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.
Read the full article below for detailed insights and actionable strategies.
The attribution problem
One sale. Four channels. 400% credit claimed.
Reported revenue: €400 · Actual revenue: €100 · Gap: €300
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, which is why we publish two cases where the product was declined as premature on case studies and why the read names unmeasurable channels rather than scoring them. 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 if it does not move a budget decision, no subscription attached, nothing installed on your site. The interactive demo lets you check the question-fit question before spending anything, with no signup.
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
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Key Terms in This Article
Analytics
Analytics is the systematic computational analysis of data. It reveals customer behavior and measures campaign performance.
Attribution
Attribution identifies user actions that contribute to a desired outcome and assigns value to each. It reveals which marketing touchpoints drive conversions.
Attribution Debt
Attribution 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.
Causality
Causality is the relationship where one event directly causes another, essential for identifying specific actions that drive desired outcomes in marketing.
Google Analytics
Google Analytics is a web analytics service that tracks and reports website traffic.
Incrementality
Incrementality measures the true causal impact of a marketing campaign. It quantifies the additional conversions or revenue directly from that activity.
Multi-Touch Attribution
Multi-Touch Attribution assigns credit to multiple marketing touchpoints across the customer journey. It provides a comprehensive view of channel impact on conversions.
Session
A Session is a group of user interactions with your website within a given timeframe. It can include multiple page views, events, and transactions.
Related Articles
Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.
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