Jake McMahon

Analytics · Growth · Measurement

$1,041,637

in recoverable revenue, found inside one billing platform’s failed-payment retry window. It took a query across 349,244 rows that nobody had run. The finding was arithmetic. The work was looking.

281 analytics events shipped for one platform, with property coverage audited at 0% before anyone read a chart.
312 competitor claims fact-checked across 14 companies, with 77% holding up and the rest corrected.
0 of 57 buying questions an assistant answered with the brand, measured before and after the work.

Most people wiring AI into a product cannot tell whether it works. I can.

That is the whole through-line. Precision and recall rather than a demo. A metric that came back at zero before anyone reported on it. Nine statistical tests with zero significant results, written up as such. Two sources disagreeing about the same site by a factor of five, both published with the disagreement intact.

I build the systems that produce and prove the numbers: instrumentation, taxonomies, dashboards, churn models, and the content operations that turn finding into pipeline.

Eight years of it, for FormDR, Scale Insights, Net Atelier, HackingHR, DialMyCalls, CYE, Spikerz, Tasq and Willow, and before that agency-side delivery for monday.com, Guardio and CirclesUp.

Nine things I do

Ordered by how often they are the actual problem.

01

Product analytics

Instrumentation design, event taxonomies, property audits, dashboard programmes, and migrations between platforms.

02

Growth and activation

Defining activation, finding time to value, onboarding across plan configurations, lifecycle email, win-back.

03

Retention and churn

Rule-based, ML and behavioural models, driver ranking, dunning and recovery, expansion surfaces.

04

SEO, AEO and GEO

SERP research, answer-box structure, and getting cited by AI engines. Priced per query, logged per call.

05

Content systems

Knowledge bases, claims registries, lint layers, reviewer roles and publishing gates. The machine, not the tips.

06

Machine learning

Classifiers, evaluation sets, calibration, confidence routing, and telling the difference between a model and a regex.

07

GTM and positioning

Market selection, beachhead, ICP before TAM, pricing architecture, sales motion as a constraint rather than a choice.

08

Data engineering

Warehouses, pipelines, joins that survive contact with reality, statistical testing, and privacy as a design constraint.

09

Engineering

Compliance architecture, test harnesses, and the plumbing that makes the other eight possible at volume.

Selected work

Named where the relationship allows. Every figure here comes from the same registry the reporting did.

FormDR

PostHog instrumentation and a property audit, Stripe revenue intelligence, a churn-prevention flow built inside a vendor’s experience limit, and JTBD research across 60 recorded sales calls.

281 events · $252,139 MRR modelled · $1.04M dunning window

Scale Insights

Amplitude verification against source data, an eleven-dashboard programme built from Python, a ClickUp board generated through an agent, and a deterministic tagging engine with a model explaining each recommendation.

11 dashboards · 39 charts · 2,100 lines

Net Atelier

GTM strategy, a three-scenario financial model validated against industry benchmarks, a competitive teardown with verified pricing, and a board deck fact-checked claim by claim.

30 claims checked · 6 fabricated · 6 competitors priced

CYE

A search and AI-citation programme: per-piece SERP gap analysis, an evidence registry with verbatim quotes, and a research stack metered to the cent.

19 pieces gap-analysed · 285 keywords · whole programme under $2

HackingHR

Reconciling two systems that disagreed about the same customer base by 2,868 users, an ML churn model, and an engagement analysis that separated machine clicks from human ones.

2,868-user discrepancy reconciled · 45% reported against 18% real

DialMyCalls

The first behavioural baseline the business had ever had, built from raw billing and usage, and the leak it surfaced on day one.

$1,300/month leaking, found in about two hours

Four frameworks I work from

Not productised packages. The diagnostics that decide what the work actually is before anybody commits to it.

Product DNA

A product’s structure determines what is possible. A strategy that fights it fails without ever being wrong.

  • Buyer and user as two cycles running at different speeds
  • User topology, and which of your two audiences is instrumented
  • Moat type as an architectural constraint, not a preference
  • Archetype, activation pattern, and complexity against time to value

GTM DNA

Market, motion and channel fit are constraints before they are choices. Most go-to-market plans are a motion chosen for reasons unrelated to the product.

  • Market DNA: where the product can actually win
  • Beachhead selection, and why the first market matters more than the total
  • ICP before TAM, so the market size means something
  • The PLG ceiling, and when self-serve stops converting

The Pipeline

Your pipeline is not broken. It is disconnected. The value is stranded in the seams between the layers, because nobody owns a handoff.

  • Five load-bearing layers, each of which usually works on its own
  • Seven dimensions, including signal density and funnel containment
  • The reader is never the problem. The structure is what failed.

The Content System

One check cannot catch six classes of failure, because the failures do not resemble each other. So the operation is layers, each with one job.

  • A knowledge base that holds leads, never proof
  • A claims registry with approved, conditional and refutable-only severities
  • A lint layer where taste becomes checkable
  • Reviewers who report, and one integrator who decides

If a number in your reporting has never been checked, that is usually where I start.