When retailers come to us to look at our AI-driven lifecycle pricing solutions, the first people we talk with—the “point people” 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.”
That’s okay. At the risk of sounding like TV’s Dr. Phil, the crucial first step to finding help is acknowledging you need help.
But when those point people in turn make their case internally for adopting our solutions, others within the company may need a bit more convincing—namely the CFO or other senior management, who tend to pre-judge every new idea strictly in terms of short-term return on investment (ROI).
We can help support a compelling business case by presenting the data visually, as we did for the women’s wear division of a large North American retailer. We worked from a data set of the company’s actual markdown history over the course of a year—over 12,700 items spanning seven departments, totaling $63 million in markdown sales.
This first chart illustrates the correlation between markdowns and sell-through in a “perfect world” scenario. Every green dot represents a single markdown-priced item. Hypothetically, markdowns shouldn’t stray too far from a diagonal trendline—discounts should not be unnecessarily too low, nor too steep, to spur optimized sell-through.