A first attribution read with no technical skills
The whole sequence in plain steps. What to click, the one thing to check before uploading, what to read first, and what to write down at the end.
By Joris van Huët, Founder & CEOUpdated 3 min read
Four steps, no code, and the only one that needs care is the export. Everything after it is reading.
The sequence
| Step | What you do | How long |
|---|---|---|
| 1 | Check how far back your analytics data goes | Two minutes |
| 2 | Export the acquisition report for your window | Five minutes |
| 3 | Upload it and read the output | One to two minutes |
| 4 | Write down one decision and the date | Two minutes |
Step 1. How far back does your data go
In Google Analytics, data retention is a setting, and if it is short then older detail has already gone. Check it before you plan a window, because no export recovers what retention has dropped. The consequence is explained in the two-month retention trap.
Aim for a window of eight to twelve weeks containing some real variation in spend. A flat period gives the method nothing to work with.
Step 2. Export the right report
You want the acquisition report showing channels over time, not a summary that has already collapsed them. The step-by-step is in how to use GA4 exports.
One useful check before uploading: total the orders in the file and compare against your store admin for the same dates. A few percent apart is normal. Thirty percent apart means analytics is missing a lot of purchases, and that is worth fixing before reading anything into channel numbers.
Step 3. Read it in the right order
Not the ranking. Read these three things first:
The range next to each estimate. A narrow range means the data could resolve that channel; a wide one means it could not, which is information rather than failure.
The coverage figure. That is the share of your real orders this read explains.
The list of channels marked as not measurable. Those are channels whose spend is too small for any method to separate their effect, and a tool that scores them anyway is guessing.
Step 4. Write it down
One line: what you decided, the number it rested on, and today's date. This is what makes next quarter's read comparable, and almost nobody does it.
What it costs
€99, once, with a full refund within 30 days, no questions asked. No subscription, no install, nothing added to your website.
What to do if the result surprises you
Do not act on it that day. Check whether the window contained anything unusual, then look at whether the surprising channel's range is narrow enough to trust. Common first-read surprises and their ordinary explanations are in the lift surprises hiding in your own data.
Related answers
Frequently asked questions
What do I need before running my first attribution read?
An analytics account that has been collecting for the window you want, and a retention setting that still covers it. Check the retention setting first, because no export recovers detail that retention has already dropped.How do I know my export is good?
Total the orders in the file and compare against your store admin for the same dates. A few percent apart is normal. A large gap means analytics is missing purchases, and every channel number built on that file inherits the gap.What should I look at first in the output?
The range beside each estimate, the coverage figure, and the list of channels marked not measurable. Read those three before the ranking, because they tell you how much weight the ranking can carry.
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.
- CausalityCausality is the relationship where one event directly causes another, essential for identifying specific actions that drive desired outcomes in marketing.
- 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.
- ShopifyShopify is an ecommerce platform for creating online stores and selling products. Attribution modeling shows which marketing channels drive traffic and conversions within Shopify.