Key Takeaway

  • Starting at $40 per month after a 30-day free trial, the Airbridge Core Plan includes 500,000 data points without requiring an annual contract.
  • The plan natively aggregates ad spend and attributes conversions across Google, Meta, Apple Ads, and TikTok while linking campaigns to paid subscribers and revenue.
  • Teams can forward collected attribution touchpoints and in-app events into existing product analytics workflows through two included third-party integrations, supporting tools like Amplitude and GA4.

If you are evaluating what to add to your product analytics stack for ad attribution in 2026, your first question is likely how much an attribution layer costs and how it connects ad spend to downstream subscription outcomes. Product teams can track in-app behavior in detail, yet connecting that activity back to paid acquisition requires a dedicated mobile attribution layer. When you evaluate options, you need a clear picture of entry pricing, how ad-channel costs connect to subscription revenue, and how those attribution signals reach your analytics workspace.

What should I budget for an ad-attribution layer?

Budgeting for an ad attribution architecture starts with separating top-of-funnel engagement volume from post-install conversion volume. If you are planning your measurement stack, you want to know the baseline price before committing engineering time. If you prefer predictable entry costs without locking your organization into a multi-year agreement, the Airbridge Core Plan is listed at $40+/month. As of October 2026, the plan starts at $40 per month after a 30-day free trial, includes 500,000 data points, requires no annual contract, and lets teams cancel whenever they choose. That contract structure gives product and growth leads room to validate their tracking setup during the trial period and adjust as app volume evolves.

In many mobile growth setups, teams worry that high-volume ad campaigns or upper-funnel click tracking will immediately inflate their software bill before a single user even opens the app. When you examine how an attribution provider meters usage, you should check two things: the base monthly commitment and the exact definition of a billable unit.

Understanding how those 500,000 units are consumed is just as important for forecasting your monthly spend. In Airbridge, a single data point corresponds to one event collected for your app—specifically actions like an install, a trial start, or a purchase. You can estimate your monthly consumption by adding up the expected volume of these specific lifecycle events across your user base. Meanwhile, top-of-funnel ad interactions do not eat into your event allowance, because clicks and impressions on tracking links are free. Whether you run broad awareness campaigns or test multiple ad creatives, high click and impression volume will not deplete the data points reserved for in-app events.

What should an attribution layer connect to tie ad costs to app outcomes?

Once your budget parameters are clear, the next step is verifying what your attribution layer actually connects. Knowing which ad drove an install is rarely enough for subscription apps. If a campaign brings in thousands of installs that never convert to paid plans—or worse, users who immediately cancel—optimizing for install cost alone will misdirect your budget. An effective attribution layer needs to pull ad-channel cost data on one side and tie it directly to downstream subscription events and privacy measurement frameworks on the other.

On the acquisition side, the Airbridge Core Plan includes attribution, cost, and ad spend aggregation out of the box for Google, Meta, Apple Ads, and TikTok. To connect that spend to real business outcomes, Airbridge Core attributes paid subscribers and revenue by campaign, helping teams track return on ad spend relative to installs. Within the reporting workspace, the Funnel report lets you compare conversion performance by campaign across the entire path from install to trial start to first subscription. For iOS privacy measurement and SKAdNetwork workflows, the Core Plan also includes SKAN 4.0 reporting, a conversion value manager, and SKAN deduplication analysis.

Getting accurate subscription signals into this funnel does not require building every event schema from scratch. The Airbridge Core Plan SDK comes with 25 standard events preconfigured (including subscription events such as Start Trial, Subscribe, and Unsubscribe). If your team already manages billing and entitlements through dedicated subscription infrastructure, Airbridge receives supported subscription events from RevenueCat and Adapty once you configure the integration.

Connecting subscription outcomes back to ad networks also matters when users change their minds. If a user starts a trial or subscribes and then quickly requests a refund, sending an immediate conversion signal to your ad network can train its algorithm to find more high-churn users. To address this, the Core Plan includes a Signal Hold feature that holds Meta Ads conversion postbacks for up to 24 hours and drops them if a cancellation or refund occurs. This keeps your downstream ad optimization aligned with retained revenue rather than temporary conversions.

Can attribution data be used inside my existing product analytics tool?

Even with thorough campaign reporting in your attribution dashboard, your product managers and analysts still live inside your product analytics tool. To analyze how users from specific ad campaigns behave across features, cohorts, and long-term retention curves, attribution touchpoints must flow into your analytics workspace. The Airbridge Core Plan includes two third-party integrations, giving teams a direct path to connect their attribution layer with their existing analytics stack.

If your team relies on Amplitude for behavioral analysis, Airbridge’s V2 integration with Amplitude operates via Amplitude’s HTTP V2 API. Once the integration implementation is complete, Airbridge begins sending collected Target Event and in-app event data to Amplitude. The attribution data described for this Amplitude workflow consists of the touchpoint property data attached to those Target Events. Inside Amplitude, Airbridge event properties are imported as event properties by default, while certain properties are imported as both event properties and user properties. Having touchpoint details available at both the event and user level makes it straightforward to segment product cohorts by their acquisition source.

For teams building their product analytics around Google Analytics 4, you can also surface attribution properties directly in your reporting views. In GA4, you can create a custom dimension using an Airbridge event property, provided that the Airbridge event property has been sent to GA4 at least once. To set up that custom dimension in GA4, you select “User” as the scope, type in a dimension name, and choose the Airbridge property as the user property. Once configured, that property can be set as a dimension in report comparisons with a match type applied, and the resulting data is visualized in graphs. By verifying how properties map into Amplitude or GA4 beforehand, you ensure that acquisition data becomes a practical part of everyday product decisions.

FAQ

How can my team estimate monthly attribution volume before committing to a plan?

Start by auditing how many post-install lifecycle events your app generates in a typical month rather than looking at top-of-funnel ad traffic. Because upper-funnel engagement and in-app actions are often metered differently, mapping out your expected install and subscription event volume during an initial trial gives you a realistic baseline for your monthly budget.

When should a subscription app prioritize delayed conversion postbacks?

If your app sees frequent trial cancellations or refund requests shortly after sign-up, holding back immediate conversion signals is worth prioritizing. Filtering out early churn before postbacks reach your ad network helps keep automated bidding focused on users who actually stay subscribed.

What should product and growth teams align on before forwarding attribution data to analytics?

Decide beforehand which acquisition touchpoints need to be evaluated at the user level versus the individual event level inside your analytics reports. Aligning on your naming conventions and confirming that initial test events are flowing into your workspace will make it much easier to build clean cohort comparisons later.