How to Solve the Retail Markdown Problem

Scaling AI Carla Winston

Markdowns — temporary price discounts on a product or service — are a part of doing business for retailers, and a component of everyday conversation between retailers and their vendor partners. From determining which products should or should not be discounted, to identifying the right discounted price-point, a lingering conundrum is how low to go in order to move inventory. This may seem simple on the surface, but too often retailers and their vendors are left with the challenge of maximizing revenue and margin while also offloading their excess inventory — a difficult feat to accomplish. 

Attempting to balance margin, volume, and revenue, on top of maintaining brand equity and market position, is the challenge when it comes to h markdowns. And for the majority of retailers, at least one of these elements (usually that of balancing margins), is sacrificed and dropped. In fact, missing the mark on markdowns results in an estimated $300 billion in lost sales for non-grocery retailers alone. This is because many retailers still depend on the not-so-tried and true manual process that uses spreadsheets containing insufficient data points to make gut decisions, leaving too much room for missed sales opportunities. But with wide-spread markdowns becoming a norm in recent months, getting markdowns right to accomplish inventory goals is now an imperative to striking the right balance between volume and margin.    

What’s the Deal With Discounts?

So what’s behind the increased number of discounts and sales we’re seeing in the retail space?

The proliferation of markdowns has been precipitated by overstocking as we moved further away from the height of the global COVID-19 pandemic and resulting supply chain impacts (e.g., ordering too much stock from persistent shortage fears). Instead of continued, growing consumer demand and spending, like what we saw during the height of the pandemic, a shift occurred beginning in 2022 that developed out of rising inflation and emerging worries of recession (among other global concerns). 

Many consumers changed their spending habits —  back the number of purchases made, reducing the amount spent per purchase, and even altering the types of products and brands they shopped for, all in response to the unknowns surrounding inflation. Reporting from McKinsey has confirmed this shift in consumer reactions to climbing inflation and looming recession, finding that 74% of consumers chose to trade-down and delay discretionary purchases. The result was a collision between too much retail inventory and dwindling demand from consumers. 

Both large retailers (Target saw inventories increase 43% vs. 2021; Walmart reported that their inventories were up 32% from 2021) and smaller retailers (38% of small businesses reducing orders) started to feel the burden from too much inventory when inflation permeated reality and shifted their shoppers’ purchase behavior. And this shift in behavior isn’t expected to change any time soon.   

The Law of Supply and Demand (and Markdown)

Which leads us back to the problem with markdowns in the face of slowed demand from previously purchase-hungry consumers and inventory heavy retailers. The built-in response to less demand and excess inventory (inventory missteps account for 53% of unplanned markdown costs), is to infuse the environment with discounts in order to spark demand and offload unused inventory using traditional methods. These traditional methods of discounting may clear inventory, but to the detriment of retailers’ bottom lines. Traditional techniques, more often than not, unnecessarily discount some items, discounting others too steeply and for too long, or else fail to discount still other products enough. This is as a result of retailers’ limited visibility into their own data, which should ideally inform them of the impact of markdowns on different products, at different times of the year, once applied. In turn, a win-lose situation is created.

Deeply discounted items may lead to quick sell-through for retailers, but without optimization, margin dollars are left on the table. So to create a win-win situation in the current environment and lessen the squeeze on margin, a markdown strategy that captures the relationship between the predicted demand of a product and price is necessary. This would allow retailers to shift away from suboptimal, markdown techniques for a solution that enables them to be proactive and data-driven in their planning. 

Unlocking proactive, data-driven planning requires an approach that uses advanced analytics to evaluate historical sales to pinpoint the elements that affect consumer purchases and confidence (e.g. inflationary pressures), as well as those that drive price elasticity for every product in order to incorporate factors that influence price-demand relationships, including: stock levels, seasonality, weather, competitive activity — just to name a handful. Goodbye to blanketed markdowns that indiscriminately discount products; hello to optimized markdowns for each item to protect margins. 

By incorporating critical information (missed in traditional methods) to predict the consumer response to a markdown, along with business guardrails unique to the retailer or vendor partner, a set of markdown prices can be generated with advanced analytics to help circumvent unplanned markdown costs and maximize a desired business objective such as profit or volume. 

The aim is not to replace your existing systems but to replace the traditional, reactive approach to markdown with an approach that uses predictive analytics for visibility into key external factors impacting demand, allowing room to adjust inventory in order to sync with consumer trends. This is a big step forward in balancing margin, revenue, and volume without sacrificing a single part of the sales equation. 

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