New features, improvements, and fixes in Attribuly.
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Recapture now integrates with ActiveCampaign, helping merchants identify more missed cart abandoners and reach them through abandonment emails.
A customer may complete a purchase through Google after first discovering your brand on TikTok. Click attribution alone can understate the value of some channels.
The Post-Purchase Survey asks customers where they first heard about your brand after checkout, adding another perspective to your attribution data.
Get started: Go to Attribuly → Marketing Analytics → Post-Purchase Survey → Setup.
Capture can now automatically sync identified visitor email addresses to your SHOPLINE admin, making it easier to manage and reach potential customers.
We've just made it way easier to segment your Capture leads right inside Shopify. 🥳
Attribuly now automatically syncs all emails captured by the Capture feature to your Shopify admin on a schedule. Each synced email gets tagged with Attribuly Capture, so you can instantly pull up a list of every visitor who dropped their email via Capture.
No more manual CSV exports → imports. It just runs in the background.
1. Connect the destination (one-time setup)
Go to Destinations → Email→ Shopify and make sure your Shopify integration is enabled. Once it's on, the auto-tagging runs on the schedule — no extra setup needed.
2. Filter customers in Shopify
Open your Shopify Admin → Customers page, then filter by the tag:
🎁 : All Benchmark metrics (NC CPA today, and every metric we add next) are 100% free forever for every merchant on Attribuly — no paid plan required.
| Home → Benchmarks section | Your NC CPA vs peer P50, with a gradient bar showing Top 25% / Middle 50% / Bottom 25% ranges. One glance tells you if you're competitive. |
| Benchmarks detail page (click View full comparison) | 6 metric cards (NC CPA, CPA, CPC, ROAS, CVR, MER) side-by-side, plus Google / Meta / TikTok / Bing per-channel breakdowns. |
| Future: Recommendations cards | Upcoming evidence rows will surface NC CPA benchmarks when we flag your spend structure (e.g. "Your Brand Search NC CPA is 36% below peer median — this is a well-tuned budget.") |
This is just the beginning. Over the next 8 weeks we're shipping these — on the house:
| NC CPA ✅ Released | "What should I pay for a new customer?" | Today |
| CPA (total conversions) | "What's my blended cost per action, compared to similar stores?" | Next 2 weeks |
| CPC | "Am I overpaying per click on Google vs peers?" | Next 2 weeks |
| Blended ROAS | "Is my $1 of ad spend turning into more or less revenue than the competition?" | Month 2 |
| CVR (Conversion Rate) | "Should I fix my ads first, or my landing page?" | Month 2 |
| MER (Marketing Efficiency Ratio) | "What % of my revenue is going out the door as ad spend?" | Month 3 |
| Secret metric 7 🤫 | Ask us in-app! Feedback from merchants shapes this column. | Month 3 |
🔒 Your data never leaves the pool individually. Every benchmark number is aggregated and anonymized — other merchants never see your store, and you never see theirs. It's a one-way mirror: everyone gets the market-level truth, nobody gets anyone's private numbers.
New Feature
MambaSMS Integration Support
We are improving Shopify Shop attribution so that orders and visits from the Shop app can be identified more consistently in attribution paths.
Previously, Shopify Shop events were mainly added during order backfill. This could miss some cases, such as orders that already had tracking events or orders that matched other visits during backfill.
During attribution path cleaning, we will check each order's channel_name.
If channel_name = Shop, a shopify_shop event will be added to the order's attribution path.
When a visit URL contains utm_source=shop_app, the visit will be recognized as shopify_shop.
This rule will not apply if page_referrer is from the same website.
With this update, Shopify Shop can be identified from both the order channel and visit UTM signals.
You can now build customer segments based on each customer's total order amount.
In Segment conditions, the Customer's property section now includes a new property:
This condition uses the customer's lifetime total order amount from Shop customer data. It helps you create more precise customer groups based on purchase value, such as high-value customers, low-spend customers, or customers who have not reached a target purchase threshold yet.
Create a segment for customers whose total order amount is greater than 500.
Example:
Customer's property: Total spent Condition: is greater than Value: 500
This segment can be used to identify loyal or high-value customers for VIP campaigns, retention programs, or exclusive offers.
Create a segment for customers whose total order amount is less than 100.
Example:
Customer's property: Total spent Condition: is less than Value: 100
This segment can help you find customers who have purchased but have not yet reached a meaningful spending level.
Create a segment for customers whose total order amount equals a specific value.
Example:
Customer's property: Total spent Condition: equals to Value: 0
This can help you identify customers with no recorded spending in Shop customer data.
The amount input displays the store's currency symbol automatically.
For example:
$ 500 € 500 ¥ 500
The currency symbol is shown to make the input easier to understand. The segment condition itself is saved as a numeric amount, so the filtering logic remains consistent with the store's Shop customer data.
Before this update, customer segments could be built from customer attributes and behavior events, but there was no simple way to filter customers by their total order amount.
With this update, merchants can:
This condition is calculated from Shop customer data.
The filtering logic uses:
shop_customers.total_spent
This means the segment is based on the total amount recorded for each Shop customer.
We’ve added a new metric, Click Conversion Rate, to help you better understand how efficiently ad clicks turn into conversions.
Click Conversion Rate is calculated as:
Conversions / Clicks × 100%
Previously, clicks and conversions were available as separate metrics, but it was harder to quickly evaluate how effectively clicks were turning into conversions. This new metric makes it easier to compare performance across campaigns, ad sets, ads, and other attribution breakdowns.
You can view Click Conversion Rate in both All Attribution and Ads Attribution reports. Open either report and check the Click conversion rate column in the data table. If the column is not visible, update your table column settings to include Click conversion rate.
Previously, Ads Attribution only showed one date range at a time, so users had to manually switch dates or export reports to compare performance. With date comparison, users can now evaluate performance changes directly in the report and identify what changed between two periods.
What’s New
You can now use Compare to in the Ads Attribution date picker, just like in All Attribution. This lets you compare your current reporting period against a previous period, previous year, or a custom date range directly inside the Ads Attribution report.
Attribution > Ads.Compare to.Previous period, Previous year, or a custom comparison range.Apply to refresh the report.Current, Comparison, and Change values.
