What vendors learn from refund requests
Refunds cluster around a handful of causes, and accuracy is barely one of them. What the actual pattern implies about how to run your own evaluation.
Three platforms, three answers, one budget to defend. Your GA4 export already holds the answer, and the first finding is free and never leaves your browser. Get your first finding free
By Joris van Huët, Founder & CEOUpdated 3 min read
Refund requests cluster around a small number of causes, and accuracy is rarely one of them. That pattern is more useful for buyers than any feature comparison, because it tells you what actually goes wrong.
The recurring causes
| Cause | What actually happened | Preventable by the buyer? |
|---|---|---|
| Fit below the floor | Spend too small for anything to resolve | Yes, by asking first |
| Wrong question | The buyer needed session detail, not allocation | Yes, by naming the question |
| Data not available | The export or the retention window did not exist | Yes, on day one |
| No decision to change | Nothing was going to move regardless | Partly |
| Genuine dissatisfaction with the answer | The number was not what was hoped for | No |
The first three are all preventable in the first hour of an evaluation, which is the practical takeaway. The fourth is a governance question rather than a tooling one.
Fit below the floor is the big one
A brand spending very little per channel cannot get a resolvable estimate from any method, because the effect is smaller than the ordinary variation in its orders. No tool fixes that, and a tool claiming to has stopped measuring.
The honest handling is to say so before taking money. The arithmetic is in the measurability floor.
The wrong-question case
Some buyers arrive wanting to know what an individual customer did, and an aggregate causal method cannot answer that. It is not a failure of the tool, it is a mismatch, and it is diagnosable in one sentence: does your question have a person in it.
That test is explained in what you can answer without multi-touch attribution.
"No decision to change" is the interesting one
Occasionally a brand buys measurement while having no live budget question. The read is accurate, the report is fine, and nothing happens, because nothing was going to. That is worth naming before purchase: what decision is currently open, and what would you do differently under each possible answer.
If there is no answer to that, the honest advice is to wait until there is.
What this means for your evaluation
Front-load the three preventable causes. Check your export and retention on day one. State your question and check it is an aggregate one. Name the channel spend levels and ask directly whether they are above the floor.
That takes an hour and eliminates most of the ways an evaluation ends badly. The rest of the sequence is in testing an attribution tool inside its refund window.
Our shape
€99 for the first read on a Google Analytics export, full refund within 30 days, no questions asked, no subscription attached, nothing installed on your site.
The line worth keeping
Most refunds are fit problems wearing a product complaint. Diagnosing fit early is cheaper for everyone, including the vendor.
Related answers
Frequently asked questions
Why do people request refunds on analytics tools?
Mostly fit rather than accuracy: spend too small for anything to resolve, a question that needed individual-level data, or an export that did not exist. All three are diagnosable in the first hour of an evaluation.How do I know if my brand is too small for causal attribution?
Ask the vendor directly what spend level per channel is needed before an effect can be separated from ordinary variation, and compare it against yours. A vendor that will not give a figure has not thought about its own floor.Should I buy measurement if no budget decision is open?
Probably not yet. If nothing is going to move regardless of the answer, an accurate report changes nothing. Name the open decision and what you would do differently under each possible answer before buying.
Go deeper: Causal attribution, explained.
Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.
Have an audience of ecommerce operators? Earn 20% recurring commission by recommending the €99 read.
Keep reading
Terms in this article
- AnalyticsAnalytics is the systematic computational analysis of data. It reveals customer behavior and measures campaign performance.
- AttributionAttribution identifies user actions that contribute to a desired outcome and assigns value to each. It reveals which marketing touchpoints drive conversions.
- Attribution DebtAttribution debt is the gap between what your ad platforms claim drove revenue and what actually caused it, carried quarter after quarter into the budget. It is how marketing debt accrues: allocate on claimed conversions long enough and the plan itself becomes the liability.
- Google AnalyticsGoogle Analytics is a web analytics service that tracks and reports website traffic.
- IncrementalityIncrementality is the part of an outcome, such as orders or revenue, that a marketing activity caused: the extra results compared with what would have happened without it. It is a causal effect, not the number of conversions credited to a channel.
- Multi-Touch AttributionMulti-Touch Attribution assigns credit to multiple marketing touchpoints across the customer journey. It provides a complete view of channel impact on conversions.
- SessionA Session is a group of user interactions with your website within a given timeframe. It can include multiple page views, events, and transactions.