Marketing and product data your AI can trust.

We’re the specialists in your messiest, most revenue-critical data — ad platforms, GA4, product events, and CRM. We govern it into one source of truth your board, your dashboards, and your AI can answer from, then prove which spend and models actually drive revenue.

  • Marketing, advertising, and product metrics governed from the source
  • Attribution and holdout measurement leaders can defend
  • AI-ready activation built only where the data can support it

Marketing & product analytics data — AI-ready and revenue-proven. For B2B SaaS at $25M–$100M ARR.

Book the AI-Ready Data Diagnostic See your starting point
Domain Methods — Marketing and product data your AI can trust.

The data foundation below your AI. The measurement above it.

Prove Which Channels — and Which Models — Actually Drive Revenue

Attribution stays primary because spend is still where trust breaks first. We connect ad platforms, product events, CRM, and billing data so growth leaders can defend which dollars, journeys, and model-assisted plays are creating revenue.

  • SaaS marketing attribution
  • Revenue analytics and CAC clarity
  • Holdout and lift-proof measurement
  • Board-ready spend confidence
See the attribution path

Your AI Will Answer Board Questions Wrong Until the Metrics Are Governed

AI does not fix metric disagreement. It speeds it up. We certify the marketing, revenue, and product definitions that dashboards, copilots, and agents need to answer from before the wrong CAC, NRR, pipeline, or activation number becomes policy.

  • Governed semantic layer for GTM metrics
  • Metric-certification and ownership rules
  • CRM, product, and warehouse source precedence
  • AI-ready data foundation
Build the governed layer

Predictive GTM Models, Proven With Holdout Lift

A lead score, churn model, or expansion signal only matters if the business can act on it and measure the lift. We build predictive GTM models with workflow ownership, caveats, and proof plans attached instead of leaving another dashboard nobody trusts.

  • Predictive GTM model design
  • Lead, churn, expansion, and account scoring
  • Workflow activation and exception handling
  • Lift measurement with honest caveats
See predictive GTM models

Then We Keep It True With Optional Run + Measure

Some teams need a handoff. Others need a month-to-month operating partner to keep definitions, model drift, measurement checks, and revenue-facing workflows from decaying after launch. That run layer is optional, not a lock-in.

  • Monthly metric and model health checks
  • Experiment and lift-readout cadence
  • RevOps and analytics operating support
  • Cancel-anytime managed run option
See Run + Measure

See the percentage of your marketing, revenue, and product metrics an AI would answer wrong today.

Book the AI-Ready Data Diagnostic

Start with the rung, not the service catalog

If you know the business question but not the right engagement shape, use the ladder: diagnose the trust gap, build the governed layer, prove the model or spend decision, then decide whether you need run support.

Diagnose

AI-Ready Data Diagnostic

You need to know which marketing, revenue, and product metrics an AI would answer wrong before you fund another automation or agent project.

Start with the diagnostic

Diagnose

Where Did the Money Go?

You are spending aggressively on paid channels and still cannot defend which half of the budget is working.

See the spend diagnostic

Diagnose

Three Teams, Three Numbers

Marketing, sales, and finance are all reporting different versions of revenue and nobody trusts the board deck.

See the metric-alignment diagnostic

Proven Core

SaaS Marketing Attribution

Paid spend, CRM stages, finance reporting, and board growth targets all need one attribution story leadership can defend.

See the attribution path

Build

AI-Ready Data Foundation & Governed Semantic Layer

Your dashboards, copilots, and AI agents need certified marketing, revenue, and product definitions before they can be trusted.

Build the governed layer

Build

Predictive GTM Models with Lift Proof

You need lead, churn, expansion, or account models that can change workflow behavior and prove whether the change actually worked.

See predictive GTM models

Managed Run

Managed Run + Measure

The work is live, but someone still needs to keep metrics, model drift, lift reads, and workflow adoption from quietly decaying.

See the run option

Operator tools

Need something useful before the first call?

Start with a worksheet your team can use in the next revenue, data, or AI-readiness conversation. The tools are grouped by the decision leaders are trying to make, not by generic content type.

AI readiness and CRM hygiene

Worksheets for checking whether CRM data, workflow ownership, and exception handling are trustworthy enough for automation.

Use the AI readiness tools

Spend and attribution confidence

Benchmarks and checklists for deciding whether channel spend, CAC, and attribution reporting are safe enough to act on.

Use the spend tools

Revenue definitions and metric governance

Scorecards and rollout trackers for making revenue, pipeline, retention, or board metrics mean the same thing across functions.

Use the metric tools
Browse all operator tools

Built on tools you already know

dbt
BigQuery
Databricks
Fivetran
dlt
Airbyte
AWS
GCP
Jason B. Hart

Most senior leaders I talk to have the same problem: they do not know who to trust and what to do next with the budget they already have. I help mid-size SaaS companies cut through that uncertainty, sort out what is actually broken, and turn messy marketing and revenue data into decisions leadership can use.

Read Jason's perspective

Jason B. Hart Founder & Principal Consultant

Trusted by data-driven teams

Representative client outcomes from teams that needed clearer numbers, faster decisions, and less dashboard theater.

We name these by role because many clients do not want homepage attribution. The tradeoff is transparency over polish: each card includes operating context, engagement scope, and a proof path to the closest published case study.

60% → 95% attribution coverage

Anonymized client outcome

B2B SaaS + Attribution Rebuild = One Number Marketing and Finance Both Trust

We were spending six figures a month on ads with no way to tell which channels were actually driving pipeline. Domain Methods rebuilt our attribution model from the ground up — unified data from ad platforms, CRM, and billing into one trusted pipeline. We went from defending numbers in every board meeting to making budget allocation decisions in hours.
VP of Growth 300-person B2B SaaS company with a seven-figure paid media budget Attribution rebuild across ad platforms, CRM, and billing
VOG

VP of Growth

300-person B2B SaaS company with a seven-figure paid media budget

Read the attribution case study

99%+ pipeline uptime

Anonymized client outcome

Mid-Market SaaS + dbt Foundation = Pipeline Reliability Nobody Has to Think About

Most consultants could write SQL but couldn’t explain why it mattered to the business. Domain Methods built a dbt foundation with real governance — tested models, clear documentation, and automated quality checks. Our team went from constant firefighting to barely thinking about pipeline reliability.
Head of Data 200-person mid-market SaaS team with a brittle dbt stack dbt foundation, testing, and warehouse governance reset
HOD

Head of Data

200-person mid-market SaaS team with a brittle dbt stack

Read the pipeline reliability case study

5 dashboards → 1 source of truth

Anonymized client outcome

B2B SaaS + Flexible Data Model = CRO and CFO Looking at the Same Metrics

We had five dashboards showing five different revenue numbers. Domain Methods didn’t just pick one — they built a flexible data model that adapts as our business changes. For the first time, our CRO and CFO look at the same metrics. That alignment alone was worth the engagement.
VP of Revenue Operations Workforce management platform with sales, finance, and RevOps all reporting different revenue numbers Revenue model redesign and cross-functional metric alignment
VOR

VP of Revenue Operations

Workforce management platform with sales, finance, and RevOps all reporting different revenue numbers

Read the revenue-alignment case study

18% churn reduction in 3 weeks

Anonymized client outcome

PLG SaaS + Reverse ETL MVP = Churn Reduction in Three Weeks

I didn’t want a six-month roadmap — I wanted to prove that our warehouse data could reduce churn this quarter. Domain Methods shipped a reverse ETL workflow in three weeks that synced churn-risk scores to our CRM and triggered automated outreach. It moved the needle immediately. That MVP approach is exactly what PLG teams need.
Head of Product PLG SaaS business with 15,000 active accounts and churn pressure on expansion revenue Reverse ETL MVP for churn-risk scoring and CRM activation
HOP

Head of Product

PLG SaaS business with 15,000 active accounts and churn pressure on expansion revenue

Read the activation case study

2-week AI readiness roadmap

Anonymized client outcome

SaaS Data Team + AI-Ready Data Diagnostic = Clarity Before Buying Another AI Tool

Leadership wanted AI use cases fast, but our definitions, source quality, and documentation were not ready. Domain Methods audited the stack, showed us exactly what to fix first, and gave us a practical roadmap. Instead of forcing AI onto messy data, we cleaned up the foundation and moved with confidence.
VP of Data Healthcare analytics company under pressure to show AI value without breaking reporting trust AI-Ready Data Diagnostic and two-week foundation roadmap
VOD

VP of Data

Healthcare analytics company under pressure to show AI value without breaking reporting trust

Read the AI readiness guide

Proof for the situations we talk about

A few representative examples of what happens when messy marketing and revenue data gets connected to decisions leaders can actually act on.

Growth / Attribution

From conflicting dashboards to one trusted attribution pipeline

A 300-person SaaS growth team stopped arguing with finance and started making budget decisions in hours.

We unified ad platforms, CRM, and billing data into one attribution pipeline the growth team and finance team could both trust.

Read case study

Product-Led Growth / Activation

A churn-reduction workflow shipped in 3 weeks

Warehouse data moved from passive reporting to a live retention workflow.

A PLG SaaS team used reverse ETL and churn-risk scoring to get high-signal accounts into the CRM fast enough to act.

Read case study

Ecommerce / Profitability

True channel-level ROAS cut wasted ad spend 35%

A DTC brand stopped trusting platform-reported vanity numbers and started reallocating budget based on real outcomes.

We connected ad spend, Shopify revenue, and downstream outcomes so the team could see which channels were actually profitable.

Read case study
See all case studies

Common questions before reaching out

What does Domain Methods actually do now?

Domain Methods helps B2B SaaS leaders make marketing, advertising, revenue, and product analytics data trustworthy enough for board decisions, dashboards, AI agents, and GTM workflows. The work usually starts with a diagnostic, then moves into governed definitions, attribution or lift measurement, predictive model support, and optional run cadence when the team needs ongoing help.

What does 'AI-ready marketing and product data' mean?

It means the source systems, entity definitions, metric logic, workflow owners, and measurement plan are clear enough that an AI tool cannot confidently answer from the wrong CAC, NRR, pipeline, activation, churn, or spend number. The goal is not AI theater. It is governed business data that a leader can act on.

Who is the best fit for Domain Methods?

The best fit is usually a B2B SaaS company around $25M–$100M ARR with real pressure around AI, attribution, board reporting, GTM efficiency, or product-led growth. Smaller venture-backed teams can still fit when urgency is high and leadership needs one senior operator to diagnose, build, and prove the next move.

Should we start with a diagnostic, a build, or managed run support?

Start with a diagnostic when the team does not yet trust the numbers or cannot name the real failure point. Move to a build when the target definitions, source precedence, model use case, or workflow is clear enough to implement. Use managed Run + Measure only after there is something worth keeping true; it is an optional month-to-month operating layer, not the front-door ask.

Do we need to grant system access before the first call?

No. The first conversation can usually start with screenshots, exported reports, schema notes, metric definitions, redacted sample rows, or a walkthrough of where the numbers stop matching. Deeper diagnostic or implementation access should stay client-controlled and scoped to the work: client-owned accounts, least-privilege roles, and any NDA, procurement, or security review before sensitive data or production systems are shared. The Terms of Service and Privacy Policy provide supporting legal context.

Practical data insights, monthly

One email per month with actionable takes on attribution, data foundations, and AI-powered activation — drawn from real client work. No fluff, no spam.

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