How Post-Install Event Tracking Works
Post-install event tracking extends the attribution chain beyond the install to capture what users actually do inside your app. While install attribution tells you where a user came from, post-install events tell you whether that user was worth acquiring. The combination of both data points is what transforms mobile marketing from a volume game into a quality game.
The technical implementation starts with the attribution SDK already integrated for install tracking. The same SDK provides methods to log custom events with parameters. When a user completes a purchase, the app calls the SDK's event tracking method with the event name, revenue amount, currency, and any custom parameters relevant to your business. The SDK sends this event to the attribution provider, which associates it with the user's original install attribution data.
This association is what makes post-install tracking powerful. A purchase event on its own is useful for product analytics. A purchase event linked to the campaign, ad network, creative, and keyword that drove the install is transformative for growth. It lets you calculate revenue per install by source, identify which creatives attract paying users versus freeloader, and optimize campaigns toward downstream value rather than upstream volume.
Defining Your Event Taxonomy
The events you choose to track define the lens through which you evaluate campaign performance. A poorly designed event taxonomy either drowns you in noise or leaves critical blind spots. The goal is a focused set of events that map directly to your business model and growth levers.
For subscription apps, the critical events typically follow the activation funnel: registration complete, onboarding finished, free trial started, trial converted to paid, and subscription renewed. Each event represents a deeper level of commitment, and tracking all of them lets you identify exactly where users from different channels drop off. A channel that drives strong trial starts but poor conversions might be attracting deal-seekers rather than genuine prospects.
For e-commerce apps, the event chain centers on purchase behavior: product viewed, added to cart, checkout initiated, purchase completed, and repeat purchase. Revenue parameters attached to purchase events enable ROAS calculation by source. For gaming apps, the focus shifts to engagement depth: tutorial completed, level reached, in-app purchase made, and daily active sessions. Each vertical has its own critical events, and the taxonomy should reflect your specific monetization model rather than copying a generic template.
Sending Events Back to Ad Networks
Post-install events become even more valuable when they flow back to the ad networks running your campaigns. This feedback loop, called postback or server-to-server event forwarding, enables networks to optimize their algorithms toward your actual business outcomes rather than just installs.
When you send purchase events back to Meta, for example, Meta's algorithm learns which user profiles are most likely to not just install your app but actually spend money. Over time, the algorithm shifts its targeting toward these higher-value user profiles, improving your return on ad spend without any manual targeting changes. The same principle applies to Google Ads, TikTok, and every major network, they all perform better when they receive downstream conversion signals.
Linkrunner handles this event forwarding automatically, routing post-install events to the appropriate ad networks based on the install's attribution. When a user attributed to a Meta campaign makes a purchase, Linkrunner sends that purchase event back to Meta via the configured postback URL. This closed-loop system ensures every network receives the optimization signals it needs without requiring your engineering team to build and maintain individual network integrations. The result is better-optimized campaigns across all channels with minimal technical overhead.
Optimizing Campaigns with Post-Install Data
The shift from install-based to event-based optimization is one of the highest-leverage changes a growth team can make. Most teams start by optimizing for installs because it is the simplest metric, more installs means more users. But install volume is a vanity metric if those users never activate, engage, or pay.
Event-based optimization works by setting your campaign objective to a post-install event rather than an install. Instead of telling Meta to find users who will install your app, you tell Meta to find users who will install and then make a purchase within 7 days. The network's algorithm adjusts its targeting accordingly, typically delivering fewer installs at a higher CPI but with significantly better downstream conversion rates. The net result is lower cost per purchase and higher ROAS.
The key to making this work is having enough event volume for the network's algorithm to optimize effectively. Most networks require 50-100 conversion events per week per ad set to exit the learning phase. If your post-install event is too rare, like a high-value subscription that only 2% of users complete, the algorithm will not have enough signal to optimize. In these cases, use a more frequent proxy event that correlates with your ultimate goal. Optimizing toward trial starts rather than paid conversions, for example, gives the algorithm more data while still improving user quality compared to install optimization.
Privacy Considerations and SKAN Events
Post-install event tracking faces the same privacy constraints as install attribution, with additional complexity. On iOS, SKAN's conversion value system is the primary mechanism for communicating post-install behavior in a privacy-preserving way. You get 6 bits (values 0-63) to encode the user's post-install activity into a single number that Apple includes in the attribution postback.
Designing an effective conversion value schema requires careful prioritization. With only 64 possible values, you cannot encode every event. Most teams use a tiered approach: lower values represent early funnel events (registration, onboarding), middle values represent engagement milestones (first session, feature usage), and higher values represent monetization events (purchase, subscription). The schema should be designed so that higher values always represent more valuable users, enabling networks to optimize toward the highest conversion values.
SKAN 4.0 introduced multiple postback windows and coarse conversion values, adding more flexibility but also more complexity. The first postback arrives 24-48 hours after install with fine-grained values. Second and third postbacks arrive later with coarser data. This multi-window approach lets you capture both early activation signals and longer-term engagement patterns, but it requires careful schema design to extract maximum value from each postback window. Teams that invest in thoughtful SKAN conversion value mapping consistently outperform those that treat it as an afterthought.
