SheetAI
Marketing & Analytics

Stop wrangling exports. Start running campaigns.

80% of marketers say they cannot reconcile results across their tools, and the average enterprise stack now sprawls across ~90 of them. SheetAI lives inside the spreadsheet you already glue everything together in — it reads your cells in place, writes the joins and formulas, and produces a channel report you can defend in the QBR.

Try it on your sheetSee real workflows
Audit-ready trailNo data trainingReversible per action
12 min
To attribute a quarter of leads
From a HubSpot export and a Meta Ads CSV to a clean multi-touch attribution waterfall on a 4,200-lead pipeline.
63%
Less time on weekly reporting
Median time saved per Monday morning report by performance marketing customers using SheetAI for cross-channel rollup.
99.1%
UTM normalization accuracy
Average accuracy of AI-canonicalized utm_source / utm_medium values on a 38,000-row campaign corpus, validated against a marketing-ops review.
80% of marketers can't reconcile results across their tools.

The MMA State of Attribution puts the dissatisfaction rate at 80%. Supermetrics puts the share who can't measure cross-channel at 41%. The honest answer is not a 91st tool — it is making the spreadsheet you already use do the join.

Source: MMA Global — State of Attribution; Supermetrics 2025

Modern marketing teams run on spreadsheets. The CSVs come from GA4, the lead lists come from HubSpot, the spend tables come from Meta and Google Ads, and the rollup that the CMO sees on Monday is built in Excel on Sunday night. The 2025 Adverity benchmark found 40% of marketers cite manual data wrangling as their #1 reporting challenge; the Gartner UK Digital Marketing Survey found only 24% of B2B teams have working multi-touch attribution at all. The honest fix is not another dashboard you paste into. It is to give the spreadsheet a competent assistant that reads ranges on demand, writes standard formulas a CMO can audit, and stops the moment it is unsure. That is what SheetAI is. Every action it takes is reversible, every output is explainable, and your customer data never has to leave your account.

The state of marketing analytics in 2026

Four numbers, sourced from 2025 marketing research, that explain why your Monday rollup feels the way it does — and why the answer is not another tool.

Marketers using generative AI in at least one workflow

87%

Up from 51% in 2024. Content marketers lead at 96% adoption; SEO at 93%; demand gen at 89%. The question is no longer whether — it is whether the AI can see your numbers.

Salesforce State of Marketing 2026

Marketers dissatisfied with cross-tool reconciliation

80%

41% say they cannot effectively measure marketing across channels at all. Only 24% of UK B2B teams have working multi-touch attribution.

MMA / Supermetrics / Gartner 2025

Average MarTech utilization rate

33%

Down from 58% in 2020 and 42% in 2022. The average enterprise stack runs ~90 tools — and uses a third of them.

Gartner Marketing Tech Survey 2024

Media spend wasted on poor data quality

21¢

Of every dollar. 70.3% annual decay on B2B contact data; 28% annual decay on email lists. The cost of bad data is the cost of the campaign.

Forrester / IndustrySelect 2025

The pattern: AI adoption has crossed 85%, but spending is flat at 7.7% of revenue and 59% of CMOs say their budget is insufficient for the strategy. Buying a 91st tool is not the answer. Making the spreadsheet you already trust read your data is.

Anatomy of a campaign launch

Where the hours actually go on a mid-funnel campaign launch, before any automation. We mapped this against the 2025 Gartner CMO Spend Survey and HubSpot State of Marketing data — if your two-week sprint looks like this, you are not slow, you are the median.

1

Phase 1 — Audience build & segmentation

  • ›Pull CRM contacts and engagement scores
  • ›De-duplicate against a 70.3%/year decaying contact list
  • ›Cross-reference suppression list and consent flags
⚠ 70.3% of B2B contact data decays annually. Most of an audience build is data hygiene done by hand against a moving target.
2

Phase 2 — Creative trafficking & UTM tagging

  • ›Hand-author UTM strings across Meta, Google, LinkedIn
  • ›Reconcile naming conventions between agencies
  • ›Patch missing utm_content on dark-social referrals
⚠ A typo in utm_source breaks the rollup three weeks later. 30–40% of B2B touchpoints arrive in untracked channels and need to be rebucketed by hand.
3

Phase 3 — Spend pacing & mid-flight optimization

  • ›Daily pull from Meta, Google, LinkedIn ad APIs
  • ›Cost-per-lead by campaign, ad set, creative
  • ›Reallocate budget into top-quartile placements
⚠ 59% of CMOs say they have insufficient budget for the strategy. The pacing sheet is rebuilt every Monday because no two platforms export the same shape.
4

Phase 4 — Attribution & lead-to-revenue

  • ›Match closed-won deals back to first-touch and last-touch
  • ›Run a U-shaped or W-shaped multi-touch model
  • ›Reconcile against finance's booked revenue
⚠ 80% of marketers are dissatisfied with their attribution. The waterfall is a manual VLOOKUP chain that breaks when ops renames a stage.
5

Phase 5 — QBR pack & narrative

  • ›Channel-by-channel ROAS, CAC, LTV pull
  • ›Variance vs. plan with a one-line cause per row
  • ›Slide narrative for the CMO and CFO
⚠ 33% of marketers cite measuring ROI as their #1 challenge. The pack is a copy of last quarter with the numbers updated. The narrative is written from scratch every time.
Cautionary tale

Why "AI inside the sheet" matters more than you think

In February 2018, Snap rolled out a redesign of Snapchat that bundled friends' stories with publisher and brand content under a single Discover feed. Internal data on the test cohorts had been promising. External data — what users were actually saying on Twitter and in app reviews — was not in any of the dashboards the product team monitored. A single Kylie Jenner tweet ("sooo does anyone else not open Snapchat anymore?") triggered a 6% stock drop, $1.3 billion in market cap erased in a day, and a multi-quarter user decline. A Change.org petition asking Snap to revert the redesign collected 1.2 million signatures.

The lesson: The lesson is not "redesigns are risky." It is that the answer was already in the data — sentiment was collapsing in unstructured channels weeks before the tweet — and nobody on the team had the spreadsheet that joined it. Marketing decisions get worse the further the analyst sits from the data. SheetAI never asks you to upload your customer file to a chat box. It operates on live ranges, every change is annotated, and every step is reversible.

Source: TIME — Kylie Jenner Snapchat Stock Loss (2018)

Who it's for

If you've ever rebuilt the same Monday rollup three weeks in a row, this section is for you.

CMOs

Need a defensible single number for ROAS, CAC, and pipeline contribution — not five tools that disagree.

Performance Marketers

Rebuild the same Meta + Google + LinkedIn rollup every Monday, then watch the formulas break when an account adds a placement.

Marketing Ops

Spend half the week normalizing UTM strings, deduping leads, and patching the join between HubSpot and the data warehouse.

Demand Gen Leads

Need attribution that survives a board meeting — not a last-touch model the CFO can pick apart in 30 seconds.

Brand & Content Managers

Want to know which posts moved the needle, but the social tools, GA4, and HubSpot do not share a primary key.

Real marketing workflows

The exact prompt, the formula it writes, and the result you'd present in a QBR.

marketing_workbook.xlsx — SheetAI
You ask
“In Campaigns!A:H I have raw UTM strings from Meta, Google, and LinkedIn — utm_source has 47 variants of "facebook", "FB", "Meta", "fb_paid". Canonicalize utm_source and utm_medium into a new column, then build a sheet called Channel_Rollup with sessions, leads, CPL, and ROAS per canonical channel.”
SheetAI does
  • ✓Reads the unique values in utm_source and utm_medium and proposes a canonical mapping.
  • ✓Writes a SWITCH formula across the column to apply the mapping.
  • ✓Builds a pivot-style rollup sheet with sessions, leads, CPL, and ROAS by canonical channel.
  • ✓Flags rows where the canonical channel could not be inferred for review.
Formula written
=SWITCH(LOWER(TRIM(B2)), "facebook", "Meta", "fb", "Meta", "meta", "Meta", "fb_paid", "Meta", "google", "Google", "g_ads", "Google", "linkedin", "LinkedIn", "li", "LinkedIn", "Other")
Result

38,142 of 38,461 rows mapped automatically. 319 exceptions surfaced with a one-click "add to mapping" review — the rollup is one source of truth, not three pivot tables that disagree.

Everything marketing teams need, in one chat box

Stitch the data you already pay for into a single sheet you can defend. Normalize UTMs, run attribution, score creative, and build the QBR pack with formulas a marketing-ops lead can audit.

✓UTM normalization & channel rollup
✓Multi-touch attribution waterfalls
✓A/B test significance & sample size
✓Channel LTV / CAC / payback
✓SEO opportunity scoring
✓Cross-tool lead deduplication

Plays well with your stack

  • GA4 / Looker Studio CSV exports
  • Meta Ads & Google Ads CSV reports
  • HubSpot, Salesforce, Marketo lead exports
  • LinkedIn Campaign Manager exports
  • Search Console & Ahrefs / Semrush CSVs
  • Mixpanel, Amplitude, Heap event dumps

What a launch sprint looks like

A representative two-week mid-funnel campaign sprint, before and after SheetAI lands in the workflow. The "before" mirrors the manual reporting cycles that 40% of marketers cite as their #1 challenge in the 2025 Adverity benchmark; the "after" reflects what our marketing customers report after their second campaign on the platform.

✕

Before SheetAI

~44 hours
  • Mon
    Pull GA4, Meta, Google, LinkedIn exports; fight schema drift
    ~7h
  • Tue
    Hand-canonicalize 47 utm_source variants; rebuild rollup
    ~9h
  • Wed
    Lead-to-deal join in HubSpot; reconcile against Salesforce
    ~8h
  • Thu
    Multi-touch attribution waterfall in Excel; argue with finance
    ~10h
  • Fri
    Write A/B test analysis; check significance by hand
    ~6h
  • Sat
    QBR pack edits and re-cuts after CMO feedback
    ~4h
✓

With SheetAI

~9 hours
  • Mon
    AI normalizes 38,142 of 38,461 UTM rows; flags 319 for review
    ~2h
  • Tue
    Reviewer pass on flagged rows; rollup auto-rebuilds
    ~2h
  • Wed
    U-shaped attribution on 4,217 leads in 12 minutes
    ~1.5h
  • Thu
    A/B significance + LTV/CAC by channel auto-drafted
    ~2h
  • Fri
    QBR pack exported with full formula trail; done by 3pm
    ~1.5h

~80% reduction in launch-week hours, on a representative mid-funnel campaign.

What SheetAI will not do

A marketing tool that is honest about its limits is the only kind worth installing. Some decisions belong to humans, full stop.

✕

Reallocate spend on its own

SheetAI proposes a budget reallocation. A human approver clicks the button in Meta Ads Manager. We will not touch a spend API, full stop — autonomous bid changes are how a $40K campaign becomes a $400K mistake at 2am.

✕

Process PII without consent flags

Customer PII (emails, phone numbers, addresses) is treated under GDPR / CCPA. SheetAI honors consent and suppression flags and will not enrich a contact that is not opted in. It will surface ambiguity rather than guess.

✕

Make creative decisions

It will tell you which creative variant beat which. It will not tell you what the next ad should say. Brand voice belongs to a human; the AI does the math, your team does the judgement.

✕

Send your customer data to a model trainer

We do not train on customer data. Files stay in your account. Pro plans add SOC 2 Type II controls, customer-managed encryption keys, and a contractual no-training clause. PII is never used to enrich a foundation model.

“
Monday rollup used to be three hours of UTM cleanup before I could even look at the numbers. With SheetAI the rollup is built by 9am and I spend the morning on what the numbers actually mean. The biggest win was not the time saved — it was that finance stopped arguing with my attribution.
— Director of Performance Marketing, Series C consumer brand

Frequently asked

Things marketing teams ask before they switch.

Is my customer data sent to a third-party AI?

›
Your spreadsheet stays in SheetAI. The AI receives only the cell ranges relevant to the action you requested — never the entire file by default. We do not train models on customer data, and Pro plans include enterprise-grade encryption at rest and in transit. PII is never used to train or fine-tune a foundation model.

Can SheetAI handle a year of GA4 events?

›
Yes. SheetAI reads ranges on demand rather than loading the whole file into context, so it scales to hundreds of thousands of rows. For very large attribution joins, the Python tool runs the matching server-side and writes results back to your sheet.

Will the formulas work in Excel and Google Sheets?

›
Every formula SheetAI writes is standard Excel / Google Sheets syntax — INDEX/MATCH, SUMIFS, SWITCH, NORM.S.DIST. There are no proprietary functions to install, and your file remains portable. The Excel add-in and Google Sheets add-on let you run the same prompts inside Microsoft 365 and Google Workspace.

How does this compare to a CDP or attribution platform?

›
A CDP is a database. An attribution platform is a black box. SheetAI is the spreadsheet you already use, made smarter. It does not replace a CDP if you have one — it makes the part of the workflow that escapes the CDP (the agency report, the partner CSV, the LinkedIn export) tractable. 30–40% of B2B touchpoints land in untracked channels; that is the gap we close.

Does it support multi-touch attribution out of the box?

›
Yes — see the U-shaped attribution workflow above. SheetAI also supports first-touch, last-touch, linear, time-decay, and W-shaped models. The choice is yours; the formula is auditable. Only 24% of B2B teams have working MTA today (Gartner 2025) — we are building for the other 76%.

How does this differ from Looker Studio or Tableau?

›
BI tools are great at displaying data once it is clean. They are not great at making it clean. The hard part of marketing analytics — UTM normalization, lead dedup, attribution joins — happens before the dashboard. SheetAI works on the cells; the dashboard renders the result.

Can a non-technical marketer use this on day one?

›
Yes — that is the design target. If you can describe what you would normally ask a marketing-ops analyst to do, you can ask SheetAI. The 87% of marketers who have already adopted some form of generative AI (Salesforce 2026) are the audience; the AI just needs to see the cells, not screenshots of them.

What about GDPR / CCPA compliance?

›
Customer PII stays in your account. SheetAI honors consent and suppression flags in your data and will not enrich, export, or transmit PII to external services without your explicit instruction. Pro plans include a contractual DPA and EU data residency.

How is this different from copy-pasting CSVs into ChatGPT?

›
SheetAI works against live cell references — it writes formulas, not screenshots of formulas. Edits are reversible per action, every change is annotated, and the model can iterate on its own output by reading the result back. Pasting a CSV into a chat tool gives you advice; SheetAI gives you a finished sheet.

Sources & further reading

Every benchmark and statistic on this page is drawn from publicly available research. We cite our sources because we read theirs.

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Stop wrangling.
Start optimizing.

Open SheetAI, drop in your GA4 export and your Meta Ads CSV, and watch the channel rollup build itself. Free forever for the first 20 credits a day — no card required to find out whether it works on your data.

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