Product Analytics Audit

Find what's broken before it breaks your decisions.

01 / 18
productquant.dev
81%

of analytics implementations contain errors that affect real product and growth decisions.

Napkyn / Kissmetrics research

02 / 18
productquant.dev

Broken tracking doesn't stay broken in one place.

M1
Month 1
Wrong data
Events fire incorrectly. Numbers look plausible. Nobody notices.
M3
Month 3
Wrong decisions
Dashboards built on bad data. Leadership makes strategy calls.
M6
Month 6
Wrong experiments
A/B tests run. Attribution is off. "Nothing works" becomes the narrative.
M12
Month 12
Wrong roadmap
Priorities set from patterns that never existed. Engineers build the wrong things.
Cost of not catching it early
03 / 18
productquant.dev

Six of the most common findings.

Double-firing events
The same event fires twice per action. Counts are inflated. Funnels show conversion rates that are impossible.
Events not firing at all
Critical user actions are never captured. Entire segments of the user journey are invisible in your data.
Inconsistent naming conventions
The same action has three different event names depending on which engineer instrumented it.
Broken attribution
UTM parameters dropped or overwritten mid-session. Paid channels appear to underperform. Budget decisions follow.
Missing consent configuration
Tracking fires before consent is given. A GDPR or HIPAA exposure waiting to surface — usually during a fundraise or audit.
No event governance
No taxonomy doc. No owner. Events added ad hoc for 18 months. The data is technically there but unusable at scale.
04 / 18
productquant.dev
Healthcare SaaS case

8 categories of PHI exposure found in a single PostHog instance.

Autocapture
High risk
GeoIP enrichment
Medium
Raw IP address storage
High risk
JWT tokens in URLs
High risk
Session recording
High risk
Console log capture
Medium
Person properties leak
High risk
Distinct ID as email
High risk
05 / 18
productquant.dev
The outcome

What remediating the PHI exposure delivered.

>99%
Reduction in PHI exposure events within 30 days of implementing the audit recommendations.
93%+
Reduction in compliance overhead cost — eliminating manual review processes that existed to compensate for bad data hygiene.

Healthcare forms platform, 2025. No name used at client's request.

06 / 18
productquant.dev

What a proper audit actually covers.

01
Event taxonomy review
Are events named consistently? Is there a taxonomy? What's missing?
04
Attribution and UTM audit
Are sources tracked correctly? Are UTMs preserved across the session?
02
Tracking integrity check
Double-fires, missing events, session breaks — every structural error surfaced.
05
Governance review
Who owns the event taxonomy? What's the process for adding new events?
03
PHI / PII exposure scan
Every vector where personal or health data could leak into your analytics platform.
06
Prioritised recommendations
Not just a list of problems — a sequenced fix plan with effort and impact ratings.
07 / 18
productquant.dev

What you receive. What you walk away with.

01
Annotated event audit
Every tracked event reviewed, categorised, and annotated with status.
02
Broken events log
A complete list of misfiring, double-firing, or missing events — with root cause.
03
PHI / PII exposure report
Every exposure vector identified, severity-rated, and paired with a remediation step.
04
Taxonomy recommendations
A clean, standardised event taxonomy you can hand to your engineering team.
05
Priority fix list
Sequenced by impact and effort. What to fix first, and why.
06
Two review calls
Kickoff to align scope. Final readout to walk through every finding together.
10 days from kickoff to final delivery
08 / 18
productquant.dev

What decisions have you made in the last 6 months based on your analytics?

How confident are you those numbers are right?

09 / 18
productquant.dev

8 questions to ask any analytics vendor or auditor.

If they can't answer these, move on.

01 Do you check for double-firing events, or only event presence?
05 Do you audit attribution and UTM parameter integrity across sessions?
02 How do you scan for PHI/PII exposure — and what's your remediation process?
06 What does your taxonomy recommendation look like? Can you show an example?
03 Will you review our consent configuration against GDPR and HIPAA requirements?
07 What does your deliverable look like — and who on my team can action it?
04 Do you produce a prioritised fix list, or just a findings report?
08 What's your experience with our specific platform — PostHog, Mixpanel, Amplitude?
10 / 18
productquant.dev

Three red flags in your current analytics setup.

FLAG 01
Your dashboards were built by the same person who set up the tracking
Confirmation bias is structural. Errors in the data layer get baked into the dashboards that are supposed to surface them. Nobody outside the team has ever verified the underlying events.
FLAG 02
Your analytics instance has been running for over 12 months without a formal review
Products change. Features get added. Events that were correct 12 months ago no longer map to user flows that no longer exist. The data keeps accumulating. The drift keeps compounding.
FLAG 03
You handle any health, financial, or personally-identifiable data — and you've never audited your tracking for exposure
Autocapture, session recording, and URL parameters are the most common unintentional exposure vectors. They're enabled by default in most tools and almost never reviewed.
11 / 18
productquant.dev

The audit process — week by week.

Week 1
Discovery & access
Kickoff call to align on scope and access requirements
Read-only access to analytics platform granted
Event inventory pulled — all events, properties, and volumes
PHI/PII scan begins against all active capture configurations
Week 1.5
Deep analysis
Double-fire and integrity checks across all critical user flows
Attribution and UTM audit across all acquisition channels
Consent configuration reviewed against regulatory requirements
Taxonomy gaps identified and documented
Week 2
Delivery & readout
All 6 deliverables compiled and formatted
Priority fix list sequenced by impact and effort
Final review call to walk through every finding
30-day async support for implementation questions
12 / 18
productquant.dev

"We already checked it."

Checked what? Most teams check their dashboards — not the underlying event data. Dashboards will always look fine if the errors are symmetric. The breakage lives one layer below what most people look at.

60% of analytics problems are invisible at the dashboard level
81% of implementations contain errors that affect real decisions
The gap between "looks fine" and "is fine" is exactly what the audit closes
13 / 18
productquant.dev

"Our dev team can do this."

In-house dev team
Time cost: Days of engineering time pulled from the roadmap
Framework: No structured audit process — ad hoc investigation
PHI scan: Unlikely — most engineers don't know what to look for
Deliverable: Internal notes, no client-ready output
Bias: They built it — hard to audit your own work objectively
ProductQuant audit
Time cost: Zero engineering time. We work from read-only access.
Framework: Structured 6-area methodology built for product analytics
PHI scan: Dedicated scan across all 8 common exposure vectors
Deliverable: 6 structured documents your team can action immediately
Bias: External — we have no interest in the data looking healthy
14 / 18
productquant.dev

This is for you if —

✓
B2B SaaS at $500K+ ARR
You have enough product complexity and user volume that bad data has real consequences for how you prioritise.
✓
Currently using PostHog, Mixpanel, or Amplitude
These are the three platforms we audit. Deep familiarity with each — including the default configurations that create exposure.
✓
Making product decisions from analytics data
Roadmap prioritisation, growth experiments, retention analysis — any decision that depends on the numbers being right.
15 / 18
productquant.dev

This is not for you if —

✕
You're pre-product or pre-analytics
If you haven't implemented any tracking yet, start with an implementation spec — not an audit. We can help with that separately.
✕
You don't have an analytics implementation yet
There's nothing to audit. An audit assumes you have events firing — and you need to understand whether they're firing correctly.
✕
You're looking for a dashboard builder
We don't build dashboards. We audit the data that feeds them. If the underlying events are wrong, no dashboard will fix the problem.
16 / 18
productquant.dev
The offer
Analytics Audit — $3,497
What's included
✓Annotated event audit (all events reviewed)
✓Broken events log with root cause
✓PHI / PII exposure report with remediation steps
 
✓Taxonomy recommendations for your engineering team
✓Priority fix list — sequenced by impact and effort
✓Two review calls + 30 days async support
Book a free scoping call · productquant.dev/offers/analytics-audit
17 / 18
productquant.dev
productquant.dev/offers/analytics-audit

Book a free scoping call

18 / 18
productquant.dev