Holiday 2026: Connect Footwear Returns, Fit and Inventory
A returned pair of shoes creates two jobs: help the customer, and decide what happens to the stock. During the holiday rush, the second job deserves the same attention as the first. The model, size, reason for return and time needed to make the pair available again all matter.
For footwear teams, fit information adds another useful layer. It can help explain why a particular model and size came back, so the next decision is based on more than a generic “wrong size” code.
What the 2026 evidence tells us
Adobe's 2026 holiday forecast reports that AI-driven traffic to retail sites grew 127% year over year in August 2026. It also says 69% of respondents feel less likely to return an item purchased with an AI assistant's help. That second figure describes what people say, rather than a measured reduction in returns. Traffic growth also does not establish that AI caused better purchase decisions.
Meanwhile, a survey of 150 supply-chain leaders by GXO and Retail Dive's Studio found that 62% regarded returns-processing costs as one of the hardest parts of reverse logistics. In GXO's sponsored article, published March 16, 2026, returns are discussed as stock coming back into the business, with consequences for availability and allocation.
AI-generated editorial illustration of a footwear returns workspace.
Look at the model and size behind the return
Imagine two customers both selecting “too small.” One may have needed a longer shoe; the other may have found the width uncomfortable. This is an illustrative example, but it shows why one return code leaves an important question unanswered.
Start with one model over a defined sales period. Compare returns with the number of pairs sold in each size, and allow enough time for returns to arrive. A popular size can have more returns simply because it sold more pairs. A size that sold out early may appear to have weak demand if the team looks only at completed orders.
The following is a proposed review method for a retailer's own data:
Bring together | Use it to decide |
|---|---|
Return reason and product condition | Separate fit complaints from defects, preference changes and damaged packaging; establish whether the pair can be sold again. |
Exact model and ordered size | Identify where complaints concentrate, using sales in the same model and size as the comparison. |
Available foot measurements, fit preference and recommendation history | Review whether the customer's reported issue matches the available fit information. |
Stock on hand, stockouts and return-to-stock time | Distinguish unsold stock from products that were unavailable or still being inspected. |
Exchange choice and final outcome | Check whether a different size or model was kept before changing guidance or future buying quantities. |
Use the findings to choose a specific next step: review the model's size advice, check product information, or investigate size availability. Reallocation and buying decisions still need demand and stock evidence. A fit complaint alone does not justify moving inventory.
AI-generated editorial illustration of different shoe designs; no measured comparison is implied.
Where Perfitt contributes
PerfittSize combines information about the shopper's feet with information about the shoe to provide model-specific size recommendations. Its product experience includes smartphone foot measurement and guidance on the fit of the shoe being considered. See the PerfittSize shopping flow.
That foot-and-shoe information is the starting point for the review above. Connecting it with a retailer's order, return and inventory records is a separate analysis that requires agreed data access and matching records. The framework does not assume an existing automated connection to a warehouse or returns system.
For the holiday season, begin with a manageable group of models. Establish what shoppers ordered, what they returned, why they returned it and what they kept after an exchange. Then use fit information to sharpen the explanation. The goal is a decision the team can act on, with a clear way to check whether the change helped.
Run a return-and-fit diagnostic with Perfitt to identify which footwear data your team can use and which questions it can answer.