Retention analytics vs attribution: two different questions
Retention and LTV tools describe what buyers did after a first order; attribution asks whether your spend caused it. Use retention to price a buyer and a holdout to price a channel.
By Joris van Huët, Founder & CEOPublished 5 min read
Run the numbers for your store: the free LTV:CAC ratio calculator.
Retention and lifetime-value (LTV) tools describe what buyers did after their first order; attribution asks whether your spend caused that first order. Shopify's customer cohort report groups customers "based on the date that they placed their first order", so it starts at the very order attribution asks about. In eBay's paid search experiments on non-brand keywords, frequent users "whose purchasing behavior is not influenced by ads" accounted for most of the ad expense, so use retention to price a buyer and a holdout to price a channel.
This page compares two kinds of tool by the question each answers. It makes no claim about any vendor's product, Retention X included.
What does a retention or LTV report measure?
Three documented behaviors, in two tools:
- Shopify follows customers. Its Customers reports group cohorts "based on the date that they placed their first order", and treat a returning customer as anyone whose order history already includes at least one order. The cohort report can be filtered by the marketing channel of the first order.
- GA4 follows devices first. Cohort exploration says "Cohorts are based on the user's device data only. User-ID is not considered when creating a cohort." The User lifetime technique shows "the average LTV of users acquired by each medium" for users active after August 15th 2020, using a user ID where one is collected: "If no user ID is collected, then Analytics uses a device ID".
- The numbers move after the fact. Shopify's customer reports use "the entire order history of the new customers in the report", so a November cohort shows its December orders once they happen. Its cohort projections rest on "the previous 24 months for each cohort" and do not appear without 24 months of data.
What does attribution measure that retention cannot?
A cohort table starts at the first order, so it says nothing about whether ads caused that order. Blake, Nosko and Tadelis (NBER Working Paper 20171, May 2014; published in Econometrica, 2015, peer-reviewed) present "a series of large scale field experiments done at eBay". For non-brand keywords, "new and infrequent users are positively influenced by ads", while "more frequent users whose purchasing behavior is not influenced by ads account for most of the advertising expenses".
That's one marketplace and one ad type, and this page cites no experiment on a Shopify store, so read it as a warning about method, not a rate for yours. It points to why a ranking of channels by repeat rate is not a ranking by cause: a channel that reaches frequent buyers will look best in a cohort table whether or not it made them buy. That step is reasoning from the result, so test it.
How do the two answers combine into a budget decision?
Payback needs both: what a buyer is worth, from retention, and what a caused buyer costs, from attribution. For illustration, in made-up euros: a first buyer costs 40 to win, the first order leaves 30 of margin and later orders add 25, so each is worth 55. Suppose half of those buyers would have ordered anyway: the channel caused only half of them, the cost per caused buyer is 80 (40 divided by 0.5), and it loses 25 on each; a cohort table can show the 55, and only a test shows the 0.5.
How do you check your own numbers?
Four steps in Shopify and GA4:
- Read retention by first channel. In Shopify, open Analytics, then Reports, then Customer cohort analysis, and set the cohort definition's first-order filter to marketing channel. Pass: every channel's cohort is large enough that one order doesn't move its repeat rate. Fail: cohorts of a handful of buyers, so merge months.
- Cross-check in GA4. In Explore, open the User lifetime template and read LTV by first user medium. GA4 samples this exploration above 1M users on the free product. Pass: the channel order matches Shopify's. Fail: it differs, which is expected when one tool counts customers and the other counts devices.
- Split new from returning. Open Shopify's New vs returning customers report and read first-time buyers by month. That is the group a channel could have caused.
- Test one channel on first orders. Pause it in one region and compare first-time buyers there with the other regions (how to run the holdout). Pass: first-time buyers fall there more than elsewhere. Fail: they don't, which points to buyers who would have arrived anyway.
Where does a causal read fit?
Later, once you know which buyers are worth winning, a causal attribution read like Causality Engine's can show what each channel caused from a GA4 Attribution paths export. It does not measure retention or lifetime value.
Sources, 30 September 2026: Customers reports (Shopify Help Center); Cohort exploration (Google Analytics Help); User lifetime (Google Analytics Help); Consumer Heterogeneity and Paid Search Effectiveness: A Large Scale Field Experiment (Blake, Nosko and Tadelis, NBER Working Paper 20171, May 2014; published in Econometrica, 2015).
Related answers
Frequently asked questions
What is the difference between retention and attribution?
Retention reports what buyers did after a first order: cohorts, repeat rate, lifetime value. Attribution asks whether your spend caused that first order. Shopify's cohort report groups customers by the date of their first order, so it starts at the order attribution asks about.Can LTV by channel tell me which channel to cut?
Not on its own. It ranks who comes back, not who was persuaded. In eBay's non-brand paid search experiments, frequent users whose purchases ads did not influence accounted for most of the ad expense. Confirm a cut with a holdout that counts first-time buyers.Why do Shopify and GA4 show different returning customers?
They count different things. Shopify follows customers through their order history. GA4's cohort exploration is based on "the user's device data only" and ignores User-ID, so a shopper on a new device can look new in GA4 and returning in Shopify.Does Shopify forecast customer lifetime value?
Its cohort report can show projections of amount spent per customer, based on the previous 24 months for each cohort. Without 24 months of data the toggle does not appear, and Shopify says projections are not a guarantee of future sales.
Go deeper: Causal attribution, explained.
Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.
Keep reading
Terms in this article
- Ad SpendAd Spend is the total amount invested in advertising campaigns. It is measured against Return on Ad Spend (ROAS) to evaluate campaign effectiveness.
- 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.
- Causal AttributionCausal Attribution uses causal inference to determine which marketing touchpoints genuinely cause conversions, not just correlate with them.
- CausalityCausality is the relationship where one event directly causes another, essential for identifying specific actions that drive desired outcomes in marketing.
- ExperimentsExperiments are scientific procedures that test hypotheses or demonstrate facts. In marketing, experiments like A/B tests determine the causal effect of campaign changes, enabling data-driven decisions.
- Google AnalyticsGoogle Analytics is a web analytics service that tracks and reports website traffic.
- Holdout TestA holdout test is an experiment where a portion of the audience does not see a campaign. This measures the campaign's true incremental impact.