Tabdeel reads return reasons across products, customers and cities, and turns them into decisions and policy changes rather than a report that gets read once and forgotten.
Every return arrives with a reason, and over time it becomes obvious which product comes back more than the rest and why. Once you know it is sizing, or the description, or the packaging, you fix the source instead of treating the symptom every time.
Learn more about ReturnsSizing leads at 46% in this sample, so a clearer size guide on the runner would move more than any policy change.
Illustrative data from one sample store, not a benchmark.
Instead of reviewing your policy once a quarter, Tabdeel proposes changes based on how your returns actually behave: a longer window for one category, a fee on a high-return product, a free exchange for a high value customer. The suggestion arrives inside the rules engine itself, and you approve it with one click.
See the rules engine14 days from delivery on every order, and you can extend it for chosen categories or customers.
15 SAR on a changed-mind return, and nothing at all when the store is at fault.
60 days for an exchange, longer than the refund window because an exchange costs you no revenue.
Most Trail Runner returns come back over sizing, so offer another size before the refund option.
Rules apply the same way to every order, and you can change any of them at any time.
When the return reason is sizing, the smart move is to offer the same product in another size before anything else. The right suggestion raises the chance of an exchange, and an exchange keeps the money in your store rather than paying it out.
Learn more about ExchangesSuggestions come from this store's own catalogue and live stock, nowhere else.
We do not change your policy behind your back. Every suggestion arrives with its reasoning and the data it was built on, and you can approve it, reject it, or trial it on one category first and watch the effect before rolling it out.
Read about securityThis is a suggestion read from the customer's photo, and a person can overrule it before the item goes back on the shelf.
Reporting built around operational questions: how much revenue we retained this month, which category drives reverse shipping cost, and where the days disappear in the return cycle. A number you can act on, not a wall of charts.
See the dashboardEvery figure here is sample data from one illustrative store over one month, not a measured result across merchants.
Your own return data: the product, the reason, the decision, the window and the destination. The more volume accumulates, the closer the suggestions get to your reality.
We do not share your commercial data with another merchant. General patterns are used only in aggregate form that identifies no individual store, and we process data in line with the Personal Data Protection Law.
Only for the rules you explicitly allow it to automate. Everything else waits in your dashboard as a suggestion until you approve it yourself.
Suggestions need enough return history to be meaningful. Stores with higher order volume see clearer signals sooner.
Book a demo and we will walk through the kinds of patterns Tabdeel surfaces, and how each one becomes a working rule.
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