We've been running paid ads for 15 years. We built the AI tool behind them.

The AI auditor for
your Google Ads

DataMind finds the causes of wasted spend, names them, and prices them in dollars per month.
Not “consider improving your ads” — but “these 7 search terms burn $195/month.”

9.1Geo · Zero-conversion spend

Fresno, CA spent $118/month with zero conversions

Excluding it: no traffic loss, spend redirected

✓ 60 days of dataReversible in one click

7.2Search terms · Waste

7 search terms burn $195/month

Excluding them moves account ROAS 1.05 → 1.09

✓ Statistically confirmedReversible in one click

6.4Bidding · Root cause

Campaign “Search — Generic — US”: bidding target unreachable — its “weak” landing pages are a symptom, not the cause

Fix at the bidding level, est. impact $340/month

✓ Root cause, not symptoms

See how it works

Authors of
articles on

Watch it work: heavy math,
then real AI analysis

A live audit generating — not a chatbot typing.

adm@copilot — formulate

$ Generating conclusions — 0/14 sections done

[]0%

▸ Analyzing: Trend: volume & pace — 27 campaigns▌

▸ Analyzing: Auction pressure (Search IS) — 22 campaigns▌

· ROAS factor decomposition (CPC × CR × AOV)

· Campaign contributions (Bennett mix/within)

· Portfolio classification

· Risks & early signals

· Data quality

· Ad groups — 51 ad groups

· Ads — 93 ads

· Keywords — 481 keywords

· Search terms — 39,951 search terms

· Geography — 214 countries, 8,733 cities

· Cross-report synthesis

· Final synthesis: account story, recommendations, hypotheses

1
Zero hallucinated numbers, by design. Every number comes from a deterministic engine — Bennett decomposition even splits growth into “budget moved” vs “campaigns actually changed” — and every identity is verified by runtime sum-checks. The LLM physically can't cite a figure that isn't in the data: a validator rejects the answer.
2
Clinical-trial-grade statistics. Eyeballing 40,000 search terms produces hundreds of false “problems.” Every term runs through Bayesian shrinkage and FDR control — only truly confirmed losers survive, each with a counterfactual attached: “remove these — account ROAS improves by X.”
3
One culprit, not 17 fix-lists. The AI cross-references every cut of your account and finds the overlaps: when one campaign shows up in semantics, geo, and landing-page losses at once, you fix that campaign — not three lists of symptoms.
4
Dead campaigns don't give advice. The engine tracks every campaign's lifecycle: stopped ones never enter recommendations, and conclusions freeze after recent settings changes — “wait for the June 23 budget change to play out first.” The savings we promise are savings you can actually collect.
5
It speaks your goal's language. If your target is CPA, every formula in the report runs in CPA optics — a double gate makes a mixed-metric report physically impossible to save, and a leak validator catches even the word “revenue” in a CPA narrative.

About 3 minutes later, this lands: ↓

One report. Two readers.

For owners and CMOs — the first screen is enough. What the account is losing per month, where, and what fixing it is worth: the account story and the top recommendations, each priced in $/month. No dashboards to interpret.

For the specialists who live inside the account — everything underneath. 30+ screens of findings, evidence, and reasoning: every recommendation with the data behind it, counterfactuals, statistical confirmations, change-history context.

02Cross-cutting view

SCross-cut synthesisavailable cuts: 6/7 — Products missing

Root cause level

The root cause lives at the campaign constraint level, not in keywords or pages. 88.3% of the account's performance loss stems from campaigns hitting budget caps, strategy limits, or status freezes — primarily Performance Max campaigns. The account's overall growth came from campaigns' own performance, not from budget reallocation between them. This means tactical fixes at the keyword or page level are blocked until the campaign-level constraints (budgets, bidding targets, and campaign statuses) are resolved first.

Constraints & statusesexplains 88%

resolve them first — while they are active, factor analysis is secondary

look first at: USA_PMAX_Interests MID_Search_Mixed WEST_PMAX_Interests-Keyword

Primary direction of work

The primary work direction is the auction (bidding strategy) layer, responsible for $2,264.69 in monthly losses — a clear dominant issue. Second priority is landing pages ($1,357.66 net monthly loss), third is geography ($574.24 net monthly), fourth is campaign structure ($397.98 net monthly), and last is search semantics ($181.69 monthly). However, this ranking assumes constraints are already fixed: USA_PMAX_Interests (34.7% of spend) is hitting its budget cap and reports an unreachable ROAS target of 1.08 (achieving 1.0259 in the second half) — its landing-page losses cannot be tackled until the budget and target are addressed. WEST_Search_Mixed (23.1% of spend) has a similar constraint: its target of 1.12 is tight relative to its 0.9264 achievement, and it carries tactical holes that will not improve until the strategy limit is relaxed.

PRIMARY CUT: Auction (bids/rank) — rest with reduced priority

Auction (bids/rank) ~$2,265/mo · Landing pages ~$1,392/mo · Geography ~$693/mo · Structure (ad groups/ads) ~$1,026/mo · Semantics ~$182/mo · PMax asset groups ~$0.00/mo

Campaign	Spend share	Symptoms (holes) period losses by cut	Root cause (bidding level) constraints from G, priority first
`USA_PMAX_Interests`	35%	landing pages $2,270geo $307	G1: Google — target caps deliveryG6: limited by bid strategy
`MID_Search_Mixed`	5%	ads/ad groups $946geo $163landing pages $68.03	G5: disapproved ads
`NORTH_Search_Brand_2-0`	1%	ads/ad groups $587semantics $186	G1: target tROAS 1.68 unattainable (fulfillment 70%)G3: fictitious budget → $32.29G12: volume limited
`WEST_Search_Brand`	12%	geo $324landing pages $149semantics $106	G12: volume limited
`SOUTH_Search_Mixed`	5%	semantics $556	G12: volume limited
`WEST_Search_Mixed`	23%	semantics $221landing pages $220	G1: Google — target caps deliveryG6: limited by bid strategyG12: volume limited
`EAST_Search_Long-Term`	1%	ads/ad groups $309geo $73.97	G1: target tROAS 1.10 unattainable (fulfillment 44%)
`CENT_Search_Mixed`	2%	ads/ad groups $209geo $156	G1: target tROAS 1.05 unattainable (fulfillment 79%)G6: limited by bid strategyG3: fictitious budget → $73.81
`EAST_Search_Mixed`	2%	geo $138	G1: Google — target caps deliveryG6: limited by bid strategyG12: volume limited
`USA_Search_Mixed`	1%	landing pages $75.46geo $27.21	no bidding-level constraints

campaign-first reading: the holes (semantics, ads, geo, landing pages) are the symptoms; when a bidding-level constraint exists (highlighted first), it is the root cause — the system can't hit the configured target and cycles through inefficient traffic, which surfaces as those holes

Price of problem categories

Loss categories rank by forward impact (net monthly forecast): auction ($2,264.69) is the largest, landing pages ($1,357.66), geography ($574.24), campaign structure ($397.98), and search semantics ($181.69). Performance Max cuts contribute $0 in losses. These cuts overlap — the same spend can appear in multiple categories — so they must not be summed. The planned scenario of removing semantic and geographic issues together would recover $181.69 and $539.47 respectively, lifting ROAS from 1.0486 to 1.0579 and improving target attainment from 98.9% to 99.8%, though the plan reaches the target line, not safely above it.

Auction (bids/rank)$2.3k/mo

Landing pages$1.4k/mo

Structure (ad groups/ads)$1.0k/mo

Geography$693/mo

Semantics$182/mo

Semantics: $363 over the period → ~$182/moStructure (ad groups/ads): $2,051 over the period → ~$1,026/mo · net of stopped ~$398/moGeography: $1,385 over the period → ~$693/mo · net of stopped ~$574/moLanding pages: $2,783 over the period → ~$1,392/mo · net of stopped ~$1,358/moProducts: Report not loadedPMax asset groups: $0.00 over the period → ~$0.00/moAuction (bids/rank): — over the period → ~$2,265/mo

Cuts overlap (one click can be both “bad term” and “bad geo”) — summing categories is FORBIDDEN; comparing them — allowed

Culprits in multiple reports

Two campaigns were already paused during the period and no longer drag the account forward. MID_Search_Mixed was paused on 2026-07-13, delivering $588.52 in monthly relief — it had disapproved ads limiting delivery and was inefficient (0.75 efficiency ratio). EAST_Search_Long-Term was paused on 2026-07-17, relieving $191.33 monthly — it was severely underperforming (0.52 efficiency ratio) with an unreachable ROAS target. Both stoppages are historical; the account has already benefited from removing them.

Campaign	Total loss	Where it leaks	What limits it	Payback
`USA_PMAX_Interests`	$2,270	Landing pages $2,270 (5)	hits budget cap bidding strategy constrained	1.07×
`MID_Search_Mixed`2 cuts⏸ stopped 13.07	$1,014	Structure (ad groups/ads) $946 (1) Landing pages $68.03 (1)	disapproved ads	0.75×
`NORTH_Search_Brand_2-0`2 cuts	$773	Structure (ad groups/ads) $587 (1) Semantics $186 (1)	limited by search volume	1.09×
`SOUTH_Search_Mixed`	$556	Semantics $556 (2)	limited by search volume	1.01×
`WEST_Search_Mixed`2 cuts	$441	Semantics $221 (2) Landing pages $220 (2)	bidding strategy constrained limited by search volume	1.01×
`EAST_Search_Long-Term`⏸ stopped 17.07	$309	Structure (ad groups/ads) $309 (1)	—	0.52×
`WEST_Search_Brand`2 cuts	$255	Landing pages $149 (1) Semantics $106 (1)	limited by search volume	1.00×
`CENT_Search_Mixed`	$209	Structure (ad groups/ads) $209 (1)	bidding strategy constrained	0.80×
`USA_Search_Mixed`	$75.46	Landing pages $75.46 (1)	—	0.68×

already removed by stopping: MID_Search_Mixed ⏸ stopped 13.07 ~$589/mo relief · EAST_Search_Long-Term ⏸ stopped 17.07 ~$191/mo relief

payback = campaign efficiency vs the account average (1.00× = average); geo-findings are not tied to campaigns (block P operates at account level) and do not participate here

Top safe actions

Seven safe and reversible actions rank by impact. First, conduct a targeting review for 6 pages receiving non-converting traffic from specific campaigns ($1,332.94 monthly effect) — parkline.com/parking/login is receiving poor-fit traffic from USA_PMAX_Interests; parkline.com/east from WEST_Search_Brand and WEST_Search_Mixed. Second, exclude 10 underperforming geographies, raising counterfactual ROAS from 1.0483 to 1.0555 ($539.47 monthly). Third, tighten match types on 5 keywords that convert well on their own query but leak 84–99% of spend to alien search terms — parking fees at eastvale [BROAD], eastvale airport parking rates [BROAD], and park on parking [PHRASE] are the top offenders ($449.06 monthly, medium reversibility). Fourth, add 7 negative keywords (statistically confirmed), improving counterfactual ROAS from 1.0266 to 1.0307 ($181.69 monthly). Fifth, A/B-test or fix 1 underperforming landing page parkline.com/info/contact_us before cutting its traffic ($58.76 monthly, medium reversibility). These actions touch 10% of account spend and are reversible within 1–2 weeks.

#	Action	Savings/mo	Evidence	Undo
1	6 pages receive non-converting TRAFFIC from specific campaigns (O8b, top: parkline.com/parking/login ← USA_PMAX_Interests; parkline.com/east ← WEST_Search_Brand, WEST_Search_Mixed) — review those campaigns' targeting, the page itself is not the problem ▸ show the full list (6)	~$1,333/mo	rule-of-thumb estimate	undo in one click
2	10 geo exclusions (P7), counterfactual ROAS 1.0483 → 1.0555 ▸ show the full list (10)	~$539/mo	counterfactual estimate	undo in one click
3	5 keywords convert fine on their own query but leak most spend to alien search terms (top: parking fees at eastvale [BROAD] — 98% alien; eastvale airport parking rates [BROAD] — 84% alien; park on parking [PHRASE] — 99% alien) — switch them to a stricter match type instead of pausing	~$449/mo	rule-of-thumb estimate	undo takes a few steps
4	7 negative keywords (M3), counterfactual ROAS 1.0266 → 1.0307 ▸ show the full list (7)	~$182/mo	statistically confirmed	undo in one click
5	1 landing page converts worse than its campaigns' payback (top: parkline.com/info/contact_us) — fix or A/B-test the page before cutting its traffic ▸ show the full list (1)	~$58.76/mo	rule-of-thumb estimate	undo takes a few steps

03Dynamics

BTrend: volume and pace

B1 · ROAS

1.08→1.01

↘ −6.6%

B2 · Revenue

$109,971→$88,548

↘ −19.5%

B3 · Purchases

5334.72→4420.56

↘ −17.1%

B4 · Cost

$101,692→$87,627

↘ −13.8%budget $3,904/d, util. 75%

What drove purchase growth

Revenue declined because spending fell more sharply than efficiency. Spend fell 14.89% while return on ad spend fell 6.78%, meaning the decline in revenue (−19.48%) is driven primarily by the budget cut rather than by deteriorating campaign performance. Spend reductions account for 68.71% of the revenue drop; efficiency losses account for 31.29%. The account is spending less and earning less in lockstep — a budget-constrained contraction rather than an efficiency crisis.

purchased by spend

revenue change: 69% explained by spend, 31% — by efficiency (ROAS)

▸ calculation identity

164+ checks → one report: causes, ranked and priced.

Checking your agency's work or defending your own — either way, this is the document you bring to the meeting.

From sign-in to a full account audit
in three steps

Step 1Connect.
Sign in with your work email, then one button: Connect Google Account. The connection is transparent — you'll see it in Connections and can remove it in one click.
Step 2Set your goals.
DataMind pulls every goal from your account and you mark the one that matters — purchase, lead, booking. Your goals can be named anything, even “cart in Albanian”. You match them to your funnel once, and the system knows what actually counts as a result.
Step 3Run your first audit.
One click. The last 60 days of data. About 3 minutes to a full review of your account.

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What you're using now
and what it can't do

	Google Ads interface	Dashboards & alert tools	Claude / ChatGPT via MCP	LLM-wrapper startups	DataMind
Shows	What changed	Metrics that crossed a threshold	An answer to what you asked	Plausible text	Why it changed — and what fixing it is worth in $/month
Math	Sorts by spend, not by distance to target	Crude thresholds — can't tell noise from calibration from a real trend	The LLM does its own arithmetic on raw rows — a different answer every run	Same thing, dressed as a product; sooner or later it quotes a figure that isn't in the data	Deterministic engine computes; AI analyzes; validators check every figure
Methodology	A 15-year-old interface	You still do the sifting yourself	Quality = your prompt. It answers what you ask — and nothing you didn't	General knowledge, no rails	164+ checks from 400+ pages of audit rules, written by specialists with 15+ years of running accounts
Month over month	—	Static rules, same blind spots	Ask twice, get two different answers	Ask twice, get two different answers	The same 164+ checks every run — results you can track and compare over time
Budget planning	One campaign at a time; 100 exports nobody aggregates	—	—	—	Whole portfolio, mixed scenarios: “+10% here, −20% there, switch this off”

Shows

Google Ads interface: What changed
Dashboards & alert tools: Metrics that crossed a threshold
Claude / ChatGPT via MCP: An answer to what you asked
LLM-wrapper startups: Plausible text
DataMind: Why it changed — and what fixing it is worth in $/month

Math

Google Ads interface: Sorts by spend, not by distance to target
Dashboards & alert tools: Crude thresholds — can't tell noise from calibration from a real trend
Claude / ChatGPT via MCP: The LLM does its own arithmetic on raw rows — a different answer every run
LLM-wrapper startups: Same thing, dressed as a product; sooner or later it quotes a figure that isn't in the data
DataMind: Deterministic engine computes; AI analyzes; validators check every figure

Methodology

Google Ads interface: A 15-year-old interface
Dashboards & alert tools: You still do the sifting yourself
Claude / ChatGPT via MCP: Quality = your prompt. It answers what you ask — and nothing you didn't
LLM-wrapper startups: General knowledge, no rails
DataMind: 164+ checks from 400+ pages of audit rules, written by specialists with 15+ years of running accounts

Month over month

Google Ads interface: —
Dashboards & alert tools: Static rules, same blind spots
Claude / ChatGPT via MCP: Ask twice, get two different answers
LLM-wrapper startups: Ask twice, get two different answers
DataMind: The same 164+ checks every run — results you can track and compare over time

Budget planning

Google Ads interface: One campaign at a time; 100 exports nobody aggregates
Dashboards & alert tools: —
Claude / ChatGPT via MCP: —
LLM-wrapper startups: —
DataMind: Whole portfolio, mixed scenarios: “+10% here, −20% there, switch this off”

No more pivot tables. Ever.

Even before the AI touches anything, DataMind replaces the part of your job you hate: exporting, VLOOKUP-ing, and rebuilding the same spreadsheets every week. Every cut of your account — campaigns, semantics, geo, landing pages, ads and assets — is already aggregated, grouped by theme, and sortable. And every row is tagged: dynamics (“ROAS Increase,” “CTR Campaign Decrease”), assessment (“Good Campaign,” “Off Plan”), red flags. The kind of markup you'd spend hours building by hand — already on the screen.

Spend Distribution

Australia
Brazil
Canada
Ecuador
Germany
Hong Kong
Italy
Japan
Mexico
Nigeria
Other
South Korea
Spain
Sweden
United Kingdom
United States

Performance Matrix

X:CR to Purc ⌄Y:AVG Value ⌄Size:Purchases ⌄

Campaign	Country/Territory	Cost	Impr.	CTR	CPC
USA_PMAX_Interests Performance Max	United States Near Plan NORAM · First Tier economy	$65,598.62 $40,404 > $25,195 (−38%) Major CountryDecrease	378,392 193,332 > 185,060 (−4%)	9.50% 10.11% > 8.85% (−12%) AverageDecrease	$1.83 $2.07 > $1.54 (−26%) AverageDecrease
WEST_Search_Mixed Search	United States Near Plan NORAM · First Tier economy	$43,661.17 $21,954 > $21,708 (−1%) Major CountryFlat	136,860 66,809 > 70,051 (+5%)	10.92% 10.96% > 10.87% (−1%)	$2.92 $3.00 > $2.85 (−5%)

Showing 1 to 500 of 1,233 entries

A campaign in your top 5 by spend — tagged “Poor Campaign.” Money leader, results loser. Visible in one glance at the bubble chart.
Half the budget sitting in unclustered semantics marked “Off Plan” — spend flowing into keywords nobody grouped, nobody watches.
A campaign where cost jumped +117% while CTR slid −12.7% — flagged automatically, before you'd ever have noticed it in the interface.

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Google's AI works for
Google first

Smart Bidding sets the prices you pay. Performance Max picks its own audiences and pages, then reports back in aggregates you can't question. After 15 years of running paid media, we built the AI that sits on your side of the table — and checks theirs.

Scenarios Landscape — Pareto frontier (30d)

X: Avg ROAS · Y: Conv. Value · bubble size: Spend

One machine spends the money.
The other checks the receipts.

Your spend can grow.
The price won't.

Agencies and most platforms charge a percentage of your media spend — scale your budget, and you pay more for the same work. DataMind is a flat subscription. Your spend is your business.

The first audit typically finds more monthly waste than DataMind costs for a whole year.

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What if you're losing money right now and just don't know it?

Run it on your own account and compare: what the report says is actually happening, versus what you think is happening. It takes a little over three minutes. If everything checks out — great, you'll sleep better tonight. If it doesn't, you just found something worth far more than three minutes of your time.

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~3 minutes · 164 checks · disconnect anytime in one click

USA_Search_Mixed1%landing pages $76.96geo $27.18G1: target tCPA $16.00 unattainable (fulfillment 81%)

campaign-first reading: the holes (semantics, ads, geo, landing pages) are the symptoms; when a bidding-level constraint exists (highlighted first), it is the root cause

Price of problem categories

Auction constraints will cost $2,152.52 per month if unaddressed — the largest single category. Landing pages cost $1,394.14 monthly after some fixes already applied. Geography costs $556.51 monthly net. Structure/ad-level issues cost $272.47 monthly after $435.69 of improvements. Semantics cost $151.75 monthly. Performance Max itself shows no loss.

Auction (bids/rank)$2.2k/mo

Landing pages$1.4k/mo

Structure (ad groups/ads)$708/mo

Geography$694/mo

Semantics$152/mo

Semantics: $304 over the period → ~$152/moStructure (ad groups/ads): $1,416 over the period → ~$708/mo · net of stopped ~$272/moGeography: $1,387 over the period → ~$694/mo · net of stopped ~$557/moLanding pages: $2,865 over the period → ~$1,433/mo · net of stopped ~$1,394/moProducts: Report not loadedPMax asset groups: $0.00 over the period → ~$0.00/moAuction (bids/rank): — over the period → ~$2,153/mo

Cuts overlap (one click can be both “bad term” and “bad geo”) — summing categories is FORBIDDEN; comparing them — allowed

Culprits in multiple reports

Two campaigns have already been paused and are now a closed matter: MID_Search_Mixed stopped on 2026-07-13, removing $522.21 monthly drag going forward. EAST_Search_Long-Term stopped on 2026-07-17, removing $36.93 monthly drag.

Who built DataMind — and
why the setting is the product

An LLM on its own is a raw stone: brilliant in flashes, unreliable exactly when the money is on the line. What turns it into something you can take into a board meeting is the setting cut around it — deterministic math, 400+ pages of audit rules, and seven layers of validation, written by people who have spent 15 years running paid media for brands you already know.

The agency

DataMind was built inside Affect Group, a performance marketing agency that has been running paid media for 15 years — for PepsiCo, Mercedes-Benz, Mastercard, Durex and Peet's Coffee, alongside hundreds of smaller accounts. Every check in the engine started as something we needed ourselves, on a real account, with real money moving through it.

The jury seat

We are the official performance marketing partner of the Shorty Awards in the US — the awards often called the “Oscars of the advertising world.” Our team also sits as head judges at the Effie Awards and the Shorty Awards: we spend part of every year deciding what qualifies as world-class work, which is a useful habit when you are writing the rules an audit engine follows.

The record

Our blog and expert analyses are read by more than a million people worldwide, and our benchmarks get quoted when the industry press needs a number it can stand behind.

With ChatGPT entering the advertising space, EMARKETER published an analysis on pricing — and referenced Affect Group as an expert source for Meta Ads CPM benchmarks.

In public

We speak at international forums and conferences, run a podcast with expert guests for the Shorty Awards (300k views), and write for Medium and Better Marketing, a platform with over 135,000 subscribers. The methodology inside DataMind isn't a secret sauce — it's the same thinking we publish under our own name, argue about in public, and get judged on.

Performance marketing,
explained in plain English

Short breakdowns from inside real ad accounts — what actually moves the numbers, no jargon. A couple of minutes a week.

@affect_group_inc

The AI auditor foryour Google Ads

Fresno, CA spent $118/month with zero conversions

7 search terms burn $195/month

Campaign “Search — Generic — US”: bidding target unreachable — its “weak” landing pages are a symptom, not the cause

Watch it work: heavy math,then real AI analysis

One report. Two readers.

From sign-in to a full account auditin three steps

Step 1Connect.

Step 2Set your goals.

Step 3Run your first audit.

What you're using nowand what it can't do

Shows

Math

Methodology

Month over month

Budget planning

No more pivot tables. Ever.

Spend Distribution

Performance Matrix

Google's AI works forGoogle first

Scenarios Landscape — Pareto frontier (30d)

Your spend can grow.The price won't.

Plans

What if you're losing money right now and just don't know it?

Who built DataMind — andwhy the setting is the product

Performance marketing,explained in plain English

The AI auditor for
your Google Ads

Watch it work: heavy math,
then real AI analysis

From sign-in to a full account audit
in three steps

What you're using now
and what it can't do

Google's AI works for
Google first

Your spend can grow.
The price won't.

Who built DataMind — and
why the setting is the product

Performance marketing,
explained in plain English