Coarse Conversion Value

What is Coarse Conversion Value?

Coarse Conversion Value (CCV) is a privacy-preserving measurement signal introduced in SKAdNetwork (SKAN) 4 by Apple that classifies app conversion events into three broad tiers: 'high', 'medium', and 'low'. It replaces granular numeric conversion values with aggregated categories, enabling advertisers to gauge campaign performance without exposing individual user behavior. CCV aligns with Apple's data minimization principles while still providing actionable signals for ad optimization and reporting.

How it works

CCV operates within the SKAN 4 postback framework, where conversion windows are organized into priority-ranked slots. Each slot maps to one of three coarse values based on the events it contains.

Priority and Value Assignment

Marketers configure their SKAN settings to assign events to priority slots. The highest-priority slot receives a 'high' coarse value, the second slot receives 'medium', and the lowest slot receives 'low'. For example, a configuration might look like this:

• Priority 3 (highest): bundled add-to-cart and purchase events map to 'high'
• Priority 2: add-to-cart only maps to 'medium'
• Priority 1 (lowest): subscription event maps to 'low'

When a user completes one of these events after installing an app, SKAN records the corresponding coarse value and includes it in the postback sent to the ad network.

Postback Windows and CCV

SKAN 4 introduced three postback windows, extending measurement beyond the first conversion. CCV is included in the second and third postbacks, which cover longer engagement periods after install. The first postback may also return a coarse value when crowd anonymity thresholds are not met, meaning the install volume for a given campaign is too low to qualify for a fine conversion value. This threshold-based behavior protects user privacy by preventing the identification of individuals within small cohorts.

Coarse vs. Fine Conversion Values

Fine Conversion Values (FCV) are 6-bit numeric values (0-63) that allow granular encoding of user actions, such as exact revenue brackets or specific in-app milestones. CCV sacrifices this granularity in exchange for broader postback eligibility. When crowd anonymity conditions are met and a fine value is available, it takes precedence. When conditions are not met or for later postback windows, CCV provides the fallback signal that still enables optimization.

Why it matters

CCV enables mobile marketers to receive meaningful conversion signals across a longer measurement window without violating Apple's privacy framework. Without CCV, campaigns with low install volumes or longer engagement cycles would receive null postbacks, leaving optimization models with no data to act on. By receiving 'high', 'medium', or 'low' signals across the second and third postback windows, marketers can segment audiences by broad engagement quality and reallocate budget toward campaigns driving higher-value user behavior. This is particularly valuable for subscription apps, gaming titles, and e-commerce brands where meaningful conversions occur days or weeks after install. MMPs like Airbridge support SKAN 4 CCV configuration, helping marketers map their key events to priority slots and interpret postback data within a unified attribution dashboard.

How to measure campaign performance using Coarse Conversion Value

Effective use of CCV requires deliberate event mapping and a clear understanding of what each tier represents for your business goals.

Step 1: Define your conversion tiers. Identify the events that represent high, medium, and low value for your business. For an e-commerce app, 'high' might represent a completed purchase, 'medium' an add-to-cart, and 'low' a product view or registration.

Step 2: Configure SKAN priority slots. Map your highest-value events to the top priority slot and less critical events to lower slots. Ensure that the events in each slot are meaningful enough to inform budget decisions at that tier.

Step 3: Align CCV tiers with optimization goals. Communicate to your ad networks which coarse value corresponds to your target action. Networks can use 'high' signals to optimize toward users more likely to complete valuable conversions, improving campaign efficiency over time.

Step 4: Monitor postback window coverage. Track how often your campaigns return coarse versus fine conversion values. A high proportion of coarse values in the first postback may indicate low install volumes, signaling a need to consolidate campaigns or broaden targeting to meet crowd anonymity thresholds.

Step 5: Use an MMP for unified reporting. Tools like Airbridge aggregate SKAN postback data across all three windows, normalizing fine and coarse values into a single reporting view. This allows you to compare campaign performance across networks without manually reconciling postback formats.

Step 6: Iterate on event mapping. As campaign data accumulates, revisit your priority slot configuration. If most conversions cluster at 'medium', consider whether your event hierarchy accurately reflects user value or whether consolidation is needed.

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