Reduce returns with clearer product information

“Does not fit” is a plausible return reason, but not yet a useful implementation brief. Is the item unsuitable for its intended use, the wrong size, or a different version from what the customer expected? Merchants aiming to reduce returns need to understand those distinctions first.

Product-page work is worthwhile when information is the problem. A clear detail before purchase can prevent an unsuitable choice. Shipping damage, picking errors and quality defects require other remedies. This article focuses on avoidable mistakes caused by unclear product information.

Which returns should you investigate first?

Start with products that have enough feedback and recurring explanations. One case warrants investigation, but does not establish a conclusion about all buyers. Connect the reason to the ordered variant, purchase date and information visible at that time where traceable.

Customer-service comments can be more precise than broad categories. “Too small” leaves the expectation unclear. “My 16-inch laptop does not fit in the compartment” points directly to usable internal dimensions. This is an illustrative example, not an sjmedia client case.

How do you turn a reason into a specific task?

We recommend a simple six-field record connecting analysis with implementation:

• Product and variant: identify the version actually ordered.
• Feedback: capture the specific reason without premature interpretation.
• Information gap: identify what was missing or misleading before purchase.
• Change: define a verifiable improvement to text, imagery or variant selection.
• Owner and date: record responsibility and when the change became public.
• Review: define which later orders and return reasons will be compared.

Separate cases without an identifiable information issue. Otherwise the editorial team is asked to solve fulfilment or product problems. General dissatisfaction cannot be entirely prevented by adding more details either.

Which details genuinely support selection?

Start with the specific uncertainty. A backpack’s usable internal dimensions may be more helpful than another lifestyle photograph. A clear compatibility statement for a spare part is more useful than “universal”. A set should make clear which items are included and which are shown only for illustration.

Show relevant details and size references accurately. Images should not imply that accessories are supplied when they are not. Explain what a measurement describes: external dimensions, internal dimensions and recommended device size answer different questions.

Place important limitations where customers make their selection. Information discovered in a hidden PDF after purchase has missed its purpose. Our product-page optimisation guide addresses the overall layout; here the specific return reason determines the change.

How can PrestaShop support this?

PrestaShop’s product management distinguishes descriptions, features, images and combinations. This supports deliberate separation of general properties and selectable versions. The storefront presentation depends on the version, theme and extensions used.

We favour PrestaShop for tailored catalogues because information maintenance and presentation can be aligned with the buying decision. Start with suitable existing fields. Additional development makes sense where a specific selection aid or mapping requirement is not supported by the current store.

A reliable source of information matters. If dimensions and properties come from a PIM or ERP, establish corrections and ownership there. A change made only in the store might be overwritten by the next import. German and English information should communicate the same facts and explain appropriate units clearly.

How do you assess whether unsuitable purchases decrease?

Compare purchases before and after the change only once both cohorts have had enough time for possible returns. A recent cohort otherwise looks better simply because returns have not arrived yet. Record whether you count units, order lines or orders.

In a purely hypothetical example, ten returned units out of one hundred sold give a unit return rate of ten percent. This does not establish an order return rate because an order may contain several units. For the information change, also examine returns attributed to the specific reason under investigation.

Consider promotions, shifts in customer mix and catalogue changes. A before-and-after comparison alone does not prove causation. With small volumes, gathering more feedback is often more sensible than declaring a reliable improvement from a handful of purchases.

What should you do next?

Choose one product group and a recurring, understandable return reason. Close the associated information gap and record the change. Review orders and relevant service enquiries alongside returns so a lower rate is not judged in isolation.

As a PrestaShop agency, we support data maintenance, presentation and implementation. The aim is an informed purchase decision: help customers choose a suitable item, without making legitimate returns harder.