A returned order often looks like a logistics problem. Yet the error may have started with a missing condition in the catalogue before the product even left the warehouse. Understanding the true cost of incorrect matching requires looking beyond the returned part to the entire workflow triggered by the error.
How can a small omission create a chain reaction?
Consider a hypothetical example: the same vehicle model uses parts with different technical specifications in different production periods. The product listing contains no date limit, and the user selects a part based only on the model name. Even if the order is processed flawlessly, the dispatched product may not suit the intended application. The problem here is not picking accuracy, but insufficient information at the point of selection.
Customer service then becomes involved; the technical team checks the issue, the return is approved, the product is received again and a new dispatch is planned. On the service side, a pending job order may need to be rescheduled. Because each step appears under a different budget, the overall impact of the data error may never be consolidated in a single report.
Make costs visible in four separate categories
Direct expenses include additional transport, picking, packing, inspection and payment-processing costs. Labour costs reflect the time spent on conversations, technical investigation and record corrections. The inventory impact depends on whether the returned product can be resold, how long inspection takes and the condition of the packaging. Commercial effects may also arise from a delayed order or the loss of a customer relationship.
It is important not to count the same loss twice. The value of a returned sale does not automatically represent a net loss; the product may be resold. Lost revenue and lost contribution margin are also different concepts. Tracking operating expenses, inventory impairment and estimated commercial losses separately therefore gives a clearer picture.
A simple calculation starts the right discussion
To illustrate the method only, assume 40 incidents linked to verified data errors in one month, with 250 TL in additional logistics and processing expenses per incident, plus 30 minutes of employee time. If the total hourly labour cost is assumed to be 400 TL, the direct operational impact is 40 × (250 + 0,5 × 400) = 18.000 TL. This is neither company data nor an industry average.
The calculation excludes inventory impairment, service delays and customer loss. These should be added only if they can be independently substantiated. The aim is not to produce a striking figure, but to see which type of error consumes which resources. The same method can also help compare the cost of a corrective project with avoidable expenses.
Do not classify every return as a data error
Incorrect selection, dispatch errors, a customer changing their mind and product defects are different causes. Simply writing 'incompatible product' on a return form is not enough to identify the root cause. The product record at the time of the order, the vehicle information entered by the customer, the dispatched product and the technical findings should be reviewed together.
The review can distinguish between a verified data error, a possible data error and a cause unrelated to data. One product group may produce more errors because it is more complex than another; the incident rate relative to the relevant order volume should therefore be monitored alongside the incident count. Comparing physical product attributes with records is also consistent with the auditing approach in the GS1 data quality programme. [1]
Build prevention into the process before the sale
Publication rules should reflect the actual risks of the product group. Required dimensions, application limits and mounting position should be defined, and conflicting records should be referred for expert review. Rather than presenting uncertainty as confirmed fitment, making the missing condition visible to the sales team creates a more reliable basis for a decision.
A correction should not remain confined to the master record. Identify the channels and systems where the same information has been published and verify that the update has reached them. Then review similar records and establish a control rule that will be triggered if the same error recurs. This turns each return from an isolated transaction into a learning opportunity that feeds back into catalogue quality.
Turn the returns report into an improvement plan
When assessing your data quality needs with Apis Business Solutions, start with recently verified errors. Identifying the most frequent cause, the affected product group and the time it takes a correction to reach every channel can clarify the first investment priority.
Sources
- [1] GS1 US - National Data Quality Program — Comparing the physical product with published attributes. https://www.gs1us.org/services/data-quality/national-data-quality-program