> For the complete documentation index, see [llms.txt](https://help.littledata.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.littledata.io/google-analytics/subscriptions/subscription-revenue-by-channel-in-google-analytics-4.md).

# Subscription Lifetime Revenue by Channel in Google Analytics 4

Subscription brands often can't see which marketing channels bring in their long-term subscribers. This page shows how Littledata ties recurring orders back to the original customer in GA4, and how to build reports on subscription revenue by channel.

### Why recurring revenue is hard to attribute

Recurring orders are charged automatically. There's no website visit when they happen, so GA4 has no session to connect them to a marketing source.

Littledata sends recurring orders to GA4 and links each one to the customer who placed the first order. That means GA4's first user dimensions, like **First user source / medium**, credit recurring revenue to the channel that first brought the customer.

{% hint style="info" %}
Littledata tracks recurring orders by default. You can turn them off with **Exclude recurring orders** in the **Data customization** tab of your GA4 destination settings.

If you also want recurring purchases to keep the UTM parameters from the first order, turn on

**Attribution boost for recurring purchases** in the **Session enrichment** tab. It is off by default.
{% endhint %}

### Native GA4 vs Littledata

This example compares two explorations for the same Shopify store, date range (1 July to 18 September 2023), dimension (**First user source / medium**) and metric (**Gross purchase revenue**). One uses the native Shopify GA4 app. The other uses Littledata.

![session-medium-influencer-ga4](https://3956158252-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F1QhTbrMraNWciQeDvu1b%2Fuploads%2Fgit-blob-98ad59dd467ade78d6d6908d1b56e54ab25c76e5%2F2-influencers.png?alt=media)

### Revenue by influencer campaign

This brand relies heavily on influencer campaigns. To focus on them, filter the exploration to **First user medium** contains "influencer", and add **First user campaign** as a second dimension.

With the native GA4 app, you only see revenue from the first subscription order. With Littledata, you see all the revenue each influencer brought in over time, including recurring orders:

![native GA4 revenue by first user campaign](https://3956158252-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F1QhTbrMraNWciQeDvu1b%2Fuploads%2Fgit-blob-be61e6760bb2d6457a08b58fbbe2b53874a21bc6%2F3-native-inf-revenue.png?alt=media)

For these campaigns, Littledata shows about 2.4 times the revenue that native GA4 shows.

![Littledata GA4 revenue by first user campaign](https://3956158252-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F1QhTbrMraNWciQeDvu1b%2Fuploads%2Fgit-blob-6f39481a2363594caa0771c759b83b435bc54e4d%2F4-ld-influencer-ltv.png?alt=media)

### Segment buyers by purchase count

Littledata sends a **Purchase Count** dimension (`purchase_count_ld`) to GA4. You can use it to build segments by how many times a customer has bought.

This segment includes users with a purchase count of 3 or 4.

![GA4 segment based on Littledata purchase count](https://3956158252-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F1QhTbrMraNWciQeDvu1b%2Fuploads%2Fgit-blob-a235a9eb2bdfb530ac62371916e8c152de79e993%2F6-purch-count-3-or-4.png?alt=media)

{% hint style="info" %}
Users who have never bought have no purchase count. In this example, a segment of Purchase Count ≤ 2 matched about 1M users (98% of all users), because it also caught non-buyers. Add a lower limit, like ≥ 1, if you only want buyers.
{% endhint %}

Next, apply segments like 1-2, 3-4, 5-6 and 7+ purchases to the influencer report. This shows how well each influencer's customers keep buying (campaign names are hidden for privacy):

![GA4 revenue by influencer](https://3956158252-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F1QhTbrMraNWciQeDvu1b%2Fuploads%2Fgit-blob-f06e78efd5794f99f15738b2e5f9dd1a0e4c0df4%2F7-individual-influencers.png?alt=media)

Campaign 2 isn't the top earner overall. But it has the most revenue from customers with 7 or more purchases, so its customers keep buying the longest.

### Segment users by lifetime revenue

Littledata also sends a **Lifetime Revenue** dimension (`lifetime_revenue_ld`). The native GA4 connection doesn't have this.

This report splits active users from each influencer campaign into three lifetime revenue segments:

![Littledata GA4 user lifetime revenue segments](https://3956158252-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F1QhTbrMraNWciQeDvu1b%2Fuploads%2Fgit-blob-c4623cbed04c9f83a25a4a2801cdfa8c4a5a4d2f%2F8-ltv-segments.png?alt=media)

The first segment holds users with a lifetime revenue above $50 and up to $200. Here's how it's built:

![Creating a GA4 segment](https://3956158252-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F1QhTbrMraNWciQeDvu1b%2Fuploads%2Fgit-blob-ae8b6627a16e7370d4c7e48a896b6254ebd6c651%2F9-creating-ltv-segment.png?alt=media)

To target these users in ads, tick **Build an audience** in the top-right corner of the segment builder.

This example uses influencers, but you can use the same reports for any channel or campaign you invest in.

### Related articles

* [Custom dimensions for calculating customer lifetime value](https://help.littledata.io/google-analytics/conversions/customer-lifetime-value-google-analytics)
* [Build a lifetime value report in GA4](https://help.littledata.io/google-analytics/conversions/lifetime-value-in-ga4)
* [How to create the subscription retention funnel in GA4](https://help.littledata.io/google-analytics/subscriptions/subscription-retention-funnel-ga4)
* [Tracking subscription orders in the Shopify checkout](https://help.littledata.io/sources/shopify/tracking-subscription-orders-in-the-shopify-checkout)