Your Analytics Team is Fast. GenAI Makes It Unfair.

A few months ago, a CPG client came to us mid-planning cycle. They needed a channel-level ROI summary across eight markets the kind of output that feeds directly into their Q4 media budget decisions. Normally that’s a week’s job. Data pull, cleaning, MMM output reconciliation, narrative, deck. We ran it through a GenAI workflow we’d been building. They had a working first draft in four hours.

Their first reaction was, did you cut corners on the MMM? We hadn’t touched the model. We’d just stopped spending analyst hours on everything around it.

That’s the thing nobody talks about enough. The MMM itself, the modelling, the validation, the interpretation that’s maybe 30% of what an analytics engagement actually involves. The other 70% is wrangling data, formatting outputs, writing the story, building the deck, answering follow-up questions. GenAI doesn’t improve the model. It clears the road around it.

“The MMM was never the bottleneck. It was everything else, the data wrangling, the deck, the write-up that was eating the week.”

Where the Time Actually Goes

In a typical marketing analytics engagement, say a post-campaign MMM or a media effectiveness review, the actual modelling is a fraction of the total time. Most of it goes on things like pulling spend data from multiple media platforms, reconciling it against finance, cleaning GRP data that arrives in three different formats, building the baseline output into a readable story, and then translating that story into something a CMO can act on in a 45-minute meeting.

Here’s roughly how the hours broke down before GenAI entered the picture and what it looks like now:

The QA time barely moves, and honestly it shouldn’t. When you’re compressing everything else, the final sense-check on your MMM outputs and channel ROI numbers matters more, not less. That’s the part where a human needs to ask: does this actually reflect what happened in market?

The Six Places GenAI Actually Helps in Marketing Analytics

These aren’t hypothetical. They’re the specific areas where I’ve seen GenAI cut real time in marketing analytics work, with examples grounded in MMM, media planning, and campaign measurement.

What This Means in Practice

The teams I’ve seen get the most out of GenAI in marketing analytics aren’t the ones who rolled out the most tools. They’re the ones who were honest about where their delivery process was slowest and started there.

In most MMM engagements I’ve been part of, the model itself is ready well before the client sees anything useful. It sits there while someone finds the time to pull together the channel ROI tables, write the exec summary, build the media planning slides. That gap between insight being ready and insight being used is where decisions get made without you. GenAI closes that gap, not by making the modelling faster, but by making everything around it faster.

Same thing with incrementality. A geo-holdout result that takes two weeks to land in a deck is already old news by the time it reaches the media planning team. If GenAI can get that to a readable summary in a day, it actually influences the next campaign not the one after that.

The Honest Caveat

⚠ Worth mentioning clearly

GenAI saves time on the things it’s good at: pulling together media data, drafting MMM narratives, writing up incrementality results, building out scenario tables. What it can’t do yet is tell you that the TV coefficient looks suspiciously high because there was a product launch that quarter that the model didn’t account for. It can’t tell you yet that the client’s CFO won’t trust a result that contradicts what their agency told them last month. It can’t judge whether a channel’s ROI looks low because the media actually underperformed or because the data came in wrong. That’s still the analyst’s job. GenAI frees up time for that work. It doesn’t replace it.

Where to Start If You Haven’t Already

You don’t need to rebuild anything. Pick one thing your team produces regularly that is mostly formatting and writing rather than actual thinking a campaign report, a monthly channel performance summary, a post-MMM readout and try running it with GenAI alongside your normal process for a few weeks. See what the output looks like. See how long it takes.

In my experience, the MMM narrative is the best place to start in marketing analytics. Not because it’s the easiest win, but because it’s the most visible one. When a CMO gets the post-model story two days earlier, and it’s cleaner than usual, they notice. That moment of trust is what opens the door to doing more.

The marketing analytics teams that will be hardest to compete with aren’t necessarily the ones with the biggest headcount or the most sophisticated models. They’re the ones who figured out how to combine solid analytical thinking with GenAI speed and made that a normal part of how they deliver, not a pilot project that never quite scaled.

TL;DR

How is your team using GenAI in analytics?

I’m curious where people are seeing real-time savings and where it hasn’t lived up to the hype. The honest experiences are always more useful than the polished case studies. Drop a comment or message me directly.

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