Jake McMahon
Led by Jake McMahon 8+ years B2B SaaS · Behavioural Psychology & Big Data

Product analytics for B2B SaaS teams.

Product analytics should help your team answer product and revenue questions from user behavior. If it only produces dashboards, it is underbuilt.

This page is for teams trying to answer:

✓ Where users stall ✓ What drives retention ✓ Which changes actually work
See the checklist See the next step

Plain English first. Tools and implementation second.

Product Analytics, Broken Down

01 — Questions What the team needs to know to make better product decisions
02 — Events The behaviors worth measuring and how they are named
03 — Views Funnels, cohorts, dashboards, and trends tied to actual decisions
04 — Action What the team changes next because the signal is clear

If those four layers do not connect, the setup stays descriptive instead of useful.

DECISIONS MISSED
73%

of B2B SaaS product teams have dashboards but cannot explain why retention changed last quarter — the data exists, the decisions don’t.

COMMON ROOT CAUSE
1 layer

Most product analytics failures trace to a single gap: events designed for tracking, not for answering questions. One bad design decision compounds for years.

TIME TO VALUE
2 weeks

From analytics audit to a decision-ready dashboard layer your team actually uses — scoped, built, documented, and handed over.

WHY PRODUCT ANALYTICS STALLS

We have dashboards but nobody changes anything because of them

“We have PostHog and Amplitude running. We have twelve dashboards. But when we go into the product review meeting nobody can point to a chart and say ‘this is why we should build that next.’ The data is there. The decisions aren’t.”

Head of Product — B2B SaaS, Series B

The event taxonomy is a mess from three different teams

“Engineering tracks things one way, growth tracks things another way, and whoever set up the original PostHog did it a third way. When I try to build a retention cohort I spend two hours cleaning data before I can even start the analysis.”

Growth PM — PLG SaaS, $18M ARR

Activation looks fine on the dashboard but conversion is still broken

“Our activation funnel shows 68% completion. But free-to-paid conversion is 2.1%. Something is wrong with the definition, not the product. We just don’t know where the gap is in the measurement.”

VP Product — B2B SaaS, $30M ARR

We track everything but can’t answer the questions that matter

“We are capturing over 400 events. We can tell you which button every user clicked. But if leadership asks ‘which accounts are at risk this month’ or ‘does feature X actually drive retention’ — we have nothing clean to show them.”

Director of Analytics — SaaS, $45M ARR

CLIENT WORK

Product analytics in production

Healthcare SaaS — Migration
90%
analytics cost reduction

Mixpanel to PostHog: 906K Events, HIPAA-Compliant

A HIPAA-regulated healthcare forms platform migrated 4 years of event history to PostHog — preserving full data, cutting compliance costs from $60K/year to under $3K, and rebuilding the instrumentation layer from scratch.

Read case study →
Healthcare SaaS — Implementation
114
events · 13 dashboards built

Product Analytics From Scratch: Full Implementation

Full PostHog implementation — 114 custom events, 13 dashboards covering activation, retention, and feature adoption, with 37 UX issues surfaced from session replay data.

Read case study →
Healthcare SaaS — Compliance
8
PHI exposure categories found & fixed

HIPAA Analytics Audit: Compliance Risk Caught Before a Breach

Eight categories of Protected Health Information were being captured by the analytics setup undetected. Full PHI-free instrumentation strategy built and implemented before any breach occurred.

Read case study →

What It Is

Product analytics is not “tracking everything.”

Product analytics is the practice of measuring the product behaviors that explain activation, retention, monetization, and expansion. The point is not to collect more events. The point is to make better decisions with less guessing.

A useful product analytics setup helps your team answer a small set of questions clearly. Which users are reaching value? Which ones stall before they get there? Which features correlate with retention? Which accounts are growing and which ones are quietly drifting?

When the setup is working, product analytics gives product, growth, and leadership the same view of what is happening inside the product. When it is not working, the team gets dashboards, event sprawl, and debate.

Where Teams Get It Wrong

Most setups answer activity questions, not business questions.

The tools are usually there. The gap is between what the team tracks and what the team actually needs to know.

The team tracks clicks, not progress.

Plenty of setups log button presses and page views. Much fewer are built around activation, repeated value, upgrade behavior, or retention risk.

Dashboards exist, but nobody changes anything because of them.

That usually means the views are descriptive but not decision-ready. The team can observe movement, but not what to fix, test, or prioritize next.

The event taxonomy never became a real operating layer.

Inconsistent names, thin properties, and missing ownership make the data harder to trust every quarter, especially as more teams add instrumentation.

The setup explains the past, but not the next decision.

Product analytics is most valuable when it shortens the time between “something changed” and “the team knows what to do next.”

What Good Looks Like

Three signs the setup is actually useful.

01 — Clear Definitions

The team agrees on the moments that matter.

Activation, retained use, upgrade triggers, and churn signals are defined in plain language. Product, growth, and leadership are not using different meanings for the same metric.

02 — Trusted Instrumentation

The underlying event layer is stable enough to trust.

Names stay consistent. Properties are meaningful. Ownership is clear. New instrumentation makes the system sharper instead of noisier.

03 — Decision-Ready Views

The dashboards point to a next action.

The team can look at a funnel, cohort, or segment view and know whether to investigate onboarding, pricing, feature adoption, or retention behavior next.

How ProductQuant Approaches It

Start with the question, not the tool.

Most analytics debt starts because tracking was added screen by screen, not question by question.

ProductQuant approaches product analytics from the business questions backward. First define what the team needs to know. Then map the product behaviors that answer those questions. Then build the views and QA process that keep the setup usable as the product changes.

That means event design, dashboards, and tooling all serve the same goal: fewer arguments, clearer priorities, and better product decisions.

01 — Define

Start with the product question

Activation, retention, expansion, churn, or feature adoption. Name what the team actually needs to understand.

→
02 — Map

Design the event layer

Choose the behaviors and properties that answer the question without turning the taxonomy into clutter.

→
03 — View

Build the right analysis layer

Funnels, cohorts, dashboards, or segment views should point to a concrete next action, not a reporting ritual.

→
04 — Run

Keep it usable over time

Ownership, QA, naming discipline, and decision reviews stop the setup from drifting as the product evolves.

A cleaner setup means each new question is easier to answer than the last one.

Related Guides And Proof

Go deeper from here.

These are the most relevant ProductQuant assets if you want implementation detail, tooling guidance, or a cleaner tracking foundation.

Guides
Product Analytics Implementation Checklist
The practical checklist for event design, instrumentation, QA, ownership, and decision-readiness.
Guide Start here →
Product Analytics vs Marketing Analytics
Where the boundary sits and why mixing the two usually creates more confusion than clarity.
Guide Read →
Best Product Analytics Tools for SaaS
A practical read on common tool tradeoffs rather than a generic feature-list roundup.
Guide Compare →
Proof
Mixpanel to PostHog Migration
A real rebuild of the analytics layer with lower cost and a cleaner operating setup.
Case Study See →
Instrumentation From Scratch
A buildout covering events, dashboards, and UX issue detection before users hit the friction.
Case Study See →
JTBD-Focused Event Taxonomy Design
Why naming and property structure matter if you want analytics to reflect real user progress.
Guide Use →

Best Next Step

Pick the step that matches the gap.

This page is educational first. If you want help turning the ideas into a working setup, these are the most relevant ProductQuant paths.

Analytics Audit
Best when the data exists, but the team is not sure what to trust or what to fix first.
PostHog Health Check
A lighter first step if the team already uses PostHog and wants a fast read on what is missing.
Event Taxonomy Builder
For teams that need a cleaner naming, property, and tracking-plan structure before dashboards improve.
Product Analytics Program
For teams that need a broader product analytics system built and run, not just a one-time audit.
Jake McMahon — product analytics consultant

WHO DOES THE WORK

Jake McMahon

Founder, ProductQuant · MSc Big Data & Business Analytics · BSc Behavioural Psychology · 8+ years B2B SaaS

Jake has designed event taxonomies, built retention dashboards, and run activation analyses for B2B SaaS companies across healthcare, HR, fintech, and developer tools. Every engagement is run personally — not delegated to a junior analyst.

The behavioural psychology and data science background means analytics systems are designed for the questions that actually drive decisions: what behaviour predicts retention, where activation is breaking, and which features drive expansion. Not just technically correct instrumentation — analytically useful instrumentation.

Product analytics strategy Event taxonomy design Activation analysis Retention analysis Feature adoption Dashboard design PostHog B2B SaaS

COMMON QUESTIONS

Product analytics: what it is and what it should do

Questions about your specific setup? Book a call →

What is product analytics and what should it actually answer?+
Product analytics is the practice of measuring how users interact with your product — what they do, what they skip, where they drop out, and what separates users who succeed from those who churn. Done well, it answers: where should we focus next? Why is retention declining? Which features drive expansion? It is not about tracking everything. It is about having clean signals for the decisions that move your numbers.
What is the difference between product analytics and marketing analytics?+
Marketing analytics tracks what happens before the user signs up — ad performance, acquisition channels, conversion rates on landing pages. Product analytics tracks what happens after: activation, feature adoption, retention, and expansion. Most B2B SaaS teams invest heavily in marketing analytics and underinvest in product analytics, which means they know how users arrive but not why they stay or leave.
How do we know if our product analytics setup is working?+
Three tests: (1) Can your team answer “what percentage of new users reach the activation milestone this week?” in under 5 minutes? (2) When retention dips, does your team know within 48 hours which cohort it affects and a plausible cause? (3) Are product decisions being changed by data, or is data collected after decisions are already made? If any answer is no, your setup has gaps.
What events should a B2B SaaS product track first?+
Start with the activation sequence — the 3–7 steps a new user needs to complete to reach their first moment of value. Then add the key features associated with retention: the actions that, when completed in week 1, predict whether a user is still active in week 8. Everything else is secondary. Most teams track too many events too early and end up with noisy, unmaintained schemas that nobody trusts.
How long does it take to build a useful product analytics system?+
A working activation funnel and one retention dashboard can be built in 2 weeks if the tracking plan is clean. A full system — taxonomy, activation, retention, feature adoption, cohort analysis — takes 6–10 weeks depending on existing instrumentation. The bottleneck is almost never the tool. It is agreeing on definitions (what counts as an activated user?) and getting clean event data from engineering.
What is the best product analytics tool for B2B SaaS?+
PostHog is the best default for B2B SaaS teams that want full control, open source, and a single tool for product analytics plus session replay, feature flags, and A/B testing. Mixpanel is better for teams that need advanced funnel flexibility. Amplitude suits larger orgs with dedicated analytics teams. The choice matters less than the taxonomy underneath — bad event structure produces bad insights regardless of tool.

Product analytics should shorten arguments, not create new ones.

If your team has dashboards, events, and tools but still cannot answer the basic product questions cleanly, start with the checklist or the audit.

Read the checklist See the audit