Leake, CEO, Trendalytics, addresses the $50 billion problems of unsold inventory in the retail industry. The column discusses how retailers can better plan and manage their inventory by leveraging consumer insights.
Fashion retail is defined by a set of moving targets. Buyers are on a constant hunt for the next big success, but even when you’ve identified a hit, it’s still not a guaranteed winner for your balance sheet. For retailers, successful buying means knowing what to buy, how much to buy, and when to buy it.
Those who’ve been on the inside of the buying process can easily point to one of the biggest sources of stress: identifying and capitalizing on new trends. But as the industry has become more data-centric, we are now able to work on the opposite problem: determining when a trend is fading and adjusting buying decisions accordingly. Unsold inventory is a $50 billion problem plaguing the industry, and reducing unsold inventory can be just as financially beneficial as perfectly timing a trendy new product.
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With the rising consumer interest in sustainability and #zerowaste, the fashion industry’s unsold inventory problem is only going to become more problematic. “Fast fashion” has become a bad word, and retailers need to make every effort to avoid the potential bad PR of overflowing stock.
Thankfully, intelligent data analysis offers us the opportunity to look at buying from both angles, identifying new trends as they’re only just taking flight and catching warning signs when a style is on its way out.
Riding the Wave
Good, robust data allows you to see trends as a wave, providing clear signals as to when the wave is growing, when it’s cresting, and when it’s starting to decline.
This data is most valuable when a trend is approaching its peak. One of the most common missed opportunities in fashion buying is to make decisions based on your most recent experience, failing to take into account a growing cache of data that could provide more context—and better predictive insights. Historical data can only provide one data point, while your best results will come from the aggregate of multiple signals. Even if a product has flown off the shelves, it is worth taking a moment to examine where the trend is in its lifecycle; you may have sold out your stock just as the trend is cresting, and doubling down on this “can’t miss” product could lead instead to “can’t sell”.
The key difference in making successful buying decisions is using predictive data rather than just performance data. It’s a question of scope: performance data tells you one story about your results, while predictive data zooms out to give more details about the story, explaining both why it happened and why it happened precisely when it did.
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We continue to see titanic shifts taking place in fashion retail, and only the agile will survive the chaos. Even as the importance of data has become a commonly accepted fact, some major retailers like Macy’s are developing retraining programs rather than focusing simply on better leveraging data. While merchandising refresher courses may add some value to their employees, their curriculum could undoubtedly be enhanced with the addition of data analysis and predictive insight training.
To stay nimble, retailers must ensure that they’re streamlining their buying operations and limiting unsold inventory as much as possible. Investing in data—specifically in well-informed, predictive data—will provide you with the tools you need to eliminate guesswork and buy more precisely.
