The wrong problem most analytics tools solve
The analytics tooling market has spent the last decade getting extremely good at one thing: making data look beautiful. Cleaner charts, faster queries, prettier dashboards, more granular filters. Each generation of tools is a genuine improvement on the last — and yet, talk to almost any e-commerce operator and you'll hear the same complaint: they still don't know what to do next.
That gap is the problem we set out to solve. Not "how do we visualize this data better," but "how do we get from this data to an action someone can take today." Those sound like the same problem. They're not, and conflating them is, in our view, the core mistake most of this category makes.
The test we apply to everything we build
Early on, we adopted a simple filter for deciding what to build: for any chart, metric, or feature we were considering, we asked — if someone looks at this, what would they actually do differently? If the honest answer was "nothing, they'd just feel informed," we either reworked it or didn't ship it.
This sounds obvious in theory and turns out to be surprisingly hard in practice, because a lot of analytics work optimizes for things that feel valuable but aren't actionable: completeness, granularity, customizability. You can spend months building a dashboard that lets someone slice their data nine different ways, and still leave them exactly where they started — staring at numbers, unsure what the numbers are telling them to do.
What we built instead
Applying that filter consistently led us to a few specific decisions that shaped the whole product:
Insights come with a recommendation attached
We don't just show that ROAS dropped on a channel — we say what we'd do about it, in plain language, the way an analyst would explain it to you across a desk.
Continuous monitoring, not scheduled reports
A decision that needs to happen today shouldn't wait for Monday's weekly sync. If something needs your attention, you should hear about it the day it happens.
Cross-channel by default, not as an add-on
Decisions about budget and inventory rarely live inside a single channel's data. Unifying Shopify, Amazon, Meta, and the rest isn't a feature — it's the starting point.
Fewer charts, more verdicts
We'd rather show you three things that genuinely need a decision than thirty things you have to interpret yourself.
A data platform's job isn't to make information available. It's to make the right action obvious. Those are different goals, and most of the industry has quietly optimized for the first one while calling it the second.
What this philosophy trades away
Building this way isn't free, and it's worth being honest about the trade-offs. A decisions-first product is, by definition, more opinionated than a pure dashboarding tool. We're making a judgment call about what matters enough to surface — which means sometimes we'll flag something you don't care about, or stay quiet on something a power user would have wanted to dig into manually.
We think that trade is worth making for the vast majority of e-commerce teams, who don't have a dedicated data analyst sitting between the numbers and the decision. But if your team's primary need is maximum flexibility for ad-hoc, exploratory analysis — slicing data nine different ways to find a pattern nobody's looked for yet — a general-purpose BI tool will always give you more room to wander. We're not trying to be that tool. We're trying to be the one that tells you what to do on a Tuesday morning, in the ten minutes before your first meeting.