You can switch app measurement tools without losing historical data by exporting your raw event data, attribution records, and aggregated reports from your current MMP before you migrate. Most major platforms like Adjust and AppsFlyer allow you to export historical data via their data export APIs or dashboard reports. The migration itself requires careful planning, a parallel tracking period, and a clean SDK swap to avoid gaps in attribution. This article walks through the most common questions teams face when switching measurement tools.
What happens to your historical data when you switch MMPs?
When you switch mobile measurement partners (MMPs), your historical data does not automatically transfer to the new platform. Attribution records, cohort data, and in-app event histories stay within the old platform’s database. What you can preserve depends on how much you export before deactivating your current setup and how your new tool handles imported data.
Most teams are surprised to discover that historical attribution data is not portable in a meaningful analytical sense. Your new MMP starts fresh from the moment its SDK is live. It will not retroactively attribute installs that happened under the previous tool. This means your dashboards will show a hard break in your data timeline, which can complicate performance comparisons across periods.
The practical implication is that you need to treat your old MMP as a data archive, not a system you simply turn off. Before migrating, decide which historical reports matter for future reference and export them in a format your team can actually use, such as CSV exports or connections to a data warehouse like BigQuery or Snowflake.
Which historical data can actually be exported before migrating?
Before switching measurement tools, you can typically export install-level attribution data, in-app event logs, cohort performance reports, aggregated campaign data, and audience segments. The exact scope depends on your current MMP’s export capabilities and your data retention tier. Most enterprise plans on platforms like Adjust and AppsFlyer support raw data exports via API or scheduled data streams.
The most useful exports to prioritise are:
- Raw install and event data at the user level, which lets you rebuild cohort analyses independently
- Aggregated campaign reports covering spend, installs, and downstream events by channel and creative
- Audience lists and retargeting segments if your MMP feeds into ad platforms
- Custom dashboards and report configurations, which you will need to recreate in the new tool
- Postback and integration settings, so you can replicate your ad network connections accurately
One area teams frequently overlook is SKAdNetwork data on iOS. If you are running iOS campaigns, make sure you export your SKAN conversion value mappings and any aggregated SKAN reports before the switch. These configurations are specific to your current setup and will not carry over automatically.
How do you migrate app measurement without a data gap?
To migrate app measurement without a data gap, run both your old and new MMP SDKs in parallel for a defined period, typically two to four weeks, before fully deactivating the old one. This overlap lets you validate that the new setup is tracking correctly while the old system continues to capture data. Only cut over completely once you have confirmed parity.
A clean migration follows this sequence:
- Audit your current tracking setup before writing a single line of new code. Document every event, postback, integration, and custom parameter you are currently tracking.
- Implement the new MMP SDK in a development build and test all event triggers against your documented list.
- Run parallel tracking in production with both SDKs active, comparing event volumes and attribution counts daily.
- Migrate ad network integrations one channel at a time, updating postback URLs and confirming data flow before moving to the next.
- Deactivate the old SDK only after the parallel period confirms the new setup is stable and complete.
The parallel period is the single most effective way to catch configuration errors before they become data gaps. Teams that skip it almost always encounter missing events or broken postbacks that are difficult to diagnose after the fact.
What causes attribution discrepancies after switching measurement tools?
Attribution discrepancies after switching measurement tools are most commonly caused by differences in attribution windows, fingerprinting policies, probabilistic matching logic, and how each platform handles view-through attribution. Even when two MMPs measure the same campaigns, their methodologies can produce different install counts for the same period.
Other frequent sources of discrepancy include:
- Different default lookback windows: One platform may default to a 30-day click window while another uses 7 days, which changes which installs get attributed to paid channels versus organic.
- SKAdNetwork handling differences: On iOS, each MMP interprets SKAN postbacks slightly differently, which affects how installs are reported by campaign.
- Postback timing mismatches: If ad network postbacks are not updated simultaneously with the SDK swap, some installs may be reported to the old MMP while others go to the new one.
- Reattribution logic: Platforms differ in how they handle returning users, which can inflate or deflate reattribution numbers during the transition period.
Accepting a degree of discrepancy during migration is normal. The goal is not perfect parity but a stable, well-documented baseline in the new platform from which you can measure forward.
Should you re-attribute existing users after an MMP switch?
Re-attributing existing users after an MMP switch is generally not recommended and often not technically possible. Your new MMP will not have the historical click and impression data needed to retroactively assign attribution to users who installed before the migration. Attempting to force re-attribution can corrupt your new platform’s data and distort lifetime value calculations.
What you can do is segment your user base clearly by install date relative to the migration cutover. Users who installed before the switch are best analysed using exported data from the old MMP. Users who install after the cutover are tracked cleanly in the new system. Keeping these cohorts separate in your reporting prevents you from mixing attribution methodologies, which would make campaign performance analysis unreliable.
If retargeting existing users is part of your strategy, focus on rebuilding those audience segments in the new MMP using current in-app event data rather than trying to import historical attribution labels.
How long does a full app measurement migration take?
A full app measurement migration typically takes between four and twelve weeks from planning to complete cutover, depending on the complexity of your current tracking setup, the number of integrated ad networks, and how quickly your development team can implement and test the new SDK. Simpler setups with fewer integrations can move faster; enterprise apps with many custom events and multiple channels take longer.
The timeline breaks down roughly as follows:
- Weeks 1 to 2: Audit your current setup, document all events and integrations, and select your new MMP configuration
- Weeks 2 to 4: Implement the new SDK in development and QA, test all events, and prepare ad network postback updates
- Weeks 4 to 8: Run parallel tracking in production, monitor for discrepancies, and migrate integrations channel by channel
- Weeks 8 to 12: Confirm stability, deactivate the old SDK, and set up your new reporting baseline
The audit phase is where most teams underestimate the time required. An inherited tracking setup often contains redundant events, broken postbacks, and undocumented custom parameters that need to be resolved before migration begins. Skipping a thorough audit almost always adds time later in the process.
If you are evaluating whether your current setup is still fit for purpose or considering a switch between tools like AppsFlyer and Adjust, our app growth stack services cover exactly this kind of measurement architecture work. And if you want to talk through your specific situation with someone who has done this across many apps and verticals, you can request a free consultation with the Wuzzon team to get a clear view of what your migration should look like.
Frequently Asked Questions
Can I use my old MMP and new MMP data together in a single dashboard or data warehouse?
Yes, and this is actually the recommended approach for maintaining analytical continuity. By exporting your historical data from the old MMP into a central data warehouse like BigQuery or Snowflake, and then piping your new MMP’s data into the same destination, you can query across both datasets in a unified environment. Just make sure you clearly label the data source and attribution methodology for each period so you are never mixing the two without context.
What should I do if I discover broken or missing events during the parallel tracking period?
Treat every discrepancy as a blocking issue and trace it back to its root cause before proceeding with the migration. Common culprits include misconfigured event parameters, SDK initialisation order conflicts, or postback URLs that were not updated correctly. Fix and re-test each broken event in your development environment first, then redeploy and re-validate in production before continuing the parallel tracking period. Do not advance to the cutover phase until all critical events show consistent parity between both platforms.
How do I handle in-flight campaigns during the MMP switch to avoid losing attribution data mid-campaign?
The safest approach is to update your ad network postback URLs at the campaign level rather than all at once, and to time the switch between major campaign flights rather than during a peak spend period. During the parallel tracking window, keep both sets of postback URLs active where your ad networks support it, so installs from ongoing campaigns are captured by both systems. Once you confirm the new MMP is receiving and attributing correctly, deactivate the old postbacks channel by channel rather than all at once.
Is there a risk of double-counting installs if both SDKs are running at the same time during the parallel period?
Running two MMP SDKs simultaneously does not cause double-counting in your ad networks as long as you manage your postback routing carefully. Each MMP should be configured to send postbacks to separate endpoints, and your ad networks should only receive install confirmations from one MMP at a time per campaign. The parallel period is for internal validation purposes only — use it to compare event volumes, not to send duplicate attribution signals to your media partners.
What is the biggest mistake teams make when switching MMPs, and how can I avoid it?
The most damaging mistake is deactivating the old MMP before completing a thorough data export and validating the new setup in production. Teams under time or budget pressure often cut the old platform too early, only to discover missing event configurations or broken integrations that are now impossible to backfill. Avoid this by treating the old MMP as a read-only archive that stays accessible for at least 30 days after the new platform goes live, and by making data export a mandatory step before any SDK work begins.
Do I need to update my privacy and data processing agreements when switching MMPs?
Yes, switching MMPs typically requires updating your data processing agreements (DPAs), privacy policy references, and any consent management platform (CMP) configurations that name your measurement provider. Your new MMP will need to be listed as a data processor in your privacy documentation, and if you operate in regions covered by GDPR, CCPA, or similar regulations, you should confirm that the new provider’s data handling practices meet your compliance requirements before going live. Involve your legal or compliance team early in the migration planning process to avoid delays at launch.
How do I set a reliable performance baseline in my new MMP after the migration is complete?
Define your baseline using the first full 30-day period after the old SDK has been fully deactivated, since this is the first clean window where all attribution is flowing exclusively through the new platform. Document the attribution window settings, reattribution logic, and any custom configurations you have applied, so future performance changes can be attributed to actual campaign shifts rather than methodology differences. It also helps to run a retrospective comparison between your exported historical data and the new platform’s first month to sanity-check that your event taxonomy and channel mapping are consistent.
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