What Conversion Values Are
A conversion value is a compact numeric signal that communicates the quality of a user conversion back to an ad network or attribution system. In the simplest terms, it answers the question: "This user installed the app, but how valuable are they?" The conversion value encodes post-install behavior into a number that ad networks can use to optimize campaign delivery toward higher-quality users.
The concept existed before SKAdNetwork, but SKAN made it a critical component of iOS measurement. In the pre-ATT world, attribution providers could send detailed post-install event data, every purchase amount, every level completed, every subscription started, back to ad networks using the IDFA. With SKAN, all of that rich behavioral data must be compressed into a single conversion value that Apple's framework transmits in a privacy-preserving postback.
On Android, conversion values play a similar role in the Privacy Sandbox Attribution Reporting API, where they encode conversion quality in event-level and aggregate reports. Outside of privacy frameworks, conversion values are also used in server-to-server postback configurations where advertisers send numeric quality signals to ad networks to improve optimization. The underlying principle is universal: give the ad network a signal about user quality so it can find more users like your best ones.
SKAdNetwork Conversion Value Mechanics
SKAdNetwork's conversion value system is built around a 6-bit integer, giving you 64 possible values (0 through 63). When a user installs your app and opens it for the first time, a measurement window begins. During this window, your app can set and update the conversion value as the user completes meaningful actions. Each update must be to a higher value than the current one, you cannot decrease the conversion value once set.
The measurement window operates on a timer system. The initial window is 24 hours from first app open. Each time you update the conversion value, the timer resets for another 24 hours. This means that if a user completes a valuable action on day 2, you can still capture it by updating the value, which extends the window to day 3. However, there is a practical limit, Apple caps the total measurement window, and longer windows delay the postback delivery, which delays your campaign data.
SKAN 4.0 introduced a tiered system with three postback windows. The first window (0-2 days) can include fine-grained conversion values (0-63) if the campaign meets Apple's crowd anonymity thresholds. The second (3-7 days) and third (8-35 days) windows provide coarse values only, low, medium, or high. This tiered approach gives you progressively less granular data over longer time horizons, balancing measurement needs with privacy protection.
Designing a Conversion Value Schema
Your conversion value schema is the mapping between the 64 available values and the user behaviors they represent. This schema is one of the most consequential decisions in your iOS measurement strategy because it determines what signals your ad networks receive for optimization and what data you have for campaign analysis.
There are three common schema approaches. Revenue-based schemas map conversion values to revenue ranges, for example, value 0 means no revenue, value 1-10 maps to $0.01-$1.00 in increments, and higher values represent larger revenue brackets. This approach works well for e-commerce and in-app purchase apps where revenue is the primary optimization target. Engagement-based schemas encode behavioral milestones, registration completed, onboarding finished, first content consumed, first social interaction. This suits apps where engagement predicts long-term retention better than early revenue. Hybrid schemas combine both, using bit-level encoding to represent multiple signals simultaneously within the 64 available values.
Linkrunner helps growth teams design and manage conversion value schemas that maximize the signal quality sent to ad networks. The platform provides tools for mapping post-install events to conversion values, testing schema configurations against historical data, and monitoring how schema changes affect campaign optimization. This is particularly valuable during the iterative process of refining your schema, small changes in how you encode user quality can have outsized effects on how ad networks optimize delivery.
Optimizing Campaigns With Conversion Values
The primary purpose of conversion values is campaign optimization. When an ad network receives a postback with a high conversion value, it learns that the campaign, ad group, and creative that drove that install are producing valuable users. The network's machine learning models use this signal to find more users with similar characteristics, gradually shifting delivery toward audiences that are more likely to generate high conversion values.
This optimization loop is only as good as the signal you provide. If your conversion value schema does not meaningfully differentiate between high-value and low-value users, the ad network cannot optimize effectively. A schema that assigns value 1 to "app opened" and value 2 to "spent $500" wastes 62 values and provides almost no gradient for the optimization algorithm to work with. Conversely, a well-designed schema that uses the full range to represent meaningful quality differences gives the network rich signal to optimize against.
Monitor your conversion value distribution regularly. If 90% of your postbacks carry the same conversion value, your schema is not providing useful differentiation. Adjust your thresholds so that values are distributed more evenly across your user base, with clear separation between quality tiers. Also watch for temporal patterns, if your conversion values are consistently lower on weekends or in specific geos, that information can inform your bidding and targeting strategy.
Test schema changes carefully. Switching your conversion value mapping mid-campaign can confuse ad network optimization algorithms that have learned to optimize against your previous schema. When making changes, coordinate with your ad network partners, consider running the new schema on new campaigns first, and allow sufficient time for the algorithms to recalibrate before evaluating performance.
Conversion Values Beyond SKAN
While SKAdNetwork brought conversion values into the spotlight, the concept extends well beyond Apple's framework. On Android, the Privacy Sandbox Attribution Reporting API uses trigger data and conversion values in both event-level and aggregate reports. The mechanics differ, Android's system supports richer conversion data in aggregate reports, but the strategic challenge is the same: encode user quality into a compact signal that enables campaign optimization.
Server-to-server postback configurations also rely on conversion values. When you send a postback to an ad network reporting that a user completed a purchase, the revenue amount functions as a conversion value. The network uses this data to optimize toward users likely to generate similar revenue. The difference from SKAN is that S2S postbacks can carry richer data, exact revenue amounts, event names, user properties, because they operate outside of Apple's privacy framework and typically involve consented or first-party data.
Looking ahead, conversion values will become more important as the industry moves further toward privacy-preserving measurement. The ability to compress complex user behavior into meaningful quality signals, and to design schemas that maximize the optimization value of limited data, is becoming a core competency for growth teams. Teams that master conversion value strategy will extract more performance from their campaigns even as the underlying measurement infrastructure becomes more constrained.
