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Blog›Marketing

Stop guessing which ads to cut and which to scale

Google, Meta, and LinkedIn each report campaign performance in isolation. AnalityQa AI AI joins your exports, calculates true CAC and ROAS, detects creative fatigue before it costs you budget, and shows exactly where spend should shift.

Try AnalityQa AI AI free →See live examples
Marketing analytics dashboard

The problem

  • →CAC calculations require joining ad spend from your ad platforms to new-customer data in your CRM — a cross-system join that most marketers do manually in a spreadsheet, if at all.
  • →Creative fatigue is detected too late. By the time CTR drops are visible in a platform dashboard, weeks of budget have already been spent on a declining creative.
  • →Budget pacing across campaigns and ad sets requires daily monitoring that falls through the cracks over weekends and holidays, leading to underspend or overspend against monthly targets.
  • →ROAS comparisons across platforms are distorted by different attribution windows and conversion-counting methods baked into each platform's native reporting.

Why the usual approach breaks down

Ad platform exports use incompatible attribution models

Google Ads defaults to data-driven attribution with a 30-day click window. Meta defaults to 7-day click and 1-day view. LinkedIn uses 30-day click. Comparing ROAS across platforms without normalising these windows produces numbers that appear to reflect reality but do not.

Creative-level data is buried in nested ad set hierarchies

To analyse creative performance, you need ad-level data joined to campaign and ad-set metadata. Most platforms export these as separate reports. Joining campaign ID to ad set ID to ad ID across three files, then matching spend to impressions to conversions, requires either SQL or a BI tool with a pre-built schema.

Budget pacing requires daily granularity that flat reports lose

Monthly or weekly aggregate reports hide the day-of-week spend patterns that cause campaigns to exhaust budgets by Wednesday or underspend on weekends. Spotting pacing issues requires daily row-level data — which is available in exports but rarely analysed at that granularity.

Cross-platform CAC calculation needs CRM data most ad tools cannot access

True CAC requires knowing how many new customers were acquired per campaign — information that lives in your CRM, not in your ad platform. Unless you have a data warehouse joining both, CAC stays approximate and campaign-level budget decisions lack a solid foundation.

How AnalityQa AI AI solves it

Upload your data — or connect it live — and ask in plain English.

01

Upload ad exports and CRM data in one session

Drop in campaign, ad-set, and ad-level exports from Google Ads, Meta Ads Manager, and LinkedIn Campaign Manager alongside a CRM new-customer export. AnalityQa AI AI detects each schema and proposes joins on campaign name, UTM, or date.

02

Normalise ROAS across platforms to a single attribution window

Specify the attribution window you want — for example, '7-day click only' — and AnalityQa AI AI filters conversion data in each platform export to that window before calculating any ROAS figure. Cross-platform comparisons become valid immediately.

03

Detect creative fatigue using frequency and CTR trends

Ask AnalityQa AI AI to flag creatives where frequency has exceeded a threshold and CTR has declined more than 20% week-over-week. It queries your ad-level data across the specified period and returns the fatigued creatives ranked by wasted spend estimate.

04

Analyse dayparting to find budget waste by hour and day

Ask 'Show me CPC and conversion rate by hour of day for my Google search campaigns.' AnalityQa AI AI extracts hour from your hourly export and groups performance metrics by that dimension, revealing whether overnight spend is delivering proportional results.

05

Calculate true CAC by campaign by joining to your CRM

AnalityQa AI AI joins ad spend per campaign to new customers per campaign via UTM or lead source, then divides to produce a true CAC per campaign. You can then ask it to rank campaigns by CAC, filter for those above your target, and estimate the budget impact of cutting them.

You askedGenerated in 4.2s

"What is our CAC by campaign for the last quarter?"

MQLs

2,418+18.2%

CAC

€142−9.4%

ROAS

4.2×+0.6×

Table: CAC by campaign — Q1 2026, sorted by CAC ascending

Last 12 mo

Table: fatigued creatives — last 30 days, with frequency, CTR trend, and estimated wasted spend

Dual-axis chart: CPC and conversions by hour of day — Google Search, March 2026

A dashboard built in AnalityQa AI — from question to chart, no SQL.

Real examples

Paste your data. Ask. Ship.

You

What is our CAC by campaign for the last quarter?

AI

AnalityQa AI AI joins your ad spend exports to your CRM new-customer export via UTM campaign parameter, sums spend and new customers per campaign, and calculates CAC for each.

Table: CAC by campaign — Q1 2026, sorted by CAC ascending
You

Flag creatives showing signs of fatigue in the last 30 days.

AI

AnalityQa AI AI groups your ad-level export by creative ID, calculates weekly CTR and frequency, and flags creatives where frequency exceeded 4 and CTR dropped more than 20% in the most recent week.

Table: fatigued creatives — last 30 days, with frequency, CTR trend, and estimated wasted spend
You

Show me CPC by hour of day for Google search campaigns last month.

AI

AnalityQa AI AI extracts hour from your Google Ads hourly report, groups CPC and conversion volume by hour, and plots both on a dual-axis chart to show where cost efficiency breaks down.

Dual-axis chart: CPC and conversions by hour of day — Google Search, March 2026
You

Compare ROAS across Google, Meta, and LinkedIn on a 7-day click attribution window.

AI

AnalityQa AI AI filters each platform's conversion data to the 7-day click window, sums spend and attributed revenue per platform, and calculates ROAS for a valid apples-to-apples comparison.

Bar chart: ROAS by platform — 7-day click attribution, Q1 2026
You

Show me daily budget pacing vs. target for our top 5 campaigns this month.

AI

AnalityQa AI AI calculates cumulative spend per campaign by day, computes the expected linear spend target, and highlights days where actual spend deviated by more than 15% from target.

Line chart: actual vs. target cumulative spend — top 5 campaigns, April 2026

What teams get out of it

✓Paid media teams that identify and pause fatigued creatives days earlier than dashboard monitoring alone, reducing wasted spend on declining ads.
✓A single cross-platform ROAS figure built on a consistent attribution window, replacing the three competing numbers that previously confused budget discussions.
✓Campaign-level CAC benchmarks that give finance and marketing a shared number for headcount and budget planning.
✓Dayparting insights that allow budget scheduling adjustments, reducing spend during low-conversion hours without manual campaign edits.

Frequently asked questions

Does AnalityQa AI AI connect to Google Ads or Meta Ads Manager directly?+

Direct API connectors for Google Ads, Meta Ads Manager, and LinkedIn Campaign Manager are on the roadmap. Today the path is to export CSVs from each platform's reporting UI and upload them. For teams syncing ad data to PostgreSQL or Google Sheets, a live connection is already available.

How does AnalityQa AI AI handle the attribution window difference between platforms?+

You specify the window you want to standardise on and AnalityQa AI AI filters the conversion columns in each platform's export to match. If a platform export does not provide windowed breakdowns at the row level, AnalityQa AI AI notes the limitation in the chart footnote so you know the caveat before drawing conclusions.

Can AnalityQa AI AI recommend which campaigns to scale or cut?+

AnalityQa AI AI surfaces the data — CAC, ROAS, fatigue signals, pacing gaps — and ranks campaigns by those metrics. It does not make autonomous budget decisions. The analyst or media buyer reviews the output and acts in the ad platform. This keeps human judgment in the loop for decisions that affect live spend.

We run hundreds of ad creatives. Will AnalityQa AI AI handle that scale?+

AnalityQa AI AI is designed for large CSV and Excel files. Ad-level exports with hundreds of creatives and millions of rows are processed without requiring any file splitting. Query performance on large datasets may take a few seconds longer than on small files.

How is ad performance data handled?+

Your files are stored in your isolated tenant, encrypted in transit and at rest. No data is shared with third parties or used to train models.

Can I track creative performance across A/B test variants?+

Yes. If your ad export includes a creative name or ad name column that distinguishes test variants, ask AnalityQa AI AI to group by that column and compare CTR, CPC, and conversion rate across variants. You can also ask it to calculate statistical significance if you provide impression and conversion counts.

How do we handle situations where a campaign runs across multiple platforms simultaneously?+

Upload exports from all platforms covering the same date range. AnalityQa AI AI joins on the shared campaign name or UTM parameter and aggregates spend, impressions, and conversions across platforms for a unified campaign view. Any platform where the campaign did not run simply contributes zero rows and does not distort the totals.

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