You're spending ₹2.5 lakh per month on attribution. Your legacy MMP contract is up for renewal at a 40% price increase. Your engineering team is stretched thin. And the thought of migrating platforms while maintaining historical data, live campaigns, and accurate attribution feels like trying to change tyres on a moving car.

This is the reality for hundreds of mobile marketing teams right now. The fear of losing historical data, breaking live campaigns, or creating attribution gaps keeps teams locked into expensive platforms long after they've stopped getting value.

Here's what most teams don't realise: a well-executed MMP migration takes 2 to 4 weeks, preserves your critical historical data, and can be completed with minimal engineering effort if you follow the right process. The real risk isn't migration itself but staying on platforms that are draining your budget without delivering proportional value.

This playbook walks you through every phase of an MMP migration, from realistic timeline expectations to specific validation steps that ensure your new platform is tracking accurately before you shut down the old one.

Migration Reality Check: Timeline, Effort, and Risk Assessment

For any enterprise, before you start planning spreadsheets and stakeholder meetings, understand what you're actually signing up for.

Realistic Timeline Expectations

A standard MMP migration from platforms like AppsFlyer, Branch, or Adjust to a modern alternative typically follows this timeline:

• Week 1: Historical data export, event mapping, SDK integration

• Week 2: Parallel tracking validation, postback setup

• Week 3: Attribution accuracy comparison, stakeholder alignment

• Week 4: Full cutover, post-migration validation

The 2 to 4 week range assumes you have dedicated engineering support (8 to 12 hours total), clear event taxonomy documentation, and decision authority to move quickly. If you're waiting on procurement approvals, coordinating across multiple teams, or dealing with custom implementation requirements, add another 2 to 3 weeks.

Engineering Effort Required

Here's the actual technical work involved:

1. SDK replacement: 2 to 4 hours (depending on platform complexity)

2. Event mapping validation: 1 to 2 hours

3. Postback configuration: 1 hour per ad network

4. QA and testing: 2 to 3 hours

5. Historical data export and storage: 1 to 2 hours

Total engineering time: 8 to 12 hours spread across 2 to 3 weeks, not the "months of dev work" some vendors suggest.

Risk Assessment Framework

The actual risks you need to manage:

High Risk (Requires Active Mitigation)

• Attribution gaps during cutover if parallel tracking isn't properly configured

• Postback delays causing ad network optimisation disruptions

• Event naming inconsistencies breaking downstream analytics integrations

Medium Risk (Manageable with Planning)

• Historical data formatting incompatibilities

• Stakeholder confusion during dual-platform reporting periods

• Learning curve for new dashboard and reporting workflows

Low Risk (Often Overstated)

• Permanent data loss (if you follow proper export protocols)

• Campaign performance degradation (with parallel tracking validation)

• Unrecoverable attribution errors (modern MMPs handle this well)

The biggest mistake teams make is treating migration as a technical project when it's actually a data continuity project. Your goal isn't to switch tools perfectly, it's to ensure you can still make confident budget decisions throughout and after the transition.

Pre-Migration Phase: Historical Data Export, Event Mapping, and Stakeholder Alignment

This phase determines whether your migration will be smooth or chaotic. Skip these steps and you'll spend months reconciling discrepancies.

Historical Data Export Strategy

Not all historical data is equally valuable. Focus on preserving what actually drives decisions:

Critical Data to Export (30 Days Minimum)

• Install attribution by source, campaign, ad set, creative

• Revenue events by install cohort and attribution source

• ROAS calculations by channel and time window (D0, D7, D30)

• Retention cohorts by acquisition source

• Fraud and rejection data

Nice-to-Have Data (If Available)

• Full clickstream and impression data

• A/B test results and variant performance

• Custom event taxonomies and conversion funnels

• Agency performance benchmarks

Data You Can Skip

• Granular device-level logs beyond 90 days

• Unattributed organic install details

• Deprecated event schemas from old app versions

Request CSV exports from your current MMP covering at minimum the last 30 days of attributed installs and revenue events. For year-over-year comparison needs, export the same calendar periods from the previous year. Store these in a data warehouse or cloud storage (BigQuery, S3, Google Sheets for smaller volumes) with clear date stamping.

If your current MMP charges for data exports or restricts API access, this is exactly why you're migrating. Prioritise the 30 to 90 day window that matches your typical ROAS payback period.

Event Mapping and Taxonomy Alignment

Your new MMP needs to understand the same events your old one tracked. This is where most migrations break.

Create a mapping document with three columns:

1. Current MMP Event Name (e.g., "purchase_completed")

2. New MMP Event Name (should match exactly unless you're fixing naming issues)

3. Revenue Flag (yes/no to ensure revenue events trigger proper postbacks)

Common mapping mistakes to avoid:

• Using different event names for the same action ("checkout_complete" vs "purchase_completed" will break cohort analysis)

• Forgetting to map revenue currency and value parameters

• Assuming event parameters will automatically carry over (they won't)

• Not documenting custom conversion events used in SKAN configuration

If you're moving to a platform with better event taxonomy tools, this is your opportunity to clean up years of inconsistent naming. But do it deliberately, not accidentally.

Stakeholder Alignment and Communication

Your marketing team, finance team, and executives all rely on attribution data for different decisions. Prepare them for what's changing and what's not.

Key Messages for Each Stakeholder Group

Marketing Team: "You'll see two sets of numbers for 2 to 3 weeks during parallel tracking. This is expected. We're validating the new platform matches reality before we fully cut over. Your campaign optimisation workflows won't change."

Finance Team: "Historical ROAS data will remain accessible through exported reports. The new platform will maintain the same revenue attribution logic, and we'll provide side-by-side comparison reports during the transition."

Executives: "We're reducing attribution costs by X% while improving dashboard usability and data accuracy. The migration timeline is 3 to 4 weeks with no disruption to live campaigns."

Document your current attribution windows, ROAS calculation methodology, and cohort definitions before migration. These should remain consistent in your new platform unless you have a specific reason to change them. When evaluating platforms, understanding when to adopt an MMP helps clarify which capabilities actually matter for your team's decision-making needs.

Parallel Tracking Phase: Running Old and New MMPs Simultaneously

This is the validation safety net that prevents attribution disasters. You're not switching platforms yet, you're proving the new one works before you commit.

SDK Integration Without Disrupting Current Tracking

Modern MMP SDKs are designed to coexist. Install the new SDK alongside your existing one without removing the old tracking. This lets you compare attribution side-by-side for the same events.

Implementation Checklist

1. Add new MMP SDK dependency to your app (React Native, Flutter, native iOS/Android)

2. Initialise both SDKs in your app startup sequence

3. Fire identical events to both platforms using your existing event triggers

4. Verify both SDKs are receiving attribution data in their respective dashboards

Total implementation time: 2 to 4 hours for most apps.

The key insight here is that you're not migrating yet, you're validating. If the new platform shows significantly different numbers in the first 48 hours, you can investigate before your old MMP contract expires.

What to Track During Parallel Phase

For 7 to 14 days, monitor these key metrics across both platforms:

Install Attribution Accuracy

• Total attributed installs (should match within 5% after accounting for attribution window differences)

• Install breakdown by source (Meta, Google, TikTok, organic)

• Click-to-install time distributions

Revenue Event Tracking

• Revenue event volume and value by source

• ROAS calculations by channel (D0, D7, D30 windows)

• High-value conversion events (purchases, subscriptions, etc.)

Discrepancy Flags to Investigate

• More than 10% difference in total attributed installs suggests attribution window or model mismatches

• Revenue event discrepancies over 15% often indicate event mapping errors

• Missing campaigns in the new platform mean postback setup isn't complete

The goal isn't perfect alignment (different MMPs use slightly different methodologies), but you should see directional consistency. If Meta shows 1,000 installs in your old MMP and 1,200 in your new one, investigate. If it shows 1,000 vs 1,050, that's within normal variance.

Parallel Tracking Cost Management

Yes, you're paying for two platforms during this phase. Budget for it.

If your old MMP charges per install, expect roughly a 2 to 3 week overlap cost. If you're migrating to platforms with more transparent pricing structures like Linkrunner (₹0.8 per attributed install with no setup fees), the overlap cost is predictable and far lower than the annual savings you'll capture.

Validation Phase: Comparing Attribution Accuracy Between Systems

You've been running parallel tracking for a week. Now comes the critical analysis that determines whether you proceed with full migration.

Building the Comparison Dashboard

Create a simple comparison spreadsheet or dashboard with these views:

Daily Attribution Comparison

• Date | Old MMP Installs | New MMP Installs | Variance % | Notes

Source-Level Breakdown

• Source | Old MMP | New MMP | Variance % | Investigation Status

Revenue Validation

• Date | Old MMP Revenue Events | New MMP Revenue Events | Variance %

Pull data directly from both platforms' APIs or dashboard exports. Don't rely on screenshots or manual counts.

Acceptable Variance Thresholds

Here's what experienced migration teams consider normal vs concerning:

Normal Variance (Proceed with Migration)

• 5% to 10% difference in total attributed installs

• 8% to 12% difference in revenue event counts

• Minor discrepancies in organic vs paid split (different fingerprinting logic)

Investigate Before Proceeding

• More than 15% variance in paid channel attribution

• More than 20% variance in revenue event tracking

• Entire campaigns missing from new platform

• Consistent directional differences (new platform always 20% higher or lower)

Red Flags (Pause Migration)

• 30%+ variance in any major channel

• Revenue events not appearing in new platform at all

• Attribution windows configured incorrectly

• SKAN postbacks not being received

Common Discrepancy Causes and Fixes

Issue: New MMP shows 20% fewer installs from Meta

Likely Cause: Attribution window mismatch. Old MMP using 7-day click, new MMP using 1-day click.

Fix: Align attribution windows in new MMP settings to match old platform methodology.

Issue: Revenue events appear in old MMP but not new one

Likely Cause: Event mapping incomplete or revenue parameter not passed correctly.

Fix: Verify event names match exactly and currency/value parameters are included in SDK calls.

Issue: Organic installs significantly different between platforms

Likely Cause: Different fingerprinting logic or organic definition.

Fix: This is usually acceptable. Focus on paid channel accuracy, which drives budget decisions.

Understanding how inaccurate attribution drains marketing budgets helps contextualise why validation matters so much, even in cases where perfect precision isn't possible.

Postback Migration: Updating Ad Networks Without Campaign Disruption

Your ad networks (Meta, Google, TikTok, etc.) receive conversion data through postbacks. Switching these from your old MMP to your new one is the most delicate part of migration.

Postback Update Strategy

Do this in stages, not all at once:

Week 1: Add New Platform Postbacks (Don't Remove Old Ones Yet)

Configure your new MMP to send postbacks to each ad network. Most modern platforms provide templates or automated setup for major networks. You'll need:

• Ad account IDs

• Postback URLs (provided by ad networks)

• Event mapping (which MMP events trigger which network conversion events)

• Attribution windows that match your platform settings

Week 2: Validate Dual Postbacks

For 5 to 7 days, both your old and new MMPs are sending postbacks. Check ad network dashboards to confirm:

• Conversion events are appearing

• Event counts roughly match between old and new sources

• Campaign optimisation algorithms aren't disrupted

Week 3: Remove Old Platform Postbacks

Once you've confirmed the new platform's postbacks are working correctly, remove the old MMP's postback configurations from your ad networks. This prevents double-counting and ensures clean data.

Network-Specific Considerations

Meta Ads

• Use Events Manager to add new conversion API endpoint

• Validate event match quality score remains consistent

• Monitor campaign delivery for 48 hours after switch

Google Ads

• Configure new conversion tracking in Google Ads interface

• Use parallel tracking to avoid disrupting Smart Bidding

• Expect 3 to 5 day learning period as algorithm adapts

TikTok Ads

• Update event tracking in TikTok Events Manager

• Verify SKAN postbacks if running iOS campaigns

• Check campaign optimisation goals haven't reset

Impact on Campaign Performance

When executed properly, postback migration causes minimal disruption. Expect:

• 2 to 3 day "learning period" as ad networks adjust to new data source

• Temporary CPA fluctuations of 10% to 15% (usually stabilises within a week)

• No long-term performance degradation if attribution accuracy is maintained

The common fear is that switching postbacks will "reset" your ad account optimisation. This hasn't been true since 2019. Modern ad networks are resilient to tracking changes as long as the underlying conversion data remains consistent.

Post-Migration Validation

You're ready to fully cut over when you've completed parallel tracking validation and confirmed postback accuracy.

Day 1 Post-Migration Monitoring

Your first 24 hours after removing the old SDK require active monitoring:

Immediate Checks (First 6 Hours)

• Verify new MMP receiving install events in real-time

• Confirm ad network conversion events appearing in platform dashboards

• Check that revenue events are triggering correctly

• Monitor any error logs in SDK implementation

Day 1 Close (24 Hours After Cutover)

• Compare install volume to previous day (should be within normal variance)

• Verify ROAS calculations match expected methodology

• Check for any missing campaigns or sources

• Confirm stakeholder reporting isn't showing gaps

30-Day Post-Migration Review

After a month on your new platform, conduct a formal migration retrospective:

Metrics to Review

1. Attribution Accuracy: Compare attributed install volumes pre-migration vs post-migration by channel. Variance should be < 10% when accounting for seasonal trends.

2. Revenue Tracking Reliability: Review revenue event capture rate. Did any high-value events stop tracking? Are cohort ROAS calculations directionally consistent?

3. Team Adoption: Survey marketing team on dashboard usability, reporting workflows, and any friction points with new platform.

4. Cost Savings Realised: Calculate actual platform cost reduction. If you migrated from a platform charging ₹3 to ₹8 per install to one charging ₹0.8 per install, quantify monthly savings.

5. Unexpected Issues: Document any attribution gaps, data quality problems, or integration challenges that emerged during migration.

Success Criteria

A successful migration delivers:

• Attribution accuracy within 10% of pre-migration baseline

• Zero gaps in critical conversion tracking (purchases, subscriptions, key events)

• Improved dashboard usability and reporting speed

• 40% to 70% reduction in platform costs

• Team confidence in new platform data for budget decisions

If you're hitting these targets, the migration succeeded. If attribution accuracy degraded or you're still manually reconciling data in spreadsheets, something went wrong in the validation phase.

Frequently Asked Questions

How long does MMP migration really take?

2 to 4 weeks for a standard migration with dedicated engineering support. Add another 2 to 3 weeks if you need procurement approval, custom integration work, or are coordinating across multiple teams. The timeline depends more on internal coordination than technical complexity.

Will I lose historical attribution data when I switch MMPs?

No, if you export properly. Before cancelling your old MMP, export 30 to 90 days of attributed installs, revenue events, and ROAS calculations as CSVs. Store these in a data warehouse or cloud storage. The data remains queryable for year-over-year comparisons even after your old platform access ends.

Can I run two MMPs at the same time without doubling costs?

Yes, during the 2 to 3 week validation period. Both SDKs track the same events, and you're only paying each platform for their respective service. If you're migrating to a more affordable platform, the overlap cost is minimal compared to annual savings. Budget for roughly 3 weeks of dual platform fees.

How do I update ad network postbacks without breaking campaigns?

Add new platform postbacks first without removing old ones. Run dual postbacks for 5 to 7 days to confirm conversion events are flowing correctly. Once validated, remove old platform postbacks. Ad networks handle this transition smoothly, though expect a 2 to 3 day learning period as algorithms adjust.

What's the biggest risk in MMP migration?

Attribution gaps during cutover if you don't run parallel tracking. Teams that skip validation and go straight to cutover often discover attribution discrepancies too late to fix cleanly. The mitigation is simple: validate for 7 to 14 days before fully switching.

How much engineering time does SDK migration require?

8 to 12 hours total spread across 2 to 3 weeks. This includes SDK installation (2 to 4 hours), event mapping (1 to 2 hours), postback configuration (1 hour per network), and QA testing (2 to 3 hours). It's far less than most teams expect.

Should I migrate during a peak season or wait for a quiet period?

Wait for a quiet period if possible. Migrating during Black Friday, holiday shopping, or major campaign launches adds unnecessary risk. Choose a period with predictable traffic patterns where you can easily spot attribution anomalies.

How do I compare year-over-year data if I switched MMPs mid-year?

Document methodology differences (attribution windows, organic definitions, event taxonomy) and present comparisons as directional trends rather than precise metrics. Export bridge period data showing both platforms tracking the same events for calibration.

Making Migration Work for Your Team

MMP migration isn't technically complex, it's operationally precise. The teams that succeed treat it as a data continuity project, not a vendor swap. They validate thoroughly during parallel tracking, preserve critical historical context, and communicate timeline expectations clearly to stakeholders.

The actual technical work (SDK integration, postback configuration, event mapping) takes 8 to 12 engineering hours. The validation work (comparing attribution accuracy, investigating discrepancies, confirming revenue tracking) takes 2 to 3 weeks of monitoring. The outcome, when executed properly, is 40% to 70% cost reduction with equal or better attribution accuracy.

If you're currently evaluating alternatives to expensive legacy MMPs, comparing AppsFlyer alternatives for Indian mobile marketers provides a framework for assessing which platforms actually deliver the capabilities you need without unnecessary complexity.

Migration to platforms like Linkrunner typically completes faster than legacy MMP transitions because event mapping wizards and automated postback setup reduce engineering dependencies. Teams report 2-week implementation timelines compared to 4 to 8 weeks for traditional enterprise MMPs, primarily due to simplified SDK architecture and clearer documentation.

The decision to migrate comes down to a simple calculation: is the annual cost of your current platform justified by the value it delivers? If you're spending ₹2.5 lakh per month on attribution while struggling with complex dashboards, restricted data exports, and slow support, the migration risk is far lower than the cost of staying put.

If your team is ready to explore migration options with transparent pricing and faster implementation, request a demo from Linkrunner to see how the platform handles attribution, deep linking, and cross-channel tracking in a unified dashboard built for mobile marketers who need clarity, not complexity.