Womenswear on Shopify: how we counted, and how to set it up
For the analyst who wants to know how a Klaviyo month was matched against a Releva month on the same flow, how the A/B test ran, and how repeat rates were cut by cohort.
- Every definition behind the numbers on the case study
- The setup in the store, step by step
- What to check in your own data
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What was measured
Period and population
A direct-to-consumer womenswear brand from Singapore, on Shopify, selling into Southeast Asia, the US and Australia, with email on Klaviyo and paid acquisition almost entirely on Meta. The case runs April to August 2026. Email: a full Klaviyo month against 18 June to 17 July 2026 on Releva, same flow, same store. Site: the first 25 days from 23 June 2026, against the 16 days before launch. Repeat: first-time buyers in the April, May and June 2026 cohorts.
What counts as a result
On the flow: emails delivered (1,077 against 3,177), purchases (16 against 122), revenue indexed (100 against 936), revenue per email delivered (100 against 317), purchases per email (1.49% against 3.84%). On the site: 378,832 recommendations shown, 8.6% clicked, buyer rate 0.89% for clickers against 0.11%. Repeat: the share of first-time buyers who reordered within 60 days.
How an order is credited
Orders counted once. Every figure is a completed Shopify checkout joined to what the shopper did; no ad platform's attribution is used. The 3.8% of visitors who clicked a recommendation brought 29% of site revenue.
Controlled or observed
The flow comparison is a matched period, not a controlled test: the Klaviyo figures come from the brand's own export, with no added discounts, which the brand confirmed. The one-week A/B test was controlled: both arms sent through Releva, the agent's picks against a copy of Klaviyo's logic, and the agent earned 96% more per email. The site rise from 31 to 42 purchases a day is recommendations and email together.
What the numbers do not say
Buyers chose to click recommendations, so part of the repeat gap is intent; it held in every monthly cohort, so selection alone does not explain it. Open rate 66.2% and click rate 7.1% are set against Klaviyo's published browse-abandonment benchmarks, 38.9% and 4.9%; unsubscribes 0.28%, no spam complaints.
The terms on the case study
| Term | How it is defined here |
|---|---|
| Matched month | A full Klaviyo month set against 30 days on Releva (18 June to 17 July 2026), same flow, same store, no added discounts. |
| Emails delivered | Emails the service confirmed as delivered on the abandoned-browse flow: 1,077 against 3,177, three times the reach on the same trigger. |
| Revenue per email delivered | Revenue on the flow divided by emails delivered, indexed to the Klaviyo month: 317. |
| A/B test | One week, both arms sent through Releva, shoppers split between the agent's picks and a copy of Klaviyo's logic. The agent: 96% more revenue per email. |
| 60-day repeat rate | The share of first-time buyers in a month who ordered again within 60 days: April 54.5% against 21.7%, May 57.8% against 18.8%, June 40.4% against 14.9%. |
| Touched buyer | A buyer who clicked a recommendation, an email, or both. Touched by both: worth about twice an untouched buyer, 1 April to 14 August 2026; 2.1 orders each against 1.3. |
What was switched on, in order
Every session captured
Server-side and first-party, so the same trigger reached about three times the shoppers, including visitors who had not left an email.
One flow moved
Abandoned browse moved from Klaviyo to Releva; the rest of the program stayed where it was.
A choice inside every email
Next basket, best buys or "others who viewed also bought", picked per person and per send from their own behavior and orders.
The same agent on the site
From 23 June 2026, recommendations on product pages and the home page, decided per visitor and per page.
The A/B test
One week, both arms through Releva, the agent's picks against a copy of the old logic.
The read-out
The matched month on the flow, the first 25 days on the site, 60-day repeat by cohort.
To check it in your own store
Export one flow from your email tool for a full month: emails delivered, purchases, revenue. Run the same flow on the agent for 30 days with no added discounts and set the two side by side, per email delivered.
In Shopify, take first-time buyers by month and the share who reorder within 60 days, split by whether they clicked a recommendation. If the gap holds in every cohort, it is not only intent.
See which of last week's visitors you missed
Thirty minutes, with your store and ad accounts open. Then 30 days free. A holdout group decides: if the agent doesn't add orders in 30 days, you don't pay.
Book 30 minutes
You'll talk to one of us.