Turn Messy Marketing Data into Decisions that Drive Revenue

Your data should be driving decisions — but conflicting metrics, broken pipelines, and tools nobody trusts are getting in the way. We fix that. Then we turn the cleanup into analytics, workflows, and AI your team can actually trust.

Marketing analytics and attribution you can defend
Trusted data foundations your team actually uses
AI-powered activation built on clean data

For mid-size SaaS and ecommerce teams ($10M+ ARR or venture-funded).

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Domain Methods — Turn Messy Marketing Data into Decisions that Drive Revenue

What We Do

Stop Guessing Which Channels Drive Revenue

For the VP of Growth who cannot prove ROAS to the board — and the RevOps team tired of defending numbers that still do not match.

Marketing analytics and attribution
Revenue operations metrics
Cross-channel performance analytics
Ad spend optimization

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Your Dashboards Are Lying. Fix the Foundation.

For data leaders who need more than technical capacity. We make the data trustworthy enough for dashboards, decisions, and AI workflows — without losing the business context that makes the models useful.

Data strategy and architecture
Pipeline development
dbt implementation
Data governance and AI readiness

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Data Sitting in Your Warehouse Isn’t Working

For product and growth teams tired of rich data gathering dust. We ship activation workflows — reverse ETL, scoring, automated audiences — that prove value in weeks, not quarters, and start with one MVP that changes a real decision.

Reverse ETL implementation
AI-powered predictions and scoring
Warehouse-as-CDP solutions
Automated activation workflows

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Exploring AI but not sure your data is ready?

Start with an AI readiness audit

Start with the problem, not the service line

If you know where trust is breaking but do not yet know which engagement fits, start with the diagnostic built for that situation.

Tony — Growth

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

Betty — RevOps

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

Hank — Product / Growth

The $500K Question

You are about to bet a quarter of roadmap and growth effort on something that might not move revenue.

See the growth-leverage diagnostic

Jimmy — Head of Data

Translate the Ask

The business asked for better analytics, but nobody has translated that into a build plan your team can trust.

See the translation sprint

Ecommerce

Show Me the Margin

Revenue is up, but you still cannot see the real profit by channel, product, and customer segment.

See the profitability diagnostic

Built on tools you already know

Most teams I talk to have the same problem: they don't know who to trust and what to do next with the budget they have. I help mid-size SaaS and ecommerce companies cut through that uncertainty — build analytics they can trust and data systems they can actually use. If your team is drowning in dashboards nobody believes, let's talk. I share practical takes on marketing data, attribution, and analytics engineering on LinkedIn — follow along.

Jason B. Hart Founder & Principal Consultant

Trusted by data-driven teams

From stronger data hygiene to AI workflows teams can actually trust.

60% → 95% attribution coverage

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

Series B Martech Platform

99%+ pipeline uptime

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

B2B Fintech, 200 employees

5 dashboards → 1 source of truth

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

18% churn reduction in 3 weeks

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

Product-Led Growth SaaS

2-week AI readiness roadmap

SaaS Data Team + AI Readiness Audit = 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

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.

Tony / 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

Hank / 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

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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.