DatabasesConnect to any database and analyse your data instantly·FilesUpload CSV or Excel files and explore them with AI·ChatAsk questions in plain language — chat with your data·DashboardsBuild interactive dashboards from your queries in seconds·AILet AI write the SQL so you don't have to·ChartsVisualise trends with auto-generated charts and graphs·No-codeZero SQL knowledge needed — just ask in plain English·ShareShare live dashboards with your team in one click·InsightsSurface hidden patterns and outliers in your data automatically·ExportsDownload results as CSV, Excel, or PNG charts instantly·DatabasesConnect to any database and analyse your data instantly·FilesUpload CSV or Excel files and explore them with AI·ChatAsk questions in plain language — chat with your data·DashboardsBuild interactive dashboards from your queries in seconds·AILet AI write the SQL so you don't have to·ChartsVisualise trends with auto-generated charts and graphs·No-codeZero SQL knowledge needed — just ask in plain English·ShareShare live dashboards with your team in one click·InsightsSurface hidden patterns and outliers in your data automatically·ExportsDownload results as CSV, Excel, or PNG charts instantly·
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Blog›Marketing

Know exactly which channels are earning their budget

Your ad spend is split across six platforms, each reporting conversions differently. AnalityQa AI AI joins your exports, normalises attribution windows, and tells you where the money is actually working.

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

The problem

  • →Google Ads, Meta, GA4, and your CRM each claim credit for the same conversion — making blended ROAS meaningless without manual reconciliation.
  • →Campaign-level spend and revenue live in separate CSV exports, so calculating true CAC or LTV:CAC ratios requires hours of spreadsheet work every week.
  • →Attribution windows differ by platform (7-day click on Meta, 30-day on Google), so cross-channel comparisons are silently comparing apples to oranges.
  • →Finance asks for monthly channel ROI reports that take the marketing team two days to assemble and are already stale by the time they land.

Why the usual approach breaks down

Data lives in six silos with no shared key

Google Ads exports campaign IDs. Meta exports ad set names. GA4 exports UTM parameters. Your CRM stores deal source as free text. No two platforms share a reliable join key, so every cross-platform analysis starts with a brittle manual mapping step.

Spreadsheet joins break the moment anything changes

VLOOKUP pipelines built around campaign names shatter when a name is renamed mid-flight, a new channel is added, or someone exports with different date granularity. The fix always falls to whoever built the sheet — usually at 11 p.m. before a board meeting.

Attribution models are baked into each platform's own reporting

Meta's last-click attribution and Google's data-driven model produce different numbers for the same campaign. Without a neutral layer that normalises both to a consistent window and model, you cannot make a valid apples-to-apples comparison.

BI tools need a pre-built data model before they can help

Looker Studio, Tableau, and Power BI require a clean, joined dataset and a schema someone has already designed. Standing that up takes weeks of engineering time — time most marketing teams do not have.

How AnalityQa AI AI solves it

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

01

Upload exports from every platform in one place

Drop in CSV or Excel exports from Google Ads, Meta Ads Manager, GA4, and your CRM. AnalityQa AI AI detects the schema of each file automatically — no column-mapping wizard, no template to conform to.

02

Auto-join on campaign name, UTM, or date

AnalityQa AI AI identifies shared dimensions across your files — campaign name, UTM source/medium, date — and proposes a join strategy. You confirm or override it in plain language. The joined dataset is built in seconds.

03

Ask about ROI across any combination of channels

Once your data is joined, ask 'What is my blended ROAS by channel last quarter?' or 'Which campaigns have a LTV:CAC ratio above 3x?' AnalityQa AI AI writes and runs the query, then returns a chart or table — no SQL required.

04

Apply a consistent attribution window across all channels

Tell AnalityQa AI AI which attribution window to use ('7-day click, 1-day view') and it normalises spend and conversion data from every platform to that definition before calculating any ratio. No more comparing platform-native numbers.

05

Pin results to a live dashboard and schedule exports

Any chart or table can be pinned to a shared dashboard. Set a weekly export and your stakeholders receive the same marketing ROI report automatically — built from the latest data you have uploaded.

You askedGenerated in 4.2s

"Show me ROAS by channel for the last 90 days."

MQLs

2,418+18.2%

CAC

€142−9.4%

ROAS

4.2×+0.6×

Bar chart: ROAS by channel — last 90 days

Last 12 mo
Segment ASegment BSegment CSegment DSegment ESegment F

Table: CAC by channel — Q1 2026 (first-touch attribution)

Table: campaigns flagged for LTV:CAC < 2x — sorted by deficit

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

Real examples

Paste your data. Ask. Ship.

You

Show me ROAS by channel for the last 90 days.

AI

AnalityQa AI AI joins your Google Ads, Meta, and GA4 exports on UTM source, sums spend and attributed revenue per channel, and calculates ROAS for each.

Bar chart: ROAS by channel — last 90 days
You

What is my CAC by acquisition channel this quarter?

AI

AnalityQa AI AI matches new customers in your CRM to their first-touch UTM source from GA4, then divides total spend per channel by new customers acquired.

Table: CAC by channel — Q1 2026 (first-touch attribution)
You

Which campaigns have a LTV:CAC ratio below 2x?

AI

AnalityQa AI AI cross-references customer LTV from your CRM with campaign-level CAC from your ad exports, then flags every campaign where the ratio falls below your threshold.

Table: campaigns flagged for LTV:CAC < 2x — sorted by deficit
You

Compare paid search vs. organic traffic conversion rate by landing page over the last 8 weeks.

AI

AnalityQa AI AI segments your GA4 session export by medium (cpc vs. organic), groups by landing page, and calculates conversion rate for each combination across the 8-week window.

Heatmap: landing page × week conversion rate — paid search vs. organic
You

Show me how Meta campaign spend and revenue trended week by week in March.

AI

AnalityQa AI AI aggregates your Meta Ads export by week, overlays spend and attributed revenue on a dual-axis chart, and highlights weeks where ROAS dropped below your average.

Line chart: Meta spend vs. attributed revenue — weekly March 2026

What teams get out of it

✓Marketing teams that move budget reallocation from monthly to weekly cycles after gaining a single cross-channel view of ROAS.
✓Reduction in time spent on weekly performance reports from several hours to under ten minutes once uploads and a dashboard are in place.
✓Clearer CAC benchmarks per channel, giving finance and marketing a shared number to plan against instead of competing platform-native figures.
✓Faster identification of underperforming campaigns — weeks earlier than spreadsheet-based reviews — through automated ratio monitoring.

Frequently asked questions

Can AnalityQa AI AI connect directly to the Google Ads or Meta API instead of CSV exports?+

Direct API connectors for Google Ads, Meta, and GA4 are on the roadmap. Today, the recommended path is to export CSVs from each platform's native reporting UI and upload them to AnalityQa AI AI. The auto-join step handles the rest. For teams on PostgreSQL or Google Sheets, a live connection is already available.

How does AnalityQa AI AI handle the difference between first-touch and last-touch attribution?+

AnalityQa AI AI does not impose an attribution model — it uses the source data you provide. If you upload a GA4 first-touch report and a Meta last-click report, it makes the model explicit in every chart label so you are never comparing them silently. You can also ask it to filter to a single model for a given analysis.

Ad platform exports sometimes contain aggregated, privacy-thresholded data. Does that cause problems?+

Google Ads and Meta both suppress rows where audience segments are too small, replacing them with dashes or omitting them entirely. AnalityQa AI AI flags any rows it cannot parse and shows you the coverage gap, so you know exactly how much of your spend is represented in the analysis before you draw conclusions.

Our attribution windows vary — Meta uses 7-day click / 1-day view, Google uses 30-day click. How do we get a fair comparison?+

You tell AnalityQa AI AI which window you want to standardise on, and it filters the conversion data in each platform export to match that window before calculating any shared metric. If a platform export does not expose windowed data at the row level, AnalityQa AI AI notes the caveat in the chart footnote.

Can this replace our Looker Studio or Google Data Studio dashboards?+

For teams that spend most of their time asking new questions rather than monitoring a fixed set of KPIs, AnalityQa AI AI is faster — no pre-built data model required. For CMO dashboards that update daily and are viewed by many stakeholders, AnalityQa AI AI's pinned dashboard feature covers most use cases. Teams that need complex blended data sources with pixel-perfect formatting often keep Looker Studio for executive views and use AnalityQa AI AI for ad hoc analysis.

Is the data private?+

Your uploaded files and query results are stored in your isolated tenant, encrypted in transit and at rest, and are never used to train models.

What does it cost, and is there a limit on how many platform exports I can upload?+

Pricing is based on the number of users and data volume, not the number of files or queries. You can upload exports from as many platforms as you need within your plan's storage limit. A free trial is available with no credit card required — details are on the pricing page.

Related guides

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E-commerce Sales Analysis Without the Spreadsheet Chaos

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Customer Churn Analysis Without the Spreadsheet Grind

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