How to Export and Use MMP Data for Analysis (2025)

Your MMP's dashboards are useful for daily monitoring, but they can't answer every question.

When you need to calculate custom LTV models, combine attribution data with revenue systems, or run cohort forecasting that goes beyond standard reports, you need raw data exports.

Most MMPs offer multiple export options: CSV downloads, APIs, and data warehouse integrations. Each serves different use cases.

Here's how to extract MMP data and use it for custom analysis that drives better growth decisions.

Why Export MMP Data?

Native MMP dashboards have limitations:

Limited historical access: Most MMPs retain raw data for 90 days to 1 year Fixed metric definitions: You can't create custom calculations beyond what the MMP supports Siloed data: Attribution data lives separately from revenue, product analytics, and customer data No predictive modeling: Advanced forecasting requires raw data and custom algorithms

Exporting data solves these limitations by giving you full control over storage, calculation, and analysis.

Export Methods: CSV vs API vs Data Warehouse

CSV Exports: Manual Downloads

Best for:

• One-time analysis
• Small data volumes (<10K rows)
• Non-technical users
• Quick ad-hoc reports

Limitations:

• Manual process (not scalable)
• No real-time updates
• Limited to aggregated data in most MMPs
• No automation

How it works:

Log into your MMP, configure report parameters (date range, metrics, dimensions), and download as CSV. Open in Excel, Google Sheets, or upload to a BI tool.

API Access: Programmatic Data Pulls

Best for:

• Automated reporting pipelines
• Regular scheduled updates
• Medium data volumes
• Integration with custom dashboards

Limitations:

• Requires engineering resources
• Rate limits on API calls
• Still requires data storage infrastructure on your end

How it works:

Use your MMP's reporting API to fetch data programmatically. Schedule scripts (Python, Node.js) to pull data daily or weekly and load into your database or BI tool.

Data Warehouse Integration: Direct Connection

Best for:

• Large-scale apps with high event volumes
• Real-time or near-real-time analysis
• Joining attribution data with other business data
• Long-term historical storage

Limitations:

• Requires data warehouse infrastructure (BigQuery, Redshift, Snowflake)
• Higher cost (warehouse storage fees)
• More complex setup

How it works:

Configure your MMP to send raw event data directly to your data warehouse. Events flow continuously, giving you real-time access to install, click, and in-app event data.

Platform-Specific Export Options

AppsFlyer Data Export

CSV/Excel Reports:

Configure custom reports with any combination of metrics and dimensions. Export up to 1M rows per report.

Pull API:

Fetch aggregated reports programmatically. Supports filters, grouping, and custom time ranges.

Data Locker:

AppsFlyer's data warehouse integration sends raw event data to:

• Google BigQuery
• Amazon S3
• Azure Blob Storage

Pricing:

Data Locker is an add-on available on Premium and Enterprise plans.

Adjust Data Export

CSV Reports:

Export custom reports directly from dashboard. Supports all standard metrics and dimensions.

Reporting API:

Programmatic access to aggregated data. Rate limits: 50 requests per minute.

Raw Data Export:

Adjust sends raw install and event data to:

• Google BigQuery
• Amazon Redshift
• Snowflake
• S3

Pricing:

Raw data export requires Enterprise plan.

Branch Data Export

Dashboard Exports:

Export reports to CSV from dashboard.

Data Feeds API:

Stream raw event data to your warehouse:

• Google BigQuery
• Amazon Redshift
• Webhook endpoints

Query API:

Fetch aggregated analytics data programmatically.

Pricing:

Data feeds available on Growth and Enterprise plans.

Setting Up Data Warehouse Integration

Most advanced setups use data warehouse integration for real-time access and unlimited retention.

Step 1: Choose Your Warehouse

Google BigQuery:

• Best for: Apps already using Google Cloud Platform
• Pricing: Pay per query (~$5 per TB processed)
• Pros: Fast, serverless, integrates with Looker and Data Studio

Amazon Redshift:

• Best for: Apps using AWS infrastructure
• Pricing: ~$0.25/hour for small clusters
• Pros: Powerful SQL, integrates with Tableau and other BI tools

Snowflake:

• Best for: Multi-cloud setups or advanced analytics needs
• Pricing: Pay per compute and storage
• Pros: Fast, scalable, best-in-class performance for large datasets

Step 2: Configure MMP Integration

Each MMP provides setup documentation, but the process is similar:

1. Create warehouse credentials: Generate API keys or service account access
2. Configure MMP data export: Input warehouse connection details in MMP settings
3. Define data schema: Select which events and fields to export
4. Test connection: Send test data to verify setup
5. Enable continuous export: Turn on real-time data flow

Step 3: Schedule Data Pipelines

Once data flows to your warehouse, create SQL-based pipelines to:

• Clean and transform raw data
• Calculate custom metrics (LTV, cohort retention, payback period)
• Join with revenue, product, and customer data
• Create aggregated tables for BI tools

Most teams use tools like dbt (data build tool) to manage transformation logic.

Common Data Export Use Cases

Use Case 1: Custom LTV Modeling

MMP dashboards show estimated LTV, but you can build more accurate models with raw data.

Steps:

1. Export install-level data with attribution source
2. Join with revenue events from your payment processor
3. Calculate actual revenue per user by cohort
4. Build regression models to predict future LTV based on early behavior

Why it matters:

Standard LTV estimates often miss nuances like subscription churn timing, upsell revenue, or platform-specific monetization differences.

Use Case 2: Contribution Margin Analysis

CPIs and ROAS don't account for platform fees, processing costs, or COGS.

Steps:

1. Export campaign spend and attributed revenue
2. Subtract Apple/Google fees (15-30%), payment processing (2-3%), COGS
3. Calculate true contribution margin by campaign

Why it matters:

A campaign with 2.0x ROAS might have negative contribution margin after fees. You need to see true profitability, not just top-line revenue.

Use Case 3: Multi-Touch Attribution

MMPs typically use last-click attribution. For longer consideration cycles, multi-touch is more accurate.

Steps:

1. Export all click/impression events per user
2. Build custom attribution models (linear, time decay, position-based)
3. Allocate credit across multiple touchpoints

Why it matters:

In categories with long research cycles (B2B, high-ticket items), users might engage with 5-10 touchpoints before converting. Last-click under-credits early awareness campaigns.

Use Case 4: Cohort Forecasting

Forecast future revenue based on historical cohort performance.

Steps:

1. Export cohort-level retention and revenue curves
2. Fit statistical models to historical data
3. Project future revenue for recent cohorts based on observed patterns

Why it matters:

Supports budget planning, fundraising, and strategic decision-making with data-backed projections.

Use Case 5: Cross-Platform User Journey Analysis

Understand how users move between web, iOS, and Android.

Steps:

1. Export attribution data from MMP
2. Join with web analytics (Google Analytics, Segment)
3. Map user journeys across platforms using hashed email or custom IDs

Why it matters:

Users might discover your product on web, research on iOS, and convert on Android. Single-platform views miss this complexity.

Data Export Best Practices

1. Start With a Clear Use Case

Don't export data "just in case." Define the specific question you're trying to answer first.

If you can't articulate the decision this data will inform, you're probably not ready to invest in custom data infrastructure.

2. Minimize Data Volume

Export only the fields and time ranges you need.

Full raw data exports can be massive (millions of rows per day). If you only need install and purchase events, don't export every in-app event.

3. Implement Data Governance

If you're exporting user-level data, ensure:

• Compliance with GDPR, CCPA, and other privacy regulations
• Proper data access controls (who can query PII)
• Data retention policies aligned with legal requirements
• Anonymization or pseudonymization where possible

4. Version Your Schemas

When you change which fields are exported or how they're calculated, version your schemas.

This prevents breaking downstream analysis when schemas evolve.

5. Document Calculations

Create a data dictionary that explains:

• What each field represents
• How metrics are calculated
• Any transformations applied
• Known data quality issues

This prevents confusion when multiple stakeholders access the same data.

Tools for Working with Exported Data

SQL-Based Analysis

Once data is in a warehouse, SQL is the primary query language.

Common SQL tasks:

-- Calculate LTV by source
SELECT
  media_source,
  COUNT(DISTINCT user_id) as users,
  SUM(revenue) / COUNT(DISTINCT user_id) as ltv
FROM installs
JOIN revenue_events USING (user_id)
WHERE install_date >= '2025-01-01'
GROUP BY media_source;

-- Retention by cohort
SELECT
  DATE_TRUNC('week', install_date) as cohort_week,
  media_source,
  COUNT(DISTINCT CASE WHEN days_since_install >= 7 THEN user_id END)
    / COUNT(DISTINCT user_id) as d7_retention
FROM installs
LEFT JOIN sessions USING (user_id)
GROUP BY cohort_week, media_source;

Python for Advanced Analysis

For statistical modeling, forecasting, or machine learning:

Libraries:

• pandas: Data manipulation
• numpy: Numerical computing
• scikit-learn: Machine learning
• statsmodels: Statistical modeling

Example use case:

Build LTV prediction model using early user behavior signals (session frequency, feature usage) to forecast 180-day LTV after just 7 days.

BI Tools for Visualization

Once data is transformed, visualize in BI tools:

• Looker: Best for teams already using Google Cloud
• Tableau: Powerful visualization, steep learning curve
• Mode Analytics: SQL-based reporting with charting
• Metabase: Free, open-source alternative

Automated Data Pipelines

Manually exporting data doesn't scale. Automate data flows.

Daily Export Scripts

Schedule Python or Node.js scripts to:

1. Fetch data via MMP API
2. Transform and clean
3. Load into database or warehouse
4. Send alerts if data quality issues detected

Use cron jobs, Airflow, or cloud functions (Lambda, Cloud Functions) to run on schedule.

Real-Time Event Streaming

For apps with very high event volumes, use MMP webhook integrations to stream events in real-time to:

• Kafka
• Kinesis
• Pub/Sub

This enables real-time dashboards and anomaly detection.

Data Quality and Validation

Always validate exported data against MMP dashboards.

Common Data Quality Issues

Duplicates: Events sent multiple times Missing data: API rate limits or connection issues cause gaps Schema changes: MMP updates field names or formats without notice Time zone inconsistencies: Exports use different time zones than dashboards

Validation Checks

Run daily checks:

-- Compare export totals to MMP dashboard
SELECT
  DATE(event_time) as date,
  COUNT(*) as install_count
FROM installs
WHERE DATE(event_time) = CURRENT_DATE - 1
GROUP BY date;

-- Check for duplicates
SELECT
  user_id,
  COUNT(*) as install_count
FROM installs
WHERE DATE(event_time) = CURRENT_DATE - 1
GROUP BY user_id
HAVING COUNT(*) > 1;

If exported totals don't match dashboard totals within 5%, investigate.

Cost Considerations

Data exports aren't free.

MMP Costs

• CSV exports: Usually free
• API access: Free or included in most plans
• Data warehouse integrations: Often requires Premium or Enterprise tier ($500-$5,000+/month)

Infrastructure Costs

• BigQuery: ~$5 per TB queried + storage costs ($0.02/GB/month)
• Redshift: $180-$1,000+/month for small to medium clusters
• Snowflake: Pay per compute (queries) + storage

For apps with <1M events/day, costs typically run $50-$300/month. For apps with >10M events/day, expect $1,000-$5,000/month.

When to Invest in Data Export Infrastructure

Not every app needs custom data infrastructure.

Export data if:

• Your MMP's native reports can't answer critical business questions
• You need to join attribution data with revenue or product data
• You're building custom LTV models or forecasting tools
• You need data retention >1 year
• Multiple teams need flexible access to raw data

Stick with native dashboards if:

• Standard reports meet all your needs
• Your team lacks engineering resources for data infrastructure
• Event volumes are low (<100K/month)
• Budget is limited

FAQs

What's the best way to export data from my MMP?

For one-time analysis, use CSV exports. For regular reporting, use scheduled API pulls or data warehouse integrations. Large-scale apps should implement data warehouse connections (BigQuery, Redshift, Snowflake) for real-time access to raw event data.

How long does MMP data retention last?

Most MMPs retain aggregated data for 2 years and raw event data for 90 days to 1 year depending on plan tier. For longer retention, export to your own data warehouse where you control retention policies.

Can I export user-level data from my MMP?

Yes, but it depends on your MMP plan and privacy settings. Raw data exports include device-level attribution and event data. Ensure you comply with GDPR, CCPA, and other privacy regulations when exporting and storing user-level data.

How much does data warehouse integration cost?

MMP fees for data warehouse integrations range from $500-$5,000/month depending on plan tier. Add warehouse infrastructure costs ($50-$5,000/month) based on data volume. Total costs for most apps: $100-$2,000/month.

What if my exported data doesn't match my MMP dashboard?

Validate time zones, date ranges, and metric definitions. Exported data may use UTC while dashboards use local time. Some metrics (like retention) calculate differently in raw exports vs aggregated reports. Check MMP documentation for calculation details.

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Exporting MMP data unlocks custom analysis that goes beyond standard dashboards. Whether you're building advanced LTV models, combining attribution with revenue data, or creating custom forecasts, data exports give you the flexibility to answer questions that matter most to your specific business.