You allocated ₹80,000 to Meta campaigns last month. Your MMP reported 12,500 installs. Meta's dashboard showed 14,200. Google Analytics counted 11,800. Finance asked which number to use for CAC calculations, and you couldn't give them a confident answer.

This isn't a hypothetical scenario. It's Monday morning for thousands of mobile marketers managing multi-channel UA programmes. The numbers drift. Postbacks fail silently. Event volumes fluctuate without explanation. And by the time someone notices the discrepancy, three weeks of budget have compounded the error.

Most teams treat attribution data quality as something to "check occasionally" or investigate only when numbers look obviously wrong. But attribution drift doesn't announce itself with alarm bells. It accumulates quietly, week after week, until the measurement foundation you're relying on for budget decisions is 15-20% off reality.

The solution isn't more sophisticated analytics or hiring a dedicated data team. It's a structured 20-minute weekly attribution audit that catches issues before they compound into five-figure budget mistakes.

The Cost of Waiting: How Attribution Drift Compounds Weekly

Attribution inaccuracy has a multiplier effect because marketing decisions build on previous weeks' data. When your Friday budget reallocation meeting relies on last week's ROAS calculations, and those calculations were based on incomplete attribution data, you're not just measuring wrong once. You're optimising in the wrong direction.

Here's what this looks like in practice. A gaming app running ₹200,000 monthly across Meta, Google, and TikTok noticed their reported D7 ROAS had dropped from 85% to 72% over three weeks. They started cutting spend on underperforming campaigns. Two weeks later, during a routine technical review, their engineering team discovered that revenue event postbacks to their MMP had been failing for Android users since a recent SDK update. 30% of their actual conversions weren't being attributed.

The measurement gap cost them ₹18,000 in lost revenue opportunity (budget they cut from actually profitable campaigns) plus another ₹6,000 in tooling costs trying to diagnose the issue retroactively. The failure happened in Week 1. They caught it in Week 5. Every decision in between was based on incomplete data.

Weekly attribution audits prevent this. Not by eliminating every possible measurement issue (that's unrealistic), but by catching the common failure modes before they influence three consecutive budget allocation cycles.

The 20-Minute Weekly Attribution Audit Framework

This audit is designed for Monday mornings, before your weekly performance review. It assumes you're running paid UA across multiple channels (Meta, Google, TikTok, or similar) and using an MMP for attribution tracking. The framework works whether you're spending ₹10,000 or ₹500,000 monthly.

Time yourself. If any single check takes longer than 3-4 minutes, you're either digging too deep (save detailed investigation for later) or your MMP dashboard requires too many clicks to surface basic health metrics.

Week 1-4: Foundational Health Checks

These are your core validation steps. Run all five checks every week for the first month. Once your attribution baseline is stable, you can rotate through them (checking 2-3 each week) unless you spot a red flag.

Check 1: Click-to-Install Match Rate (3 minutes)

Compare reported clicks from ad networks to clicks tracked by your MMP, then validate install attribution rates.

Open your MMP dashboard and pull the past 7 days by channel. You're looking for three numbers:

• Clicks reported by Meta/Google/TikTok in their native dashboards

• Clicks your MMP attributed to those channels

• Click-to-install rate for each channel

Pass criteria:

• MMP-tracked clicks should be 85-95% of ad network reported clicks (some drop-off is normal due to bot filtering and attribution windows)

• Click-to-install rates should stay within ±15% week-over-week unless you changed creative or targeting significantly

• No channel should show zero clicks while the ad network reports active campaigns

Fail signals:

• MMP shows 60% fewer clicks than Meta reports (tracking link implementation issue or postback failures)

• Install rate jumped from 8% to 22% overnight (possible click spam or fraud)

• Clicks reported but zero installs attributed for 48+ hours (SDK integration broken or store listing misconfigured)

A fintech app running this check in Week 2 discovered their iOS Universal Links verification had expired after a domain certificate renewal. Meta was reporting 18,000 clicks but their MMP showed only 4,200. The tracking link wasn't redirecting properly. Fixing the certificate restored attribution for 76% of their iOS traffic.

Check 2: Postback Delivery Rate (2 minutes)

Postbacks are how your MMP sends conversion data back to ad networks so their algorithms can optimise. If postbacks fail, you're still measuring internally, but Meta and Google don't get the signal to improve targeting.

Navigate to your MMP's postback monitoring section (most platforms have this under Integrations or Partner Configuration). Check the past 7 days:

• Postback success rate per partner (Meta, Google, TikTok)

• Failed postback volume

• Postback delay (time between event occurrence and postback sent)

Pass criteria:

• Success rate >95% for all active partners

• Postback delay <10 minutes for 90% of events

• Zero "authorisation failed" errors (indicates API token expiry)

Fail signals:

• Success rate dropped below 90% (partner API changes or credential issues)

• Consistent 4-6 hour delays (event processing bottleneck)

• Sudden spike in failed postbacks after being stable (integration broken)

When postbacks fail silently, your internal ROAS looks fine but Meta's algorithm isn't learning. You keep buying the same user profile that worked last week, even though conversion rates shifted. Platforms like Linkrunner surface postback health in the main dashboard with automatic alerts when success rates drop below 95%, so you catch this before the next budget meeting.

Check 3: Event Volume Trend Analysis (4 minutes)

Pull your top 5-7 conversion events (install, signup, first purchase, day 1 retention, revenue) and compare this week to the previous two weeks.

You're not looking for exact matches. You're looking for unexplained variance.

Create a simple table:

• Event name

• This week's volume

• Last week's volume

• Two weeks ago volume

• Percentage change week-over-week

Pass criteria:

• Volume changes align with known campaign changes (if you increased spend 30%, installs should rise proportionally)

• Event ratios stay consistent (e.g., if 40% of installs typically complete signup within 24 hours, that ratio shouldn't drop to 22% without explanation)

• No events show zero volume while installs are happening

Fail signals:

• Revenue events dropped 40% but installs stayed flat (event tracking broken or payment integration failed)

• Signup volume stable but install volume doubled (either installs are overcounted or signup tracking is undercounting)

• A key event that fired 800 times last week shows zero this week (event name changed in latest app release and MMP mapping wasn't updated)

This check caught a critical issue for an eCommerce app. Their "add to cart" event volume dropped from 3,200 to 340 between Week 3 and Week 4, while installs actually increased. Investigation revealed their Android app team had refactored the checkout flow and renamed the event from "add_to_cart" to "cart_item_added" without updating the MMP event mapping. Two weeks of creative optimisation decisions were based on incomplete funnel data.

Check 4: Organic vs Paid Attribution Split (3 minutes)

One of the most common attribution failures is organic install overcounting, which makes paid UA look less effective than it actually is.

Pull your install attribution split for the past 7 days:

• Paid attribution percentage

• Organic attribution percentage

• Unattributed percentage

Pass criteria:

• If you're actively running paid UA, paid attribution should represent 40-70% of total installs (varies by vertical and brand strength)

• Organic percentage should be relatively stable week-over-week (±10%)

• Unattributed installs <5% of total volume

Fail signals:

• Organic percentage jumped from 35% to 68% in one week while paid spend stayed constant (attribution window shortened accidentally or click tracking broken)

• Paid attribution dropped to 15% but you're spending ₹50,000 weekly (massive attribution leak)

• Unattributed installs spiked to 20% (SDK implementation issue or users installing directly from search without clicking ads)

A common cause: teams shorten their attribution window from 7 days to 1 day without realising it, which pushes many genuine paid installs into the organic bucket because users didn't install within 24 hours of clicking the ad.

Check 5: Channel-Level ROAS Sanity Test (3 minutes)

This isn't a deep ROAS analysis. It's a quick sanity check that your measurement foundation is stable enough to trust.

For each major channel (Meta, Google, TikTok), check:

• Reported ROAS this week

• Reported ROAS last week

• Any major creative or targeting changes that would explain variance

Pass criteria:

• ROAS variance between weeks is <25% unless you made major campaign changes

• No channel shows infinite ROAS (revenue recorded but zero spend tracked)

• ROAS trends align with your actual campaign actions (if you paused underperforming ad sets, ROAS should improve)

Fail signals:

• Meta ROAS dropped from 110% to 45% but you didn't change anything (revenue attribution broken or cost data not syncing)

• Google shows ₹0 spend but 2,400 installs attributed (cost integration disconnected)

• TikTok ROAS tripled overnight while other channels stayed flat (likely attribution window issue giving TikTok credit for conversions it didn't drive)

MMPs like Linkrunner enables you to monitor ad campaign, ad set and ad creative level ROAS and other revenue-related metrics and compare this with other campaigns in the same view!

Red Flags That Require Immediate Investigation

Some attribution issues can wait until next week's audit. Others require immediate escalation because they're actively draining budget. Here's how to distinguish between "monitor" and "investigate now."

Immediate investigation triggers:

1. Revenue event tracking stopped completely (zero revenue events for 12+ hours while installs continue)

   • Impact: You're blind to actual conversion performance

   • Typical cause: Payment integration broken, event name changed, or SDK error

   • Action: Check app logs, verify event firing in test environment, review recent app releases

2. Postback success rate below 85% for 24+ hours

   • Impact: Ad networks can't optimise, leading to quality degradation

   • Typical cause: API token expired, partner platform changes, or rate limiting

   • Action: Re-authenticate partner connections, check MMP integration status

3. Install volume dropped >40% week-over-week with stable spend

   • Impact: Either attribution is broken or campaigns collapsed

   • Typical cause: SDK integration issue, store listing problem, or tracking link failure

   • Action: Validate SDK is firing, test install flow manually, check click-to-install rate

4. ROAS calculation showing impossible numbers (>500% or negative)

   • Impact: Budget decisions will be wrong in either direction

   • Typical cause: Cost data not syncing, currency mismatch, or duplicate revenue events

   • Action: Verify cost import from ad networks, check revenue event deduplication

5. Unattributed install percentage >15%

   • Impact: Losing visibility into what's driving growth

   • Typical cause: Attribution window too short, click tracking broken, or users bypassing tracking links

   • Action: Review attribution window settings, test tracking links, check for direct traffic sources

Monitor next week (not urgent):

• ROAS variance of 10-20% (normal fluctuation)

• Organic percentage shifted by 5-8% (acceptable drift)

• Single-day postback delays (infrastructure hiccup, not systemic)

• Event volume variance of 15-25% with known campaign changes

The key difference: immediate flags indicate your measurement system is broken. Monitor flags suggest normal variance or campaign performance changes.

Building the Audit into Team Rhythm

The weekly attribution audit only works if it becomes a habit, not a task you remember every third week.

Make it a Monday morning ritual

Schedule it for 9:00-9:20 AM every Monday, before your weekly performance review or budget meeting. This timing ensures you catch issues before making spend decisions based on bad data.

One growth team at a mobility app built this into their Monday standup. The performance lead runs the 20-minute audit while the team reviews the previous week's campaign performance. When a red flag appears, they discuss it immediately while everyone's together. When everything passes, they move to budget allocation with confidence.

Set up Slack alerts for critical thresholds

Manual audits catch most issues, but automated alerts catch the urgent ones faster.

Configure alerts for:

• Postback success rate drops below 90%

• Any conversion event shows zero volume for 6+ hours

• Revenue discrepancy between MMP and payment processor exceeds 15%

• Install volume drops >30% day-over-day

Most modern MMPs support webhook alerts or integration with Slack/Teams. Linkrunner's anomaly detection flags these issues automatically in your weekly email digest, so the audit becomes reviewing alerts rather than hunting for problems manually.

Create a shared audit log

Use a simple spreadsheet or Notion doc to track:

• Date of audit

• Who ran it

• Pass/fail status for each check

• Any issues found and resolution status

This creates accountability and historical context. When you notice ROAS trending down in Week 8, you can check Week 4's audit and see if there were early warning signs.

Rotate responsibility across the team

If only one person runs the audit, it becomes fragile. When they're on holiday or leave the company, attribution quality drops.

Rotate the audit across your performance team on a monthly schedule. Document each check with screenshots showing exactly where to find each metric in your MMP dashboard. New team members can run the audit in their second week.

Book the escalation path

Before you find a critical issue, decide who handles what.

Example escalation matrix:

• Postback failures → Performance lead re-authenticates partner connections

• Event tracking stopped → Escalate to engineering team immediately

• Fraud patterns → Flag with finance and pause suspicious campaigns

• ROAS calculation errors → Revenue operations team reviews currency and deduplication settings

Implementing the Audit in Your MMP

The specific dashboard locations vary by platform, but every MMP should surface these metrics without requiring SQL queries or data exports.

In your MMP dashboard, bookmark these views:

• Attribution overview (last 7 days, grouped by channel)

• Postback status monitoring (partner integrations page)

• Event volume trends (conversion events, time-series view)

• Multi-touch attribution report (user journey or path analysis)

• Fraud detection summary (if available)

If finding these metrics requires more than 3 clicks from your home dashboard, your MMP is designed for analysts, not operators. Platforms built for weekly operational use put foundational health checks on the first screen you see when logging in.

Set your default dashboard view to "Last 7 Days" so every login shows you the audit window automatically. Create a saved filter for "Paid Channels Only" to exclude organic noise when validating paid attribution specifically.

Key Takeaways

Attribution data quality isn't something you achieve once and forget. It's a weekly maintenance routine, like reviewing campaign performance or updating creative assets.

The 20-minute attribution audit catches the common failure modes before they compound:

• Click tracking breaks and you lose 30% visibility into paid installs

• Postbacks fail silently and ad networks optimise toward the wrong user profiles

• Event mapping changes and revenue attribution stops working

• Organic overcounting makes profitable campaigns look unprofitable

Run foundational checks every Monday for the first month. Once your baseline is stable, rotate through foundational and advanced checks (2-3 from each layer weekly). Set up Slack alerts for critical thresholds so urgent issues surface immediately.

The audit doesn't eliminate every attribution challenge, but it prevents the expensive ones: the issues that influence three consecutive weeks of budget decisions before anyone notices the measurement is wrong.

Most importantly, the audit builds confidence. When your CFO asks "Are these ROAS numbers accurate?", you can answer "Yes, we validated the measurement foundation yesterday" instead of "I think so?"

Frequently Asked Questions

How long does it take to build the audit habit?

Most teams report the audit taking 25-30 minutes in Week 1, dropping to 18-22 minutes by Week 4 as they get familiar with dashboard navigation and pass/fail criteria. The time investment is similar to reviewing weekly performance dashboards, with significantly higher ROI since you're validating the data quality those dashboards depend on.

What if I don't have time for a 20-minute audit every week?

If you're managing ₹50,000+ monthly in UA spend, you're already spending hours in weekly performance reviews and budget meetings. Those reviews are worthless if built on inaccurate attribution. The question isn't "Can I afford 20 minutes for an audit?" but rather "Can I afford to make budget decisions based on data I haven't validated?" Start with just Check 1 and Check 2 (5 minutes total) if you're genuinely time-constrained.

Should junior team members run this audit or does it require senior experience?

The audit is deliberately designed for operators at any level. Checks have clear pass/fail criteria and don't require interpretation or judgment. Junior team members can run the audit in their second month while senior team members handle escalation when issues are found. Rotating audit responsibility across experience levels also serves as training for understanding how attribution systems actually work.

What if my MMP doesn't surface all these metrics easily?

If accessing basic health metrics (postback status, event volumes, attribution splits) requires custom dashboard builds or SQL exports, your MMP is optimised for data teams, not marketing operators. This is a common issue with legacy platforms designed before operational efficiency became a priority. Evaluate whether the friction is worth the cost, particularly if you're spending ₹40,000+ annually on a tool that requires 45 minutes just to validate data quality.

How do I handle attribution audits across multiple apps or business units?

Create a shared audit template with one row per app. Each Monday, the performance lead for each app fills in their row (20 minutes per app). Use conditional formatting to highlight failures automatically. Apps with clean audits need no discussion. Apps with red flags get escalated. This scales to 5-10 apps with minimal coordination overhead.

Should I run this audit if I'm only spending ₹5,000 monthly on UA?

At lower spend levels, run a condensed version: Check 1 (click-to-install match), Check 2 (postback delivery), and Check 5 (ROAS sanity test) every week (10 minutes total). Add the full audit framework when you cross ₹15,000 monthly spend or when attribution accuracy directly impacts funding decisions.

Start Your First Audit This Monday

The attribution audit framework outlined here isn't theoretical. It's based on patterns seen across dozens of mobile growth teams managing ₹500,000 to ₹5,000,000 in annual UA spend.

The common thread: teams that validate attribution weekly catch expensive issues early, while teams that "check occasionally" discover problems only after budget has compounded the error for three or four weeks.

Start this Monday. Block 9:00-9:20 AM on your calendar. Run through Checks 1-5 in the foundational layer. Document what you find, even if everything passes. Build the habit before you need it.

If you're looking for a measurement platform that surfaces these health checks without requiring manual dashboard assembly, request a demo from Linkrunner. The platform is designed around operational efficiency for teams managing multi-channel attribution, with automatic anomaly detection, postback monitoring, and fraud pattern alerts built into the core dashboard, so the 20-minute audit becomes reviewing automatically flagged issues rather than hunting for problems manually.