Why Your Platform Reports Don't Match (And What to Trust)

The Problem: One Conversion, Three Claims

Here's a scenario every marketer has experienced:

Your Actual Sales: 100 conversions

Platform Reports:
├── Google Ads:     85 conversions
├── Meta Ads:       45 conversions
├── Email:          35 conversions
├── Organic Search: 25 conversions
└── Total Claimed:  190 conversions  ← 90% overcounting!

You sold 100 products, but your platforms claim responsibility for 190. Someone is lying—or more accurately, everyone is telling their version of the truth.

This isn't a bug. It's how platform attribution works by design.

Why Platforms Disagree

1. Different Attribution Windows

Each platform uses different time windows to claim credit:

Consider this journey:

Day 1:  Customer clicks Google Ad
Day 15: Customer clicks Meta Ad
Day 20: Customer converts

Who gets credit?
├── Google Ads: ✓ (within 30-day click window)
├── Meta Ads:   ✓ (within 7-day click window)
└── Reality:    Both touched the customer, one conversion occurred

Both platforms legitimately claim this conversion under their own rules. Neither is wrong—they're just using different definitions.

2. Platforms Only See Their Own Touchpoints

Google can't see your Meta ads. Meta can't see your email campaigns. Each platform operates in a silo:

What Google Sees:
[Google Ad] → [???] → [???] → [Conversion]
     ↑
"We drove this conversion!"

What Meta Sees:
[???] → [Meta Ad] → [???] → [Conversion]
              ↑
"We drove this conversion!"

What Actually Happened:
[Google Ad] → [Meta Ad] → [Email] → [Conversion]

Platforms can only credit what they can see. The result is each claiming more credit than they deserve.

3. Self-Serving Attribution Logic

Platforms aren't neutral observers—they're selling you ads. Their attribution is designed to make their platform look effective:

This isn't conspiracy—it's incentives. Platforms that show better ROAS get more ad spend.

The Real-World Impact

Budget Misallocation

When you optimize budget based on platform data:

Platform-Reported ROAS:
├── Google Ads:  4.2x  → "Increase budget!"
├── Meta Ads:    3.1x  → "Maintain"
├── Display:     0.8x  → "Cut this!"

Multi-Touch Reality:
├── Google Ads:  2.8x  (overcredited by 50%)
├── Meta Ads:    2.2x  (overcredited by 40%)
├── Display:     1.9x  (undercredited by 140%!)

Result: You cut Display, which was actually introducing
customers that Google and Meta later converted.

The Attribution Arms Race

Platforms continuously adjust attribution to maintain perceived performance:

• Meta shortened their window from 28 days to 7 days (post-ATT) partly because shorter windows showed more immediate impact
• Google extended conversion windows and added data-driven attribution that favors Google touchpoints
• Each platform adds "modeled conversions" that estimate untracked activity—conveniently in their favor

You're not measuring marketing performance. You're measuring each platform's attribution marketing.

How to Build a Single Source of Truth

Option 1: Third-Party Attribution Tool (Recommended)

Use a neutral tool that sees all touchpoints and applies consistent logic:

How it works:
1. Track all touchpoints (ads, email, organic, direct) in one system
2. Apply a single attribution model across all channels
3. Deduplicate conversions—each sale counts once
4. Report consistent metrics across channels

Benefits:
- Unbiased cross-channel comparison
- Deduplicated conversion counts
- Consistent attribution windows
- Full journey visibility

Limitations:
- Additional cost
- Implementation required
- May not match platform optimization signals

Option 2: Incrementality Testing

Measure true causal impact through experiments:

Incrementality tests reveal what each platform actually causes, not just what it touches.

Option 3: Marketing Mix Modeling (MMM)

Use aggregate data and econometrics to estimate channel contribution:

Best for:
- High spend ($500K+/year)
- Mix of online and offline channels
- Strategic planning (not tactical)

How it helps reconciliation:
- Estimates total channel impact independent of platform reporting
- Accounts for brand effects, seasonality, competition
- Provides calibration factors for platform data

Practical Reconciliation Workflow

Step 1: Establish Baseline Discrepancy

Document how much platforms over-report:

-- Compare platform-reported vs actual conversions
SELECT
  date_trunc('week', conversion_date) as week,
  SUM(google_reported) as google_claims,
  SUM(meta_reported) as meta_claims,
  SUM(email_reported) as email_claims,
  SUM(actual_conversions) as actual,
  (SUM(google_reported) + SUM(meta_reported) + SUM(email_reported))
    / NULLIF(SUM(actual_conversions), 0) as overcount_ratio
FROM conversion_reconciliation
GROUP BY 1
ORDER BY 1 DESC;

Typical result: 1.4x to 2.0x overcounting.

Step 2: Apply Discount Factors

Create platform-specific discount factors based on historical overcounting:

Warning: These are rough averages. Your actual factors depend on your funnel, customer behavior, and channel mix. Calculate your own from historical data.

Step 3: Use Each Data Source Appropriately

Don't try to make platforms agree. Use each data source for what it's good at.

Step 4: Validate with Holdout Tests

Quarterly, validate your reconciliation with incrementality tests:

What NOT to Do

Don't Sum Platform Numbers

❌ Wrong: "Google (85) + Meta (45) + Email (35) = 165 conversions"

✓ Right: "We had 100 conversions. MTA credits Google 35%,
          Meta 28%, Email 22%, Organic 15%."

Don't Pick the Highest Number

Some teams use "whichever platform claims the conversion." This systematically over-credits bottom-funnel and always distorts the picture.

Don't Ignore the Problem

Using platform data "because it's what we have" leads to systematic misallocation. The effort to build a single source of truth pays back in better decisions.

Don't Expect Platforms to Agree

They have different incentives, different windows, different visibility. Reconciliation isn't about making them match—it's about building your own truth.

Summary

Platform attribution data conflicts because:

1. Different windows — Google uses 30 days, Meta uses 7 days
2. Siloed visibility — Each platform only sees its own touchpoints
3. Self-serving logic — Platforms are incentivized to maximize their claimed value

What to do:

Never sum platform-reported conversions. They're each telling their version of the truth, and those versions overlap.

Further Reading

On Building Your Source of Truth:
- What is Multi-Touch Attribution? — The cross-channel solution
- MTA vs MMM — Complementary measurement approaches

On Platform-Specific Issues:
- Why Did GA4 Remove Attribution Models? — Google's attribution changes
- Last-Touch Attribution — Why platform defaults mislead

Holly Henderson

Co-Founder, mbuzz

Holly Henderson is Co-Founder of mbuzz. With 10+ years in marketing including roles at Westpac, Avon, and Forebrite, she's obsessed with making measurement actually useful.

Harvard Extension School Forebrite Westpac Avon