Reading the Data
Metrics, experiments, and telling signal from a number that just looks like one.
Your analytics fires at the system's moment of done, not the user's.
When Your Analytics Says One Thing and Your Users Say Another
When product data shows healthy engagement but user interviews surface consistent frustration, the problem is almost ne…
ReadYou know what your data is named, not what it represents.
Architecting a Scalable Customer Data Pipeline
How product events, data warehouses, and marketing automation connect - and where the decision points are that a PM nee…
ReadA local maxima arrives not as a failed experiment but a pattern across winning ones.
Escaping the Local Maxima in Product Optimization
When iterative A/B testing optimizes you into a corner, the signal is not a failed experiment - it is the shape of your…
ReadThe dashboard you inherited records what was easy to instrument, not what is true.
Setting Up Your First Analytics Stack as a New PM
Most new PMs inherit a broken analytics stack and spend months optimizing dashboards that measure the wrong thing. The…
ReadThe checkout flow is not where the repeat purchase decision is made.
Minimizing Build Complexity in A/B Test Design
Client-side A/B testing is structurally incapable of moving retention metrics - it is a button-color optimization progr…
ReadWaiting for significance in an 80-account environment is not rigor. It is avoidance.
Mitigating Data Ambiguity in Enterprise Business-to-Business Software as a Service
In enterprise B2B, statistical significance is the wrong target - your account base is too small, your weights are too…
ReadHigh experiment velocity can coexist with a low organizational learning rate for years.
Applying the Return on Time Invested Framework for Experiment Prioritization
Most experiment backlogs optimize for conversion lift. This article argues that a test resolving a strategic unknown is…
ReadShipping without an event spec is not a resourcing problem. It is a thinking problem.
Shifting Analytics Instrumentation Left in Agile Sprints
Most teams treat event tracking as a post-ship cleanup item. This article makes the case that if you cannot name the fi…
ReadNo tooling decision is going to fix a schema problem.
The Three Levels of Product Analytics Maturity
A diagnostic map of where your team's analytics practice actually sits - from basic event logging to behavioral cohorts…
ReadA number that cannot tell you what to change next is a mood board.
Translating Vanity Metrics into Actionable KPIs
The difference between a number that sounds good in a slide and a number that tells you what to do next - and how to co…
ReadThe metric that earns the lead is the one that can deliver an uncomfortable answer.
Designing the Vanity Metric Test for Executive Reporting
A structured three-gate test for deciding whether a metric belongs in an executive report - one that requires a denomin…
ReadA more expensive way to lose the same users you already had.
AARRR Is Not the Metric, It's the Diagnostic
AARRR is not five goals to chase at once. It is a way to find the one leak worth fixing first.
ReadMost experimentation is negotiation wearing a lab coat.
A/B Tests Settle Arguments, Not Questions
Most A/B tests are not experiments. They are negotiations with extra steps, and the result was decided in the room firs…
ReadA product can be enormous and shallow at the same time.
Adoption vs Tourism: Are Your Users Learning or Just Visiting?
A big weekly active number can hide the truth that most people tried your product and never came back.
ReadYou keep fixing the costume instead of the thing wearing it.
What Support Tickets Taught Me About Prioritization
A backlog of reported issues is not a fix list. The first job is to find out how few problems you actually have.
ReadIf your feature did not survive a holdout, you shipped the novelty spike.
Choosing the Right Experiment, Bandits, Holdouts, and Lift Tests
When a standard A/B test is the wrong tool, and how to choose between multi-armed bandits, holdout groups, and incremen…
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