Most analytics agencies give you dashboards. ProductQuant gives you decisions.

Most analytics work produces beautiful dashboards that nobody uses to make a decision. The problem isn't the dashboards — it's that analytics was never connected to an experiment engine in the first place.

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Analytics agency vs. ProductQuant — what you actually get.

Analytics Agency	ProductQuant
Builds dashboards around existing data	Redesigns event taxonomy before building anything
Reports on what happened	Predicts what will happen (churn model, expansion triggers)
Data as deliverable	Data as infrastructure for experiments
No experiment engine	Running experiment engine (statistically rigorous, success criteria defined upfront)
No competitive intelligence	Competitive Intelligence Database updated continuously
Engagement-dependent — outputs stop when billing stops	Infrastructure continues after engagement ends
Success = dashboard approved	Success = metric moved by a confirmed experiment
Analytics only — no GTM or positioning layer	Analytics + experiment engine + GTM copy + competitive intelligence

Dashboard theater — and how to recognize it.

Dashboard theater happens when analytics work produces beautiful outputs that nobody uses to make decisions. It's common. It's expensive. And it usually isn't the agency's fault — it's a structural problem with how the engagement was designed.

Warning sign 1: Your dashboard tells you what happened, not what to do.

Analytics that drive decisions answer "what should we change?" not just "what are our numbers?"

Warning sign 2: Your events were designed for the tool, not for your business questions.

If your event taxonomy was designed around what's easy to track, your insights will always be limited by that original scope.

Warning sign 3: You've never run an experiment that used your analytics as the source of truth.

Analytics disconnected from experimentation is reporting. Analytics connected to experiments is a growth system.

What ProductQuant builds instead.

Event Taxonomy (Decision-First)

Every event maps to a business decision. Designed around your questions, not around your tooling.

Decision-First Dashboards

Every dashboard answers a specific, pre-defined question. No vanity metrics. No orphaned charts.

ML Models From Your Data

Churn prediction trained on 50-85 behavioral features from your actual user data. Not a template — a model.

Experiment Infrastructure

Success criteria defined upfront. Statistically rigorous analysis. Sample size calculations before launch. Every test produces a definitive result.

How the economics compare.

Option	Total Investment	Timeline	What You Own After
Analytics agency	$96K-$240K/yr (ongoing)	Indefinite	Dashboards (stop working if vendor changes)
Analytics agency + growth consultant	$216K-$480K/yr (ongoing)	Indefinite	Dashboards + strategy document
Growth LAB	$21K-$42K	3 months	Analytics infrastructure + running experiment engine + intelligence database
Growth OS	$180K-$300K	6 months	Complete 7-framework growth OS

When an analytics agency is the right choice.

You have a well-designed event taxonomy and just need dashboard work
You're pre-Series A and need basic reporting before you're ready for a full growth system
You need a specific one-time reporting deliverable, not ongoing infrastructure

When ProductQuant is the right choice.

You have dashboards but they don't drive decisions
You need analytics connected to a running experiment engine
You want churn prediction — not churn reporting
You want analytics, experimentation, and competitive intelligence in one connected system

Common questions.

Can ProductQuant build on top of an existing analytics setup?

Yes. DISCOVER starts with a full audit of what you have. We rebuild what needs rebuilding and extend what's already reliable.

What analytics platforms does ProductQuant work with?

Mixpanel, Amplitude, PostHog, Segment, Rudderstack, BigQuery, Snowflake — and custom event pipelines. The tools are secondary to the instrumentation design.

Does ProductQuant replace our data team?

No. ProductQuant installs infrastructure your team owns and learns from. Most clients' data teams become more effective with a redesigned event taxonomy and a running experiment engine.

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See what decision-first analytics looks like.

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