What AI can (and can't) do for marketing analytics

AI in marketing analytics, plainly: what AI tools do well with ad and sales data, where they make mistakes, and how to check an AI answer before you act.

By the Matrix ROI team at Smith App StudioOctober 18, 20263 min read

The short answer

AI is good at the tedious parts of marketing analytics: summarizing what changed, answering plain-English questions about your data, spotting unusual swings, and drafting reports. It is weak at knowing your business context and can state wrong numbers confidently. Use it to speed up analysis, and verify every figure it gives you against the source data.

AI saves time on summarizing, querying and drafting, not on judgment.
Language models can state wrong numbers fluently, so check every figure.
AI is only as good as the data underneath; garbage in, confident garbage out.
Prefer tools that show which data each answer came from.

of organizations reported using AI in 2024, up from 55% in 2023: 78%; of organizations reported using AI in 2024, up from 55% in 2023; Stanford HAI, AI Index 2025
of CMOs say generative AI delivers return through time efficiency (2025): 49%; of CMOs say generative AI delivers return through time efficiency (2025); Gartner, via Silicon UK press release
of CMOs say generative AI investments are not currently a priority (2025): 1%; of CMOs say generative AI investments are not currently a priority (2025); Gartner, via Silicon UK press release

Where businesses stand with AI

AI use in business has spread quickly. Stanford's 2025 AI Index reports that 78% of organizations reported using AI in 2024, up from 55% the year before. In marketing specifically, Gartner's 2025 CMO Spend Survey found that just 1% of CMOs said generative AI investments are not currently a priority.

The returns marketing leaders report are mostly about speed. In the same Gartner survey, CMOs said generative AI was delivering return through improved time efficiency (49%), improved cost efficiency (40%), and more capacity to produce content or handle business (27%). Notice what is missing: better decisions. AI helps you get to the numbers faster. Whether the numbers are right, and what to do about them, is still on you.

What AI does well

Generative AI tools, the kind you talk to in plain sentences, are useful in marketing analytics wherever the work is reading, sorting and summarizing:

Answering questions in plain words, such as "which campaign had the lowest cost per sale last month?", without you building a report.
Summarizing what changed since yesterday or last week, in a few sentences instead of a dozen charts.
Flagging unusual movements, like a cost per sale that doubled overnight or a day with zero conversions.
Drafting the written part of a client or team report for a person to edit.
Cleaning up messy exports: renaming columns, spotting duplicates, matching campaign names that are spelled differently.

Where CMOs say generative AI is paying off, 2025

Time efficiency49%
Cost efficiency40%
More content or business capacity27%

Share of CMOs in Gartner's 2025 CMO Spend Survey citing each way generative AI investments are delivering return.

Source: Gartner 2025 CMO Spend Survey (press release, Silicon UK copy)

Where AI goes wrong

The biggest risk is that large language models, the technology behind chat assistants, generate text that sounds right. They do not look up a number the way a spreadsheet formula does unless the tool is built to make them. Ask a general chat assistant about your ROAS and it may give you a precise, confident figure that does not match your data. This is often called a hallucination.

Other limits matter just as much. AI cannot know that your best product was out of stock, that a competitor ran a sale, or that your website went down on Saturday. It also inherits every problem in the data it reads. If your ad platforms double-count sales, an AI summary of platform data will double-count them too, only in more polished sentences. Before trusting any AI analysis, make sure the inputs are sound; see why ad platforms claim more sales than you made.

Finally, be careful what you paste into public AI tools. Customer names, emails and order details may be personal data under privacy laws. Check the tool's data terms before uploading anything with customer information in it.

How to check an AI answer

Treat an AI answer like a report from a new junior analyst: probably useful, needs checking. A few habits catch most problems.

Ask where each number came from: which table, which date range, which filter.
Spot-check one or two figures against the platform or your Stripe or store records.
Watch the date range; AI tools often quietly assume "last 30 days" or the calendar month.
Be suspicious of round, tidy numbers and of answers that agree too neatly with what you hoped.
Ask the same question a second way and see whether the answer holds.

Picking an AI analytics tool

The useful question is not whether a tool has AI, but whether you can trust what it says. Look for tools that answer from your actual data rather than general knowledge, show their sources, and refuse to guess when the data is missing. They should also work on verified figures, such as sales from your own payment records, not only on what ad platforms claim.

Matrix ROI, developed by Smith App Studio, takes this approach: its AI analyst answers questions in plain words, checks every number in its answer against your data before you see it, and lists its sources. You can try it in the live demo. Whichever tool you pick, the AI should make it faster to reach a decision you can defend, not replace the checking. For the numbers worth asking about in the first place, start with the KPIs that belong on a small-business dashboard, and if you want reports generated for you, read what to automate and what to read yourself.

Frequently asked questions

Can ChatGPT analyze my marketing data?

A general chat assistant can summarize a spreadsheet you upload and suggest patterns, but it can also misread columns or state numbers that are not in your file. Check every figure it gives you against the original data, and avoid uploading customer personal information unless the tool's data terms allow it.

Will AI replace marketing analysts?

AI is taking over the repetitive parts of the job, such as pulling numbers, formatting reports and writing first-draft summaries. The judgment parts remain: knowing the business context, spotting bad data, and deciding what to do next. For small businesses, AI mostly means the owner can get answers without hiring a full-time analyst.

What is an AI hallucination in analytics?

It is when an AI tool states something that sounds plausible but is not true, such as a precise ROAS figure that does not match your data. Language models generate likely-sounding text, so unless a tool checks its answers against your actual records, a confident number is not proof of a correct one.

What should I look for in an AI marketing analytics tool?

Answers drawn from your own data, with sources shown for each number. A willingness to say when data is missing instead of guessing. Use of verified sales from your payment or order records, not only ad platform claims. Clear terms about how your data is stored and used.

Found this useful? Pass it on.

X LinkedIn Facebook Email

Sources

General information only, not legal, tax or financial advice. Figures belong to the sources linked above and were checked on the date shown. See our disclaimers.

Keep reading

Agencies & reportingAutomated marketing reports: what to automate and what to read yourself
Agencies & reportingThe marketing KPIs that belong on a small-business dashboard
AttributionWhy ad platforms claim more sales than you made

See what your ad platforms claim next to what your records prove.

14-day free trial. No card needed to start.

Start free trial Live demo