Jason B. Hart

Founder & Principal Consultant

Founder & Principal Consultant at Domain Methods. Helps mid-size SaaS and ecommerce teams turn messy marketing and revenue data into decisions leaders trust.

Core expertise

Marketing attribution
Revenue analytics
Analytics engineering
dbt project architecture
GTM and RevOps data alignment

Platforms worked in

dbt, BigQuery, Snowflake, Databricks, GA4, HubSpot, Salesforce

Typical engagements

Mid-size SaaS, Product-led growth SaaS, Ecommerce and DTC brands

Jason B. Hart is the founder of Domain Methods, where he helps mid-size SaaS and ecommerce teams build analytics they can trust and operating systems they can actually use.

He has spent the better part of a decade helping companies untangle attribution, revenue reporting, data architecture, and analytics workflows. Before founding Domain Methods, Jason was Director of Data & Analytics at Springboard and a startup co-founder. Across dozens of engagements, he has worked with B2B SaaS, product-led growth, and ecommerce teams that needed their marketing, finance, product, and data numbers to tell the same story.

His work typically sits at the messy boundary between business questions and technical implementation: defining metrics, rebuilding attribution, shaping warehouse and dbt models, and turning scattered source data into systems leadership can actually use to make decisions. He regularly works across modern cloud tooling including dbt, BigQuery, Snowflake, Databricks, GA4, HubSpot, and Salesforce.

Jason publishes practical guides and opinionated essays on marketing measurement, analytics engineering, and AI readiness for mid-size operators. His perspective is straightforward: most companies do not have a dashboard problem or an AI problem first — they have a data trust problem.

He works with operators who need a translator as much as a builder: someone who can sit with a VP of Marketing, Head of Data, RevOps lead, or finance stakeholder, define what the business is actually asking, and then turn that into models, metrics, and workflows a team can own after the engagement ends.

Best Marketing Attribution Approaches for Mid-Size SaaS

What is the best marketing attribution approach for mid-size SaaS? The best marketing attribution approach for a mid-size SaaS company depends less on the model and more on whether the team can connect marketing touches to trusted pipeline and revenue outcomes. In practice, most teams are choosing among three paths: a DIY reporting stack, an attribution platform, or a consulting-led rebuild that fixes the trust layer underneath the dashboards. That distinction matters because the pain usually shows up as an executive proof problem before it shows up as a tooling problem. HubSpot’s 2026 State of Marketing reporting found that 33% of marketing leaders say measuring ROI is their top challenge.1 And in B2B SaaS, the buying path itself is rarely simple: Forrester says an average of 13 people are involved in a purchasing decision.2 If your customer journey is that messy, the real question is not “Which model sounds smartest?” It is “Which approach gives leadership a number they will actually use?”

Confessions of an Analytics Consultant: 5 Things I Wish Every Client Knew Before Hiring Me

There are a few things I wish more companies understood before they hired an analytics consultant. Not because it would make the work easier for me. Because it would make the work more honest, faster, and more useful for them. A lot of consulting sales language is built to reduce friction. Everything sounds clean. Fast. Proven. Low-risk. The consultant is positioned like a reassuring appliance: plug them in, get clarity out.

Data Truth vs. Data Comfort: Why Most Companies Choose the Wrong One

Most companies say they want data truth. What they usually want is data comfort. They want numbers that are clean enough to present, familiar enough not to start a fight, and specific enough to sound authoritative in a meeting. That is understandable. It is also how organizations end up making expensive decisions with fragile reporting. The deeper problem is not that people are anti-data. It is that they often prefer a comfortable version of the number over a trustworthy one.

How to Build a Marketing Dashboard That People Actually Use

What Is a Marketing Dashboard That People Actually Use? A marketing dashboard that people actually use is not a wall of charts. It is a decision tool built for one audience, one operating question, and one set of trusted definitions. That sounds obvious. It rarely gets built that way. Most dashboard projects start too far downstream. The team jumps into charts, filters, and layout before they answer the harder questions:

How to Calculate True Customer Acquisition Cost (Not the Vanity Version)

What is true customer acquisition cost? True customer acquisition cost (CAC) is the full cost of winning a new customer once you include the costs, attribution caveats, and business context that platform dashboards usually leave out. Vanity CAC is the cleaner-looking version. It usually takes one narrow slice of spend, divides it by a flattering conversion count, and calls it a decision. That is how a team ends up saying:

How to Evaluate Whether Your Company Actually Needs dbt

A lot of companies ask the dbt question too late or for the wrong reason. Sometimes the team has already outgrown spreadsheet logic, hidden dashboard calculations, and one heroic analyst holding the reporting layer together with duct tape. Other times, someone heard that every serious data team uses dbt and assumes buying into the pattern will automatically fix trust, governance, and reporting chaos. Both paths can be expensive. What dbt Actually Is, in Business Terms dbt is a way to turn data transformation logic into visible, version-controlled, testable business logic instead of leaving it scattered across dashboards, one-off SQL, and analyst memory.

How to Present Marketing Data to Your Board (Including What You Don't Know)

What Is Board-Ready Marketing Data? Board-ready marketing data is marketing performance reporting that uses agreed definitions, finance-compatible time windows, and explicit confidence levels so leaders can make strategic decisions without mistaking polished numbers for proven truth. Most board decks do not fail because they have too little data. They fail because they show a lot of data without making it clear which numbers are solid, which are directional, and which are still politically negotiated behind the scenes.

How to Run a Metric Alignment Workshop (Without Starting a Political War)

A metric alignment workshop is a short, structured meeting designed to stop teams from using the same label for different business realities. If marketing says pipeline, sales says pipeline, and finance says pipeline — but each one is defending a different number, logic path, and use case — you do not have a reporting problem first. You have a coordination problem with financial consequences. That is more common than most leadership teams want to admit. Salesforce’s State of Data and Analytics (2nd Edition) reports that leaders estimate 26% of their organization’s data is untrustworthy, which is exactly why metric disagreements become expensive once a dashboard starts looking more official than the underlying logic actually is.1

How to Set Up Marketing Attribution Without a Data Engineer (And When to Stop Trying)

What is the simplest useful way to set up marketing attribution without a data engineer? The simplest useful attribution setup without a data engineer is an 80/20 system built on consistent UTMs, a few clean CRM fields, self-reported attribution, and one small scorecard that answers a real budget question. That answer is less glamorous than most software demos, but it is a lot more useful. If you are a marketing leader at a growing SaaS company, the problem usually does not start as, “We need multi-touch attribution.”

How to Stop Your Marketing Team from Building Shadow Spreadsheets

What Is a Shadow Spreadsheet? A shadow spreadsheet is a privately maintained report that someone builds because the official dashboard, CRM view, or finance output does not answer the question they need to act on. It is usually not rebellion. It is a workaround for a trust, freshness, definition, or workflow gap somewhere upstream. Marketing teams do not usually build shadow spreadsheets because they love spreadsheets. They build them because the official number keeps failing them at the exact moment they need to make a decision.

How to Tell Whether You Have a Tools Problem or a Foundation Problem

What Is a Tools Problem vs. a Foundation Problem? A tools problem means your team already agrees on the decision, metric definitions, workflow, and source-of-truth rules, but the software you have is still the limiting factor. A foundation problem means the mess is happening underneath or between the tools: definitions drift, source systems disagree, ownership is fuzzy, the warehouse logic is brittle, or the business has not actually named what the output should change.

Reverse ETL Tools Compared: Census vs. Hightouch vs. Custom Build

What is the best reverse ETL option for a warehouse-first team? The best reverse ETL option depends on how quickly your team needs to operationalize trusted warehouse data and how much ongoing engineering ownership it can realistically support. For most mid-size SaaS teams, the real choice is between buying speed with a tool like Census or Hightouch and accepting the maintenance cost of a custom build. That decision matters because the activation gap is still huge. Salesforce’s State of Data and Analytics reporting found that 63% of technical leaders say their companies struggle to turn data into business priorities and that leaders estimate 19% of company data is siloed, inaccessible, or unusable.1 A warehouse can be technically sound and still commercially inert if the data never makes it into the systems where teams actually work.

The Real Cost of ‘We’ll Figure Out the Data Later’

A lot of teams say some version of this with a straight face: We know the data is messy. We just do not have time to fix it right now. That sounds pragmatic. Usually it is expensive. The cost of bad data work is easy to imagine when a migration goes sideways or a dashboard breaks in front of the board. The cost of delaying the work is harder to see, because it leaks out through slower decisions, repeated debates, and a growing pile of workarounds that everybody quietly starts treating as normal.

Your Shopify Dashboard Says Growth. Your Margin Says Otherwise.

A lot of ecommerce reporting makes one number too easy to celebrate: revenue. Revenue matters, obviously. But revenue alone is a terrible reason to feel confident if you cannot see what it cost to create it. That is why a surprising number of ecommerce teams are living inside a weird split-screen reality. The Shopify dashboard says growth. The ad platforms say performance. The bank account says, “something still feels off.”

How to Audit Your Marketing Data in Two Days (And What You'll Probably Find)

What Is a Two-Day Marketing Data Audit? A two-day marketing data audit is a focused review of the numbers your team actually uses to make budget, pipeline, and performance decisions, with the goal of finding where those numbers stop being trustworthy. It is not a full analytics transformation. It is not a warehouse rebuild. It is not a month of stakeholder interviews dressed up as rigor. It is a short, decision-first diagnostic.

How to Translate Business Questions Into Data Requirements

One of the most expensive mistakes a data team can make is taking a request literally. A stakeholder says, “We need a dashboard.” The team hears, “Build a dashboard.” Three months later, the dashboard ships. Everyone is underwhelmed. The stakeholder says it is not quite what they meant. The data team feels underappreciated. Trust goes down on both sides. The problem is not usually effort. The problem is translation. The Request Is Usually a Proxy for a Decision Most business stakeholders are not thinking in tables, models, joins, or dimensional grain.

The Anti-Roadmap: 10 Analytics Projects Your Mid-Size SaaS Company Should Not Start This Quarter

Every quarter, smart mid-size SaaS teams approve at least one analytics project that sounds sophisticated, forward-looking, and completely reasonable. And every quarter, some of those projects quietly eat time, budget, and political capital without making decisions better. That is the dangerous part. Bad analytics bets rarely look stupid at kickoff. They look strategic. They come with slides. They usually have a sponsor. Sometimes they even have a vendor demo behind them.

The Dangerous Comfort of False Precision: Why Your Dashboard Decimal Points Are Lying

What Is False Precision in Reporting? False precision in reporting is when a metric looks exact enough to inspire confidence even though the underlying definitions, source systems, or attribution logic are still unstable. It makes weak data feel decision-ready, which is why a polished dashboard can create more risk than an obviously messy one. The most dangerous number in your company is not always the wrong one. It is the one that is wrong but looks precise enough to shut down the conversation.

AI Won’t Fix Your Data (But Here’s What It Can Actually Do for Marketing Analytics)

There is a sentence I keep coming back to when companies ask about AI for marketing analytics: AI is a multiplier. And a multiplier applied to zero is still zero. That sounds harsh, but it is useful. Because most teams do not have an AI problem yet. They have a trust problem. Their attribution logic is shaky. Their CRM has duplicate lifecycle data. Their warehouse models are only lightly tested. Marketing, finance, and sales all use slightly different definitions. Then leadership says, “We need to use AI,” as if a new interface can make those problems disappear.

The 'What Does Revenue Even Mean Here?' Workshop Guide

If your CEO, CFO, CRO, and head of marketing all use the word revenue but mean different things, you do not have a communication problem. You have an operating problem. This is one of the most common ways mid-size SaaS companies lose trust in their own reporting. Finance shows net new ARR. Sales talks about bookings. Marketing reports sourced pipeline. The board deck compresses all of it into one chart with a label like “revenue” and everyone leaves the meeting less confident than when it started.

AI Readiness Through Data Hygiene: A Practical Guide

What Is AI Readiness Through Data Hygiene? AI readiness through data hygiene means ensuring your source data, business definitions, pipeline reliability, documentation, and governance are strong enough that AI models — scoring, automation, copilots — amplify good decisions instead of scaling bad ones. Everyone wants AI right now. Very few teams are ready for it. The gap usually is not model access. It is data hygiene. If your CRM has duplicates, your warehouse models are lightly tested, your metric definitions change depending on who is in the room, and your dashboards already disagree, AI will not solve the problem. It will scale it.

Marketing Attribution for SaaS: The Complete Guide

What Is Marketing Attribution for SaaS? Marketing attribution for SaaS is the process of identifying which marketing touchpoints — ads, content, events, outbound — actually contribute to pipeline and closed revenue. For mid-size SaaS companies with long sales cycles and multiple tools, the core challenge isn’t choosing an attribution model; it’s getting marketing, sales, and finance to trust the same number. I’ve spent the better part of a decade helping SaaS companies figure out which marketing dollars are actually generating revenue. And the single most consistent thing I’ve learned is this: most attribution problems aren’t attribution problems at all.

Why Your Attribution Model Is Lying to You

Most SaaS companies don’t have an attribution problem. They have a trust problem. Marketing says one thing, finance says another, and the CEO doesn’t believe either number. Sound familiar? The issue isn’t which attribution model you pick. It’s that your data pipeline, your tracking, and your reporting were never designed to answer the question you’re actually asking: “Is our marketing spend generating revenue?” Stop Obsessing Over Models First-touch, last-touch, multi-touch, data-driven — the model matters far less than whether your underlying data is clean, consistent, and connected to actual revenue.

Building a Modern Data Foundation with dbt: A Practical Guide

What Is a Modern Data Foundation? A modern data foundation is the combination of a cloud warehouse (Snowflake, BigQuery, Redshift), a transformation layer (typically dbt), and governance practices (testing, documentation, ownership) that makes analytics, automation, and AI trustworthy. It’s the infrastructure layer that determines whether your data team delivers insights or fights fires. I’ve helped dozens of companies build data foundations. The ones that fail almost never fail because they picked the wrong tool. They fail because nobody agreed on what the foundation was supposed to do.

5 dbt Implementation Mistakes That Kill Data Trust

dbt changed the game for analytics engineering. But like any powerful tool, it can create as many problems as it solves — especially when the implementation is rushed or the team doesn’t have a clear plan. Here are the five mistakes I see most often when companies implement dbt, and what to do instead. 1. No Testing Strategy This is the most common and most damaging mistake. Teams build dozens of models but write zero tests. Then they wonder why stakeholders don’t trust the numbers.

Data Activation Playbook: From Warehouse to Revenue

What Is Data Activation? Data activation is the practice of operationalizing warehouse data — sending modeled, governed insights into the tools where teams actually work. This includes reverse ETL syncs, predictive scoring workflows, automated audience segmentation, and warehouse-as-CDP architectures that replace expensive standalone tools. Your data warehouse already knows which customers are about to churn. It knows which trial users are most likely to convert. It knows which accounts are primed for expansion.

Your Data Warehouse Is a Goldmine You're Not Using

What Is Reverse ETL? Reverse ETL (also called data activation) is the process of pushing transformed, trusted data from your warehouse back into operational tools like CRMs, email platforms, and ad networks. It turns your warehouse from a reporting backend into an engine that drives real-time decisions across sales, marketing, and product teams. Here’s a pattern I see constantly: a company spends six months and a significant budget building a data warehouse. They hire analysts. They implement dbt. They build dashboards.