When the data is wrong: the cost of inaccurate product content
Wrong specs, fitment, or images cost more than empty fields ever will. Here's the cost math on inaccurate product data, and how to measure it.
Missing a spec field costs you a click. A wrong one costs you a return, a chargeback, a one-star review, and sometimes the listing itself. Retailers spend a lot of energy on completeness scores, but completeness and correctness are different problems with different price tags, and the correctness problem is the one showing up in your returns ledger right now.
Incomplete is a gap. Incorrect is a liability.
Incomplete data loses you the sale before it happens: a shopper can't find a compatible part, can't confirm a dimension, bounces to a competitor listing. That's an opportunity cost, and it's real, but it's contained.
Incorrect data loses you the sale after it happens. The customer trusted the listing, ordered, paid, and got something other than what the page promised. Now you're not just missing a sale — you're paying for a reverse shipment, a restock (if the item is even resellable), a support ticket, a refund, and in a meaningful share of cases a damaged relationship that shows up as a lost repeat customer. Total US retail returns are estimated at roughly $850 billion for 2025, with online return rates commonly cited in the 19–25% range. Not all of that is a data problem, but a sizable slice of it is preventable, and "item not as described" is consistently one of the top-cited reasons shoppers give.
The categories of wrong that cost the most
Not every error is equal. Some get caught at checkout. Some don't surface until the box is open, and those are the expensive ones.
| Error type | Where it bites | Why it's expensive |
|---|---|---|
| Fitment / compatibility | Auto parts, appliance parts, industrial components | Wrong part fits nothing; buyer often reorders correctly and returns the first, doubling fulfillment cost |
| Dimensions / weight | Furniture, apparel, packaged goods | Triggers "not as described" disputes and freight-class errors on the outbound shipment |
| Images that don't match the SKU | Any catalog with variant sprawl (color, finish, bundle) | Buyer receives a different-looking item; high refund rate, high review damage |
| Spec or material inaccuracy | Electronics, building materials, safety equipment | Can create real liability if the item is used for a purpose the (wrong) spec implied it could handle |
| Pricing / availability mismatch | Marketplace and syndicated feeds | Triggers marketplace penalties independent of whether the customer even completes the order |
Fitment and spec errors are the costliest because they're invisible until the product is in the buyer's hands. A missing field is obvious on the page. A wrong torque rating or an incorrect "fits 2019-2023" range looks exactly as credible as a correct one, right up until it doesn't work.
Where the cost actually lands
"Bad data costs money" is true but not actionable. Here's where to look for the line items, and how to measure each one.
| Cost | Mechanism | How to measure it |
|---|---|---|
| Return freight and restocking | Wrong item ships, customer sends it back | Returns platform (Loop, Narvar, or your 3PL) filtered by reason code "not as described" vs. "changed mind" |
| Unsellable inventory | Returned item can't be restocked at full value due to damage or open-box status | Warehouse management system markdown/write-off report tagged to return reason |
| Chargebacks and dispute fees | Cardholder disputes under Visa/Mastercard "not as described" codes rather than requesting a return | Payment processor dashboard, chargeback reason code 13.3 (Visa) or 4853 (Mastercard) — Visa's reason code 13.3 exists specifically for this |
| Support ticket load | Buyer contacts support to confirm fitment/specs before or after purchase | Ticket volume tagged by SKU or category in your helpdesk, indexed against order volume |
| Marketplace suppression | Amazon and similar platforms deprioritize or pull listings flagged for inaccurate content | Marketplace seller central health dashboard; suppressed-listing count and reinstatement time |
| Review and rating drag | Buyer leaves a 1–2 star review citing mismatch, which suppresses future conversion on that PDP | Review platform sentiment tagged for "not as described," "wrong size," "different than pictured" |
| Lost repeat purchase | Buyer doesn't come back after a bad experience | Cohort repeat-purchase rate for customers with a "not as described" return vs. customers with no return |
The chargeback line deserves attention distributors often skip. A customer who feels misled by the listing frequently disputes the charge with their card issuer instead of requesting a return, which skips your return process entirely and adds a dispute fee on top of the refund. That's a cost incomplete data never creates, because incomplete listings don't set false expectations to violate — they just don't convert.
Marketplaces punish wrong data harder than empty fields
If you sell on Amazon, Walmart Marketplace, or similar, inaccurate content is treated as a policy issue, not just a UX issue. Inaccurate product information is one of the most common triggers for Amazon listing suppression, pulling a listing from search and buy-box eligibility independent of any customer complaint. Minor content issues are often reinstated within a day or two once corrected, but policy-level infractions can take a week or more to resolve. That's revenue at zero for the suppression window, on top of whatever returns already happened before the platform caught it.
This is a different failure mode than a thin listing sitting quietly at the bottom of search results. Thin listings underperform. Wrong listings get flagged.
Trust is the cost you can't put a line item on
Every category above is measurable. The one that isn't — but matters most — is what a wrong-item experience does to whether that customer trusts your catalog next time. A shopper who gets the correct-but-sparse listing learns to ask more questions. A shopper who gets confidently wrong information learns to distrust the whole store, and that discount follows them to every future PDP, every review they read, every AOV decision. You can proxy this with repeat-purchase rate by return reason, but the real cost compounds quietly for months after the refund clears.
What this means for how you run your catalog
Completeness and correctness need separate scorecards, because they fail differently and get fixed differently. A gap-fill project reduces bounce and abandoned carts. An accuracy pass — validating specs, fitment, and images against source documentation rather than whatever was typed into the system five years ago — reduces returns, chargebacks, and suppression risk. Anglera enriches on top of whatever PIM you already run, extracting and quality-scoring values from actual supplier documentation rather than guessing, so the fitment and spec fields your buyers rely on are backed by a source, not a legacy assumption. The through-line is the same one that gets a buyer to the right product: it also has to survive contact with the box they open.