What Your Returns Are Trying to Tell You
Most brands see returns as a cost. Smart brands treat them as forecasting intelligence.
In retail, returns are often framed as a problem to fix - a financial drain, a logistical headache, a sustainability concern. But returns aren’t just a cost centre. They’re also a signal. One that, if read correctly, can significantly improve your replenishment forecasting.
Most brands separate their returns team from their merchandising and buying teams, treating returns purely as a customer service or warehouse function. But when you think about it, returns are a live feed of real customer data: what sizes fit, what colours resonate, what quality expectations aren’t met, and - most importantly for forecasters - what actually stays in customers’ wardrobes.
Gross vs Net: The Forecasting Reality
Many brands set their replenishment orders based on gross sales - how many units left the warehouse. But in categories like fashion, where return rates regularly exceed 30-40%, gross sales can be a dangerous metric to forecast against.
What matters is net sales: how many units actually stayed sold. If you don’t adjust for returns, you risk two equally damaging scenarios. First, you overbuy, assuming demand was higher than it was, and end up sat on excess stock. Second, you misread return patterns, failing to reorder in the right sizes, styles, or ratios, missing out on sell-through opportunities.
High Return Rates Don’t Break Forecasting - They Inform It
At first glance, high return rates seem like a forecasting nightmare. How do you plan when nearly half of what you sell comes back? But that’s the wrong way to look at it.
Return rates, when analysed properly, inform forecasting. They show:
Where fit issues exist (e.g. size 8s are consistently returned while 10s sell out)
Where demand is real (customers buy, keep, and re-buy similar items)
Where expectations aren’t met (colour discrepancies, fabric feel, product length)
For example, one apparel brand we talked to routinely operated with return rates above 40%. For continuity styles - core t-shirts, denim lines - this wasn’t a crisis. Returns were stable and predictable, baked into net sales expectations. Replenishment orders factored in a consistent return rate, making it part of the sales curve rather than a disruptive anomaly.
Where it becomes more complex is with seasonal styles or products that sell out fast. If you sell out of a key item in week one of a season, do you reorder immediately? Or do you wait for returns to come back before committing more cash? The answer depends on your category, supplier lead times, and willingness to risk missed sales versus holding excess stock.
The Sizing Curve: Returns as a Fit Diagnostic
Returns data also reveals hidden opportunities in your size curve. Let’s say you launch a new dress. Size 8s fly out but come straight back as returns, while size 10s and 12s sell out and stay sold. Gross sales data might suggest size 8 is the bestseller. Return-adjusted data tells you a different story: customers actually needed a size up.
A denim team at a European premium fashion brand found this when launching a new jeans fit. Return data showed the sizing ran large. They responded by adjusting the product spec for future buys, effectively shifting sizes down by one while adding a new smaller size to accommodate true customer demand. It wasn’t just about reducing returns, it was about selling the right sizes to the right customers.
When Returns Become a Forecasting Constraint
While return rates inform replenishment, they also introduce timing constraints. Ideally, you’d wait until all returns are processed before placing a reorder. In reality, waiting too long risks missing the sales window. For fast-fashion brands with short product lifecycles, waiting isn’t viable. For brands selling continuity items or trans-seasonal pieces, there’s more flexibility to wait for return data before committing to replenishment.
This is where tools and processes matter. Brands with robust returns data visibility - not just headline return rates, but granular data by SKU, size, colour, and reason code - can make smarter, faster decisions. They can place reorders that align to net demand, adjust size curves dynamically, and avoid overbuying.
The Strategic Takeaway
Returns are rarely viewed as a source of strategic insight. But if you’re ignoring them in your forecasting process, you’re only seeing half the picture. Especially in categories with high returns, like fashion, footwear, and online-only apparel, treating returns purely as a cost centre misses their operational and commercial value.
Returns aren’t just a problem to minimise. They’re a signal to optimise.
Brands that integrate returns data into their replenishment forecasting, analysing what stays sold, why items come back, and how size curves shift in reality, will hold better stock, sell more at full price, and serve customers in ways that keep them coming back.