Your MMP says you drove 12,000 installs last month at ₹95 CPI. Meta's dashboard shows 11,200 installs at ₹102 CPI. Google claims 12,800 installs at ₹88 CPI. When your CFO asks which number is real and whether those campaigns were profitable, you're opening three tools and a spreadsheet instead of giving a straight answer.

The gap between attribution data and actual decisions gets expensive fast—not because the data doesn't exist, but because it lives in five different places that don't talk to each other. This guide walks through why MMP data silos kill unit economics, which metrics actually matter in unified dashboards, and the practical steps to connect attribution, cost, and revenue data so your team can see real ROAS and make faster budget calls.

Why MMP Data Silos Are Destroying Your Unit Economics

Your mobile measurement partner (MMP) tracks installs and in-app events in one place. Your ad platform costs live in Meta Ads Manager and Google Ads. Your revenue data sits in your backend or CRM. When all this data lives separately, you can't actually see if your campaigns are profitable.

Here's what happens in practice: Finance asks for ROAS by channel on Monday morning. You spend Tuesday downloading CSV files from your MMP, pulling spend data from three ad platforms, then matching campaign names in a spreadsheet. By Wednesday afternoon, you finally have an answer—but the data is already two days old, and your team kept spending on campaigns that might be underwater.

The real problem isn't the manual work. It's that your MMP shows 10,000 installs while Meta reports 9,200 and Google says 10,500. Nobody knows which number to trust when deciding where to put tomorrow's budget.

What data silos actually look like:

• You're using VLOOKUP to merge three spreadsheets just to calculate which campaign is profitable

• Your growth team makes budget calls on yesterday's data while burning today's spend

• Marketing says campaigns are hitting 2x ROAS but finance sees different numbers in the P&L

• Attribution reports conflict between tools, so nobody trusts any of the data

The Real Cost of Disconnected Attribution Data

The biggest cost isn't the hours spent building spreadsheets. It's the optimization windows you miss completely.

By the time you notice that a TikTok campaign's Day 7 retention dropped from 45% to 22%, you've already spent ₹80,000 on installs that won't convert to paying users. When you can't quickly see which ad sets drive both volume and revenue, you either underspend on what's working or throw money at campaigns without knowing what made them successful in the first place.

Fragmented data also kills trust between teams. Growth says they're hitting targets. Finance sees the CAC payback period stretching from 90 days to 180 days. The CFO starts questioning every marketing budget request because nobody can produce a single source of truth.

Essential MMP Metrics for Marketing Intelligence Dashboards

A mobile measurement partner (MMP) is a third-party platform that tracks the complete user journey from ad click through install to in-app actions and revenue. Think of it as a neutral referee that tells you which marketing channel actually drove each install and what those users did afterward.

The point of connecting your MMP to marketing intelligence dashboards is to see acquisition cost and user value in the same view. You're trying to answer one question: which campaigns bring in users who actually pay back their acquisition cost?

Campaign Performance Data

This layer shows clicks, impressions, installs, and cost per install (CPI) broken down by campaign, ad set, creative, and channel. It's the top-of-funnel view that tells you what's driving volume and at what efficiency.

When you combine MMP attribution with ad platform cost data in one dashboard, you can see that Meta Campaign A delivers installs at ₹80 while TikTok Campaign B costs ₹150—without exporting anything or matching rows manually. You're looking at the same data your MMP sees, but now it's sitting next to the spend numbers from your ad accounts.

In-App Event Tracking

Post-install actions like sign-ups, purchases, level completions, or subscription starts show user quality instead of just volume. Your MMP tracks when someone completes onboarding or makes their first purchase, then attributes that event back to the specific campaign and creative that drove the install.

A dashboard combining install volume with event completion rates reveals that Campaign A drives 500 installs but only 50 users complete onboarding. Campaign B drives 300 installs but 180 users complete onboarding. Now you know which campaign actually brings quality users, not just download numbers.

Revenue and ROAS Metrics

Attributed revenue per campaign, return on ad spend (ROAS), and customer acquisition cost (CAC) answer the question finance actually cares about: are we profitable?

When your MMP sends revenue events to your BI platform alongside install and cost data, you get real ROAS by channel. You'll see that Google App Campaigns have a 7-day ROAS of 1.8x while influencer campaigns sit at 0.4x. That changes where you allocate budget tomorrow morning.

Retention and LTV Signals

Day 1, Day 7, and Day 30 retention rates show you which campaigns bring users who stick around. Cohort lifetime value (LTV) predicts long-term profitability. Churn patterns tell you when users drop off.

When retention data from your MMP flows into dashboards next to acquisition metrics, you can spot that Android installs from Network X have 60% Day 1 retention versus 25% from Network Y—even though both have similar CPIs. Without connecting this data, you'd keep spending on Network Y because the CPI looks good on paper.

How to Connect Your Mobile Measurement Partner MMP to BI Platforms

Most MMPs offer three ways to get data into your business intelligence (BI) tools: APIs, webhooks, and direct connectors. An API (application programming interface) lets your BI platform pull data from your MMP on a schedule—typically every hour or every few hours. A webhook pushes data from your MMP to your systems the moment an event happens, giving you real-time updates.

Direct connectors are pre-built integrations between your MMP and BI tools like Looker, Tableau, Google Data Studio, or Metabase. If both your MMP and BI tool support a native connector, you can skip the custom setup.

Step 1: Audit Your Current Data Stack

Start by listing every tool that holds a piece of your marketing data. Your MMP tracks attribution. Ad platforms like Meta, Google, and TikTok hold cost data. Your CRM or backend database stores revenue. Your analytics tool tracks user behavior.

Map out where each metric lives. Install counts come from your MMP. Spend per campaign comes from ad platforms. Revenue per user comes from your backend. This audit shows you the gaps before you start connecting systems.

Step 2: Choose API vs Webhook Integration

For most teams starting out, API integration is simpler. You configure your BI tool or data warehouse to pull MMP data every few hours. That's fresh enough for daily optimization decisions without requiring you to set up servers to receive webhook data.

Webhooks make sense if you're running very high-spend campaigns where you want to see performance updates every few minutes. But webhooks require more technical infrastructure—your team has to build endpoints that receive and process the incoming data stream.

Step 3: Build Your Data Pipeline

You have two paths here. First option: connect your MMP directly to your BI tool if both support native integration. Second option: route MMP data through a data warehouse like BigQuery or Redshift first, then connect your BI tool to the warehouse.

The warehouse approach gives you more flexibility to combine MMP data with other sources and clean it up before visualization. Tools like Fivetran or Stitch Data can automate the data flow so you're not manually downloading CSVs every morning. Once data lands in your warehouse or BI tool, you create unified tables that join MMP attribution with ad platform costs and backend revenue.

Step 4: Test Attribution Accuracy

Before trusting your new unified dashboard, run test campaigns and compare numbers across systems. Check that your MMP's install count roughly matches what ad platforms report. Some discrepancy is normal—MMPs filter fraud and use different attribution windows than ad platforms—but huge gaps signal a configuration problem.

Verify that revenue events from your MMP match what your backend systems record. If your MMP shows ₹50,000 in attributed revenue but your finance team sees ₹75,000 in actual transactions, something's misconfigured. Fix it now before your team makes budget decisions based on wrong data.

Dashboard Templates That Actually Move the Needle

The difference between dashboards that sit unused and dashboards that drive decisions comes down to matching the view to the viewer. Growth managers running campaigns daily don't look at the same metrics as a CFO reviewing quarterly performance.

Performance Marketing Dashboard

This is the daily view for whoever's actually running campaigns. It shows yesterday's spend, installs, CPI, and ROAS by channel—Meta, Google, TikTok, Apple Search Ads, and any other networks you're testing.

The dashboard combines MMP attribution with ad platform cost data so you see both sides in one place. When TikTok's CPI jumps from ₹100 to ₹180 overnight, you spot it immediately and pause the campaign before burning another ₹50,000. Key metrics here: spend, installs, CPI, click-to-install rate, and 1-day/7-day ROAS by campaign.

Executive ROAS Dashboard

Founders and finance teams don't want to see 47 campaigns. They want to know if marketing is profitable this month.

This weekly or monthly view shows total spend, attributed revenue, blended ROAS, and LTV:CAC ratio across all channels combined. It pulls MMP revenue data and combines it with financial systems to answer one question: are we growing profitably or just buying vanity installs? When blended ROAS drops below 1.0, it's time to cut spend or fix retention—not scale harder.

Channel Optimization View

This campaign-level breakdown shows which creatives, audiences, and placements drive the best retention and revenue—not just the most installs.

It uses MMP event data and cohort analysis to reveal that video creative A drives 40% more Day 7 retention than image creative B, even though both have similar install volumes. You'll see that broad targeting on Meta brings cheaper installs but interest-based targeting delivers users who actually complete purchases. This view helps you scale what works and kill what doesn't before wasting budget on high-volume, low-value campaigns.

Avoiding MMP Integration Failures That Cost Millions

Even teams with solid technical resources make mistakes when connecting MMPs to dashboards. The mistakes lead to misattributed revenue, wrong budget decisions, and lost trust in the data.

Common integration mistakes:

• Ignoring attribution windows: Your MMP uses a 7-day click and 1-day view window while your ad platforms use different windows, creating discrepancies that make reconciliation impossible. Standardize attribution windows across systems or document the differences so your team knows why numbers don't match perfectly.

• Wrong event mapping: You set up "purchase" events in your MMP but your backend sends "transaction_complete" events, so revenue never gets attributed. Work with your dev team to ensure event names and parameters match exactly between your app and MMP configuration.

• Not accounting for SKAN limitations: iOS SKAN (Apple's privacy-focused attribution framework) provides aggregated, delayed data without user-level detail. Teams treat it like deterministic Android attribution and make bad decisions. Build separate dashboard views for iOS and Android, and understand that iOS campaign optimization works differently post-ATT.

• Over-complicating dashboards: You include 40 metrics in one view because you might need them someday. The dashboard becomes slow to load and impossible to scan. Create role-specific dashboards with only the 6-8 metrics each team actually uses daily.

Automating Real-Time MMP Data Intelligence

Manual dashboard checking works when you're spending ₹50,000 a month. At ₹5 lakh or ₹50 lakh in monthly ad spend, you can't afford to miss the moment when a campaign's performance shifts.

Setting Up API Automation

Configure your BI tool or data warehouse to pull fresh data from your MMP's API every hour or every few hours. This keeps your dashboards current without anyone manually exporting CSVs or clicking refresh.

Most MMPs provide detailed API documentation. Tools like Zapier or custom scripts can handle the scheduled pulls if your BI platform doesn't have native scheduling.

Building Smart Alerts

Set up notifications that fire when key metrics move outside normal ranges. ROAS drops below 0.8x. Install volume spikes 50% above baseline. Day 1 retention falls below 30%. CPI jumps 40% overnight.

Alerts catch problems before they burn significant budget. You can route alerts to Slack, email, or SMS depending on urgency, so your growth team knows immediately when something breaks instead of discovering it in tomorrow's report.

AI-Powered Campaign Insights

Modern MMPs use AI to analyze your attribution data and automatically surface insights you'd miss manually. Instead of spending hours slicing data to find optimization opportunities, AI flags underperforming campaigns and suggests where to shift budget.

For example, AI might surface that Campaign X's retention dropped 15% this week or that iOS installs from Creative B have 2x higher purchase rates than Creative A. Linkrunner's AI analyzes patterns across channels to make it obvious which campaigns to scale and which to cut, turning your MMP from a data repository into an active optimization partner.

Turn Your MMP Into a Revenue Machine

When you unify MMP attribution with marketing intelligence dashboards, you move from guessing about profitability to knowing exactly which campaigns, creatives, and channels drive revenue. The teams that win in mobile app growth don't have better creative or bigger budgets—they have cleaner data and faster feedback loops.

The difference between profitable growth and expensive vanity metrics comes down to seeing the full picture: attribution, cost, events, and revenue in one place, updated constantly, with insights surfaced automatically. No more reconciling screenshots and spreadsheets. No more waiting two days for ROAS reports. No more budget decisions based on partial data.

Linkrunner unifies attribution, deep linking, SKAN, and marketing analytics in one platform built specifically for mobile-first brands. You get accurate attribution across Meta, Google, TikTok, and every other channel, combined with AI that automatically flags what's working and what's burning budget. See how clean, always-on attribution data looks—request a demo.

FAQs About MMP Data Integration

How often should MMP data sync with marketing dashboards?

For daily campaign optimization, syncing every 1-4 hours gives you fresh enough data without overloading your systems. Real-time syncing via webhooks makes sense if you're running very high-spend campaigns where hourly decisions matter, but most teams get more value from stable hourly or twice-daily updates.

What's the difference between MMP attribution and ad platform attribution?

Ad platforms like Meta or Google attribute conversions based on their own tracking pixels and give themselves credit generously, often using longer attribution windows. MMPs act as neutral third parties, tracking the actual install and in-app events on the device, then attributing back to the last click or view before install using standardized windows—giving you unbiased data across all channels.

Which BI tools work best with mobile measurement partner MMP data?

Google Looker Studio (formerly Data Studio), Tableau, Metabase, and Power BI all handle MMP data well if your MMP offers API access or direct connectors. The best choice depends on your team's existing stack and technical comfort—Looker Studio is free and easy for smaller teams, while Tableau and Looker offer more power for complex multi-source analysis.

How do you handle SKAN data in unified dashboards?

SKAN provides aggregated, delayed data without user-level detail, so it can't be combined directly with deterministic Android attribution. Create separate dashboard sections for iOS SKAN campaigns, focus on campaign-level trends rather than user-level cohorts, and use conversion values to estimate revenue ranges instead of exact amounts.