Product analytics and software consulting for small B2B SaaS teams

Understand your users. Trust your metrics. Decide what to build next.

I work with small software teams that have a live product and an analytics or reporting problem they haven’t solved. Product analytics, SQL, dbt, dashboards, and the code to connect them. One person. Fixed scope, fixed price.

Projects start at $2.5k with a ten-day diagnostic. The first step is a short fit call.

Start with a fit call See the offers

The problems I take on

Most of them arrive as one of these sentences.

“We can’t explain where users drop off.”
“Our dashboards disagree.”
“We collect events but can’t use them to make decisions.”
“Our reporting takes too much manual work.”
“We have a backlog of product ideas and need evidence to prioritize.”

Each of these is a measurement problem before it is a dashboard problem. The question is usually clear. What’s missing is agreement on what the answer is for.

Three offers

Fixed scope and fixed price. Most engagements start with the diagnostic. If the data can’t support the analysis you want, I’ll say so and deliver a measurement plan instead.

$2.5k–$4.5k

10 business days

Signals Diagnostic

Ten business days to agree on what to measure and why. I interview the people who make product decisions, read what your tools already collect, and write down the metric tree, the event schema, and the gaps between them.

North Star + KPI tree (ratified)
Event schema v1 with owners
90-day build roadmap

How the diagnostic works

$6k–$18k

2–4 weeks

Spine Build Sprint

Two to four weeks to make the measurements true. Events flowing, dbt models with tests, and 3 dashboards execs actually look at. Documented and yours to maintain.

Reliable events with QA
dbt models & tests
Runbook your team can maintain

What a sprint covers

from $1.5k/mo

6–20 hrs/mo

Fractional Steward

A dashboard is a commitment to keeping an answer useful. A fixed block of hours each month keeps definitions from drifting and gives each release a measurement plan.

Monthly KPI review & anomaly flagging
Release readouts & measurement plans
Governance (naming, QA, documentation)

How stewardship works

Work

Three anonymized examples. Two are in-house roles (W-2); one is an advisory build. Each is labeled as what it was.

Real estate marketing SaaSW-2 role

From many dashboards and no shared definitions to one product analytics program.

First event schema, first warehouse and dbt models, activation and retention dashboards, and a monthly KPI review that stuck.

35% YoY

30-day churn, down

Fintech advisoryAdvisory engagement

Five data sources and days of spreadsheet work, replaced by one internal tool.

Unified portfolio data model, automated opportunity detection, and a four-week build the team adopted.

85%

less manual data work per week

EdTechW-2 role

A product analytics program that kept pace as one product line became many.

Unified KPI framework and event conventions across properties, standardized executive dashboards, governance rituals.

Telemetry unified across properties

Read the case studies

What happens next

Step 1
A short fit call
I ask what decision you are trying to make, what you have tried, and what data exists. If it is not a fit, I say so on the call.
Step 2
A paid diagnostic
Ten business days at a fixed price. You get a written plan whether or not we work together afterward.
Step 3
Build, then hand off
Fixed scope by module, documented for your team. Ongoing stewardship is a separate agreement, not an assumption.

Start with a fit call

Not ready to talk? See what a stranger thinks your homepage sells with the Stranger Test, or take the Instrument This App quiz.