ANALYTICS AUDIT — $3,497 · 10-DAY SPRINT

Jake McMahon — ProductQuant

8+ years B2B SaaS · Behavioural Psychology + Big Data (Masters)

Know exactly which data to trust, which gaps to fix, and in what order — in 10 days.

A 10-day audit that reviews your entire analytics stack and tells you exactly what’s broken, what it’s costing you, and what to fix first.

Book the Audit → See what’s included ↓

5 actionable improvements worth more than the fee — or full refund · 10-day delivery

WHAT GETS AUDITED

Stack assessment Tool config, event taxonomy, data quality, dashboard setup

Event audit Every event reviewed — status, issues, recommendations

Gap analysis 5–10 biggest measurement gaps sized by revenue impact

Fix roadmap Dev-ready specs, prioritised by impact vs. effort

60-min walkthrough Walk through every finding and prioritise what to fix first

$3,497 · fixed price · 10-day sprint

YOU ALREADY KNOW THE DATA IS WRONG

Three people, three answers to the same question

“Someone asked how many users activated last month and three people gave three different numbers. Events fire inconsistently. Properties are missing. We stopped looking at dashboards because the data doesn’t match reality.”

VP Product — B2B SaaS, $8M ARR

Instrumented what was easy, not what matters

“We tracked button clicks, page views, generic events. But the questions that actually drive decisions — which features correlate with retention? Where do users drop off? — can’t be answered with what we have.”

Head of Product — Series B

“Fix analytics” has been on the backlog for two quarters

“Every retrospective ends the same way: we should fix our tracking. But nobody knows where to start. Engineers don’t know what to instrument. Product doesn’t know what to ask for. The gap between the data we have and the data we need keeps growing.”

Product Manager — B2B SaaS

Dashboards exist but nobody opens them

“We built 12 dashboards last year. I checked the view count — most haven’t been opened in weeks. The numbers don’t match what CS sees. Nobody trusts them, so nobody uses them.”

CEO — $5M ARR

WHAT THE AUDIT TYPICALLY FINDS

Most analytics stacks have more broken events than useful ones.

Most events are either broken, duplicated, or tracking actions nobody uses for decisions.

In a typical audit, fewer than 20% of tracked events answer a question anyone cares about. The rest are noise that makes your dashboards less trustworthy, not more.

The highest-value features often have no instrumentation at all.

The features driving expansion revenue, retention, or activation rarely have the event coverage to prove it. You can’t tell who uses them, when, or how often — so you can’t double down on what works.

Activation and retention metrics that don’t actually predict anything.

Teams define activation around a feature milestone — “completed onboarding” or “created first project” — and discover most churned users also “activated.” The metric is meaningless, and the real predictor is buried deeper in the funnel.

Revenue impact of each gap is unknown — so the fix never gets prioritised.

Your team knows analytics is broken but can’t justify the engineering time because nobody has sized the cost of each gap. The audit sizes every gap by the revenue it obscures — so the business case writes itself.

WHY AN EXTERNAL AUDIT

Your team built the analytics stack. They can’t audit their own assumptions.

The people who instrumented your product made reasonable decisions at the time. But those decisions accumulated into an event taxonomy that reflects engineering convenience, not product questions. An internal fix starts from the same assumptions that created the gaps. An external audit starts from the questions your team actually needs answered — and works backward to the events required to answer them.

Your PM gets a gap analysis sized by revenue impact. Your engineer gets exact event names, properties, and implementation specs. Your team gets a ranked roadmap they can execute without a meeting to explain it. Everyone works from the same document, pointed at the same priorities.

TIMELINE

From read-only access to a ranked fix roadmap your team can act on immediately.

DAYS 1–2

Stack Assessment

Read-only access to your analytics platform. Tool configuration, event taxonomy, data quality, dashboard setup, and integration health reviewed. No write access, no code changes.

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DAYS 3–5

Event Audit

Every event reviewed by hand. Which ones are useful, which have broken properties, which critical user actions have no tracking at all. Full inventory with status and recommendations.

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DAYS 6–7

Gap Analysis

The 5–10 biggest questions your analytics can’t answer today. Each gap sized by revenue impact. This is what the fix roadmap is built on.

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DAYS 8–10

Roadmap + Walkthrough

Dev-ready specs for each gap. Prioritised by impact vs. effort. 60-minute walkthrough call. Recording included so your team can reference it during implementation.

Day 11: your team ships the fix that recovers the most blind spots.

WHAT YOU GET

Every gap sized by the revenue it costs you. Every fix ranked by what it recovers.

Days 1–2 · Assessment

Stack Assessment Report

A clear picture of what your analytics can and can’t tell you today. Tool configuration, integration health, dashboard setup, and where the reliability gaps are — so you know exactly which numbers to trust and which to ignore.

Tool configuration reviewed against best practices
Integration health checked — are events actually arriving?
Dashboard reliability scored — which ones reflect reality
Data quality issues flagged before they corrupt decisions

Days 3–5 · Audit

Event Audit Spreadsheet

Every event reviewed by hand: which are useful, which have broken properties, which critical user actions have no tracking at all. Your team sees at a glance what to keep, what to fix, and what to add — no guessing about where to start.

Every event: status, issues, and specific recommendations
Broken properties identified with fix instructions
Missing critical actions flagged with revenue context
Duplicate and redundant events marked for cleanup

Days 6–7 · Analysis

Gap Analysis — 5–10 Gaps Sized by Revenue

The 5–10 biggest questions your analytics can’t answer today, each sized by the revenue impact of the blind spot. This is what justifies the engineering time — your team sees the cost of not fixing each gap.

Each gap sized by revenue it obscures, so engineering gets prioritised
Questions your data can’t answer today, made explicit
Connection between measurement gaps and business outcomes
8–12 pages, specific and actionable

Days 8–9 · Roadmap

Implementation Roadmap with Dev-Ready Specs

For each gap: exact event names, properties, dashboard charts, and implementation steps. Prioritised by revenue impact vs. engineering effort. Your dev team can follow it without a meeting — the spec is the meeting.

Exact event names and properties your engineers add next
Dashboard charts to build, with the queries behind them
Prioritised by revenue impact vs. implementation effort
Quick wins separated from structural changes

Day 10 · Walkthrough

60-Minute Walkthrough Call + Recording

A live session where every finding is walked through, questions are answered, and priorities are confirmed. Recording included so your engineers can reference it during implementation. Your team leaves knowing exactly what to build and in what order.

Full findings walkthrough with data at each point
Gap analysis explained with revenue context
Roadmap priority discussion — challenge the ranking
Recording your team references during implementation

On the cost of bad data: every product decision made without trustworthy analytics is a coin flip dressed up as strategy. If your team ships 4 features per quarter and half of them miss because the data pointed the wrong way, that’s two quarters of engineering wasted on the wrong priorities. The audit pays for itself the first time your team ships the right fix instead of the loudest opinion.

FIT CHECK

Your dashboards exist. Your team doesn’t trust them. Here’s how to tell if this audit is the right move.

GOOD FIT

B2B SaaS at $3M–$30M ARR with analytics that nobody trusts

PostHog, Amplitude, Mixpanel, or similar running

The situation

You set up analytics months ago and have been adding events since. Dashboards exist but nobody opens them for decisions. Three people give three different answers to the same question. Your team knows the data is unreliable but can’t justify the engineering time to fix it because nobody has sized the cost of each gap.

What you leave with

Every event reviewed — what to keep, what to fix, what to add
The 5–10 biggest gaps sized by revenue impact — the business case for the fix
A dev-ready roadmap your engineering team executes without debate

Decisions backed by data your team actually trusts — starting the week after the audit.

NOT A FIT

Pre-product, no analytics tool, or data isn’t the bottleneck

Wrong stage or wrong problem

When this audit doesn’t apply

If you haven’t set up an analytics tool yet, there’s nothing to audit. If you have fewer than 1,000 monthly active users, the data volume may be too low to draw reliable conclusions. And if your analytics are solid but your team doesn’t know what to do with the data, the problem is upstream of instrumentation.

Better starting points

Activation Deep Dive

If activation is the specific problem and you need the funnel mapped end-to-end.

Focused Sprint

Answer a single product question when analytics is solid but insight is missing.

The Foundation

Full growth diagnostic across all six layers — analytics is one piece of that.

What this audit doesn’t cover

The Analytics Audit delivers the diagnosis and the ranked fix roadmap. Your team does the implementation. If you need the full picture — analytics plus retention, activation, competitive, and go-to-market — that’s a different engagement.

Implementing the fixes — your engineering team ships the instrumentation changes
Building dashboards from scratch — the audit specifies what to build, your team builds it
Ongoing analytics management — the audit delivers the roadmap, your team executes it

For the full growth diagnostic → The Foundation

Jake McMahon — ProductQuant

Jake McMahon

8+ years building retention, activation, and growth programs inside B2B SaaS · Behavioural Psychology + Big Data (Masters)

I run the audit myself. Not a team of analysts. Not an automated report generator. Every event reviewed by hand, every gap sized by me based on what I know about B2B SaaS activation, retention, and expansion revenue. The difference between a useful audit and a checkbox exercise is whether the person doing it knows what a good analytics setup actually looks like — and why most don’t.

Most audits hand you a list of things that are broken. This one hands you a ranked roadmap with revenue sizing and implementation specs. Your PM sees the business case. Your engineer sees the exact event names and properties. Nobody needs a meeting to translate the findings into action.

I won’t do this:

Deliver a generic best-practices checklist without reviewing your actual events
Flag gaps without sizing the revenue impact of each one
Recommend instrumentation changes without providing exact event specs
Treat every analytics platform the same — each has platform-specific issues to look for

What analytics platforms do you audit?

Amplitude, Mixpanel, PostHog, Segment, GA4, Heap, and custom-built stacks. Read-only access is all that’s needed. The audit process is the same regardless of platform — what changes is which platform-specific issues to look for. Custom stacks often have more gaps than managed platforms.

Teams Jake has worked with

PRICING

One price. A prioritised fix list your team acts on the following week.

$3,497

one-time · fixed price

10-day sprint

Stack assessment — tool config, data quality, integration health
Event audit spreadsheet — every event reviewed with recommendations
Gap analysis — 5–10 biggest gaps sized by revenue impact
Implementation roadmap — dev-ready specs, prioritised by impact
60-minute walkthrough call + recording
All assets formatted for your PM, designer, and engineer
Everything stays with your team permanently

At least 5 actionable improvements worth more than the fee — or full refund.

Book a 30-minute call →

At least 5 actionable improvements worth more than the audit fee — or full refund. If the data can’t support meaningful findings, we tell you in the first 2 days and scope what’s possible. The deliverable either exists or it doesn’t.

Questions.

Or book a call →

What analytics tools do you audit? +

Amplitude, Mixpanel, PostHog, Segment, GA4, Heap, and custom-built stacks. Read-only access is all we need. The audit process is the same regardless of platform — what changes is which platform-specific issues to look for.

Do we need to give you admin access? +

No. Read-only access is sufficient for the entire audit. We’ll specify exactly what we need at kickoff — typically a guest login or read-only API key. No write access, no code changes. The data stays in your systems throughout. Access can be revoked at any time after the audit.

What if we’re on a custom-built analytics stack? +

We audit the architecture regardless of tooling. Custom stacks often have more gaps than managed platforms — which means the audit typically surfaces more value, not less. The same process applies: event review, gap analysis, and dev-ready specs.

What do we own at the end? +

Everything. The stack assessment, the event audit spreadsheet, the gap analysis, the implementation roadmap, and the walkthrough recording. All formatted for your team to use directly. There’s no dependency on ProductQuant after the audit ends.

What happens after the audit? +

You get a prioritised roadmap. If your team has engineering capacity, you implement it yourself. If you want the full diagnostic across all six growth layers — analytics plus retention, activation, competitive, and go-to-market — the audit feeds directly into The Foundation. The audit work carries over and the cost is credited toward The Foundation.

What’s the guarantee? +

If we can’t find at least 5 actionable improvements worth more than the audit fee, full refund. No questions. In practice, the first 3 findings typically cover the cost. If the data genuinely can’t support meaningful findings, we tell you in the first 2 days and scope what’s possible.