Meta Ads vs. Google Ads: Five Metric Names That Don't Mean the Same Thing
The 60-second version
The same word means a different calculation on each platform. A field guide to the five most commonly confused metrics between Meta and Google Ads.
- Why "conversions" isn't one definition
- Attribution window defaults differ by platform
- ROAS: platform-attributed vs. blended
Last Tuesday a founder sent me two screenshots taken ten minutes apart. Meta Ads showed a 4.2x ROAS for the week. Google Ads showed 3.1x for the same week, on the same store, selling the same products to substantially the same customers. He wanted to know which one was lying.
Neither was. They were answering different questions that happen to share the same nouns. "Conversions" in Meta's interface and "conversions" in Google's interface are two different calculations with two different default attribution windows, two different counting rules, and two different ideas about what deserves credit.
This post is a field guide to the five most confused metrics — conversions, attribution window, ROAS, reach, and frequency — plus a reconciliation checklist you can run every Monday in under an hour.
Why "conversions" isn't one definition
A conversion is not a physical event both platforms observe identically. It is a counting rule applied to observed events, and every part of the rule differs:
- What counts as the event. In Google Ads, the advertiser defines conversion actions explicitly — a purchase action, a lead action, each with its own value and counting setting ("every" vs. "one"). In Meta, the event comes from the pixel, the Conversions API, or the app SDK, and the ad set optimises toward the selected event, but reporting columns mix standard events, custom events, and custom conversions with little indication of which is which.
- What counts as causing it. Google defaults to data-driven attribution across the Google click journey, splitting credit fractionally. Meta defaults to its own 7-day click or 1-day view rule, assigning full credit to the last Meta touchpoint it saw.
- Whether one person can convert twice. Google's "every" vs. "one" setting decides whether two purchases by the same clicker count as two conversions or one. Meta's columns have their own pair —
conversionsvs.unique conversions— and buyers routinely read the first while thinking the second.
Concrete example: a customer clicks a Google ad on Monday, clicks a Meta ad on Wednesday, and buys once for ₹4,999 on Friday. Google claims one conversion worth ₹4,999. Meta claims one conversion worth ₹4,999. Your database shows one order worth ₹4,999. Every system is internally consistent, and summed naively they report ₹9,998 of "attributed revenue" from a ₹4,999 order.
The rule that prevents most errors: never sum attributed conversions or attributed revenue across platforms. Each platform's number is a claim about its own contribution, computed as if the other platforms did not exist. Add them and you double-count every shared journey.
Attribution windows: different defaults, different revenue
The attribution window is the lookback period — how long after a click or view a conversion still gets credited. The defaults are not the same, and the default is what most accounts actually run on:
| Setting | Meta Ads default | Google Ads default | Why it matters |
|---|---|---|---|
| Click window | 7-day click | Data-driven, up to 30-day click for most conversion actions | Google credits older clicks Meta has already forgotten |
| View window | 1-day view on by default | View-through mostly excluded from Search; included on YouTube/Display with its own rules | Meta's revenue includes impression-driven orders Google Search never claims |
| Model | Last-touch within Meta, full credit | Fractional data-driven across Google touches | One Meta conversion usually equals 1.0; one Google conversion often equals 0.4 + 0.6 split across clicks |
| Changeability | Set per ad set at creation, rarely revisited | Set per conversion action, changeable retroactively for future reporting | Two ad sets in one Meta account can run different windows silently |
The practical effect is large. Moving a Meta ad set from 7-day click + 1-day view to 1-day click typically cuts reported conversions by 20–40% with zero change in actual sales. Any week-on-week comparison straddling a window change is meaningless until you normalise for it.
The 'Meta collapsed' week that was a settings change
ROAS: platform-attributed vs. blended
This is the confusion that costs real budget. Platform ROAS and blended ROAS share a name and a formula shape — revenue divided by spend — but the numerators come from different universes:
Two Numbers Called ROAS
Data JourneyPlatform ROAS
The platform's own attributed conversion value divided by its own spend, inside its own window and model. Each platform reports its own ceiling.
Blended ROAS (MER)
Your warehouse revenue divided by all marketing spend. No attribution, no windows, no models. One number for the whole business.
The relationship
The sum of platform-attributed revenues will almost always exceed warehouse revenue, because shared journeys are credited twice. Blended is the constraint; platforms are the diagnostics.
A typical Indian D2C week makes this concrete: Meta claims ₹14.2 lakh on ₹3.4 lakh spend (4.2x), Google claims ₹9.6 lakh on ₹3.1 lakh spend (3.1x), and the warehouse shows ₹39.1 lakh total revenue on ₹7.8 lakh total spend — 5.0x blended. Only the blended number can go in a budget case.
Show query
Where each number belongs. Platform ROAS is for relative decisions inside that platform — which ad set, which creative, which keyword. Blended ROAS is for absolute decisions — total budget, hiring, whether the business is working. Crossing them (killing a platform because its attributed ROAS dipped while blended held) is the most expensive category error in this post.
Reach and frequency: overlapping but not identical math
Reach and frequency look like neutral audience physics, but each platform computes them over its own observed population with its own identity graph:
- Reach counts different people. Meta's reach is logged-in users across its family of apps, deduplicated well within its walls. Google's reach spans Search, YouTube, and Display with different identifiers per surface. Neither sees the other's audience, so "combined reach" is not the sum — the overlap is invisible to both.
- Frequency divides by different denominators. Meta's frequency is impressions over its counted reach; Google's video planning uses modeled deduplication across YouTube and Display. A 3.1 on Meta plus a 2.4 on Google does not mean the average customer saw your ads 5.5 times.
- The practical use differs. Within one platform, frequency is a genuinely useful fatigue signal — CTR falling as frequency crosses 4–6 in prospecting is real. Across platforms, combined frequency can only be estimated, never added.
Treat reach and frequency as platform-internal diagnostics, like platform ROAS. Useful for pacing and fatigue inside the walls; meaningless summed across them.
A reconciliation checklist you can actually run
Every Monday, same order, under an hour. The point is not to make the numbers agree — they never will — but to know exactly why they disagree this week:
- Freeze the windows first. Screenshot or export the attribution setting on every active Meta ad set and every Google conversion action. If anything changed since last Monday, stop — that explains the movement. Log it with a date.
- Separate click from view. In Meta, split reported revenue into 7-day click vs. 1-day view before any comparison. If the view share jumped, the "growth" is attribution mix, not demand. Report click and view revenue on separate lines, always.
- Compute blended before platform. Warehouse revenue divided by total spend is the first number in the report, in the biggest font. Platform ROAS figures go below it, labelled as diagnostics. Anyone who reads only the first line still gets the truth.
- Check the overclaim ratio's movement. Sum of platform-attributed revenue divided by warehouse revenue. A stable ratio around 1.3–1.8 is normal for a two-platform mix. A jump of more than ~0.15 week-on-week means something changed — window, creative mix driving view-throughs, or traffic shifting to iOS where modeling runs heavier.
- Normalise the money. Confirm every figure is net revenue (paid orders, refunds excluded, tax and shipping treated identically) before comparing. A platform counting gross order value against a warehouse counting net revenue manufactures a permanent 8–12% "discrepancy" out of nothing.
- Write the one-paragraph bridge. End the report with a sentence of this form: "Warehouse revenue was ₹39.1L on ₹7.8L spend (5.0x blended). Meta claims ₹14.2L of it and Google claims ₹9.6L under their own windows; the ₹15.3L gap is overlap, view-through, and direct/organic demand." That paragraph is the entire reconciliation.
Where Each Metric Belongs
Reporting HierarchyBid on it
Platform-attributed conversions and ROAS inside each platform's own UI. The algorithm trains on its own signal — feed it nothing else.
Diagnose with it
Reach, frequency, click-vs-view splits, and the overclaim ratio. Internal health signals that explain movement but never leave the team.
Report on it
Warehouse revenue, blended ROAS, contribution margin. No platform touches either side, so nothing needs reconciling.
Frequently asked questions
Which platform's conversion number should I trust?
Neither, for absolute questions. Each is the best signal for relative decisions inside its own platform — which ad set beats which. For how the business is doing, trust the warehouse and compute blended efficiency.
My agency changed Meta's window to 1-day click "for accuracy." Good idea?
It is more conservative, not more accurate. A 1-day-click window understates any journey longer than a day — which, for a ₹2,000+ AOV product, is most journeys. Optimise on the platform's default window and report on blended numbers instead.
Why does Google show fractional conversions like 0.6?
That is data-driven attribution splitting one conversion across the Google clicks that preceded it. Do not round these, and do not compare them to Meta's whole-number last-touch credits as if they were the same unit.
Can I just use one platform's numbers as the source of truth?
Only if that platform touches nearly all demand. The moment two paid platforms plus organic all matter, every single-platform number overclaims by construction. Blended measurement is not optional at that point; it is arithmetic.
How often should I run the reconciliation checklist?
Weekly for the full checklist, daily for blended ROAS alone during scale or sale periods. Re-examine the windows themselves quarterly, or after any agency change — that is when they move silently.
The summary
- "Conversions," "ROAS," "reach," and "frequency" are different calculations sharing the same names across Meta and Google — different events, windows, models, and populations.
- Attribution window defaults differ (Meta 7-day click + 1-day view vs. Google data-driven with longer click lookbacks), and a window change moves reported numbers with zero change in sales.
- Platform ROAS is an attributed ceiling for bidding and relative decisions; blended ROAS from the warehouse is the constraint for budgets and board decks. Never sum attributed revenue across platforms.
- Reach and frequency are platform-internal diagnostics — useful for fatigue and pacing inside one platform, meaningless added together.
- Run the Monday checklist: freeze windows, split click from view, compute blended first, watch the overclaim ratio's movement, normalise net revenue, and write the one-paragraph bridge.
MER Calculator
Total revenue divided by total marketing spend — the attribution-agnostic efficiency number, plus its contribution-margin-adjusted variant.
Chinmay Raibagkar
About author →Founder of DataLens AI. He helps non-technical teams read their ad and database numbers with confidence — which number to trust, what to do next, and what to ignore.