Tried Pecan AI to Predict Campaign ROAS, Here’s How It Went

Okay, so here’s the deal. I’ve been working in marketing for a few years now. And while I’m comfortable looking at numbers, anything that has “machine learning” or “predictive modeling” in the description usually makes me close the tab. Too much setup, too much jargon, too little time.

Last week, I was asked to figure out if we could predict campaign performance before launch. Not in a vibe-check way. Properly. That’s when someone mentioned Pecan AI, and I gave it a shot.

Not as Complicated as I Expected

So, I logged in, expecting a ton of setup and confusion. But the first screen asked: What do you want to predict? Felt like a form I could fill.

I clicked on “Campaign ROAS Forecasting.” No long tutorials, just a guided setup. Kinda like Google Forms, but for data predictions.

The Data Part Was… Surprisingly Chill?

We had our data in BigQuery. I expected issues. Weird formats, columns missing, maybe some error that needed a data engineer. But nope. It connected, ran a few checks, and let me define what result I cared about (our ROAS cutoff was 1.2).

I didn’t need to clean every field. It gave me suggestions, and I could skip a few steps if I wasn’t sure. Honestly, I appreciated that.

Model Building Was Quiet in the Background

I didn’t have to “build a model” the way I thought I would. No coding. No graphs full of math. It ran something in the background and told me which factors mattered most. Like the ad set size, spend pattern, placement, stuff I could understand.

There was a progress bar and a “model quality” score. It felt like waiting for a file to render, not watching a machine do something mysterious.

Predictions Were Fast and Kind of Useful

After like 15 minutes, it showed a table with predicted ROAS for each campaign I’d uploaded. Some were flagged as “unlikely to hit target,” and honestly, it matched my gut. But now I had numbers to show the team.

We dropped two planned campaigns and rebalanced the budget. That alone saved us about ₹1.2L (~$1,500). Not a crazy number, but enough to feel like the tool did something real.

Stuff That Could Be Better

Just being honest here:

• Would’ve liked more control over how the model works, especially if I was more technical.
• Some smaller tools we used didn’t have integrations (we ended up exporting/importing CSVs manually).
• Pricing is transparent for smaller plans, but for larger stuff, you still need to “talk to sales.” Classic.

So… Would I Recommend It?

If you’re a full-on data scientist, probably not. You’d want more control, more depth.

But what if you’re a marketer, product person, or someone who deals with campaign performance and doesn’t want to ping the data team five times a day? Then, yeah, this is useful.

It doesn’t feel like magic. It feels like a tool that helps you skip guesswork.

Not life-changing. But time-saving.