Pricing Overview

This page exists for one reason: buyers should not have to click through nine pages just to figure out whether Domain Methods is roughly in range.

Every engagement still gets scoped to the situation. But the pricing model is not mysterious.

We use fixed-fee work, clear deliverables, and defined next steps. No hourly retainer fog, no lock-in. Most teams start with one fixed-fee project to see if we’re the right fit. If you want us to keep the work running afterward — monitoring drift, governing data quality, or reporting monthly lift — Run + Measure is month-to-month, cancel anytime. No “let’s discover the budget first” dance.

The short version

There are four common ways teams start with us, plus one optional support path after the work has proven its worth:

1. a focused diagnostic when the problem is visible but the right fix is not
2. a translation sprint when the business ask is still fuzzy and the team needs a clearer scope before building
3. a GTM Metric-Certification & Governance Sprint when the definitions, ownership, and semantic layer need to become decision-grade
4. Predictive GTM Models with Lift Proof when the team is ready to score, route, forecast, or optimize — and prove the model changed revenue behavior
5. Optional Managed Run + Measure when a proven layer is important enough to keep governed, monitored, evaluated, and re-proven month to month

Diagnose → Build → Run

The ladder is simple on purpose: start with the smallest proof-producing step, then expand only when the evidence justifies it.

The free AI-traffic quick check is useful inside an engagement or QBR. It is not a priced rung in the ladder.

Reusable IP that lowers the risk of starting

The ranges above are not just selling senior time. They are backed by reusable operating assets we can bring into a diagnostic, build, or Run + Measure cadence when they fit the situation.

Pricing at a glance

Diagnostic offers

These are the best fit when the cost of being wrong is already obvious, but the next move is still unclear.

You do not need a giant transformation proposal yet. You need a sharper read on where trust is actually breaking and what should happen next.

Where Did the Money Go?

• Typical range: $5,000-$7,500
• Typical timeline: about 2 weeks
• Best for: teams that cannot defend channel performance or spend efficiency with confidence
• Next-step path: often routes into Revenue Analytics

Three Teams, Three Numbers

• Typical range: $5,000-$7,500
• Typical timeline: about 2-3 weeks
• Best for: RevOps and leadership teams dealing with revenue-definition conflict across functions
• Next-step path: often routes into Revenue Analytics, RevOps Consulting, or Data Foundation

The $500K Question

• Typical range: $5,000-$7,500
• Typical timeline: about 2-3 weeks
• Best for: teams trying to pressure-test a roadmap, workflow, or growth bet before they commit real budget or engineering time
• Next-step path: often routes into Data Activation

Show Me the Margin

• Typical range: $5,000-$7,500
• Typical timeline: about 2 weeks
• Best for: ecommerce teams that can see revenue but still cannot trust profitability by channel, product, or segment
• Next-step path: often routes into Revenue Analytics or deeper warehouse work

Translation sprint

Translate the Ask

• Typical range: $2,500-$5,000
• Typical timeline: about 1-2 weeks
• Best for: heads of data and cross-functional teams stuck between an urgent business ask and an unscoped build request
• What you are buying: a cleaner operating scope, decision frame, and implementation path before the team burns time building the wrong thing

This is the smallest engagement on the site for a reason. Sometimes the highest-value move is not a bigger project. It is getting the request into a shape the team can actually execute.

AI-ready data diagnostic work

AI-Ready Data Diagnostic

• Entry diagnostic: about $5,000-$7,500 when the team needs a fast read on one urgent AI/data question
• Flagship diagnostic: about $12,000-$20,000 over 2-4 weeks when leadership needs the wrong-answer rate, live demo, and build roadmap
• Best for: teams under AI pressure that need an honest read on whether marketing, ad, product, CRM, revenue, and warehouse data can support automation yet
• What you leave with: the percentage of important business questions AI would answer wrong today, the evidence behind that number, and a sequenced fix-first roadmap

This is not an AI strategy deck. It is a trust diagnostic for teams trying to avoid expensive theater. If the flagship diagnostic turns into a committed build, the full flagship fee credits toward that build.

Build packages

These are not priced on dbt hours or rows moved. They are priced on the judgment, governance, decision risk, and proof burden involved in making marketing, ad-platform, product, and revenue data safe for leadership decisions and AI workflows.

GTM Metric-Certification & Governance Sprint

• Typical range: $40,000-$150,000
• Best for: teams that need certified definitions, source precedence, entity resolution, ownership, QA, and evaluation rules for the metrics that dashboards, AI agents, and board reports will reuse
• What you are buying: a governed semantic layer for the small set of GTM/product metrics that actually drive operating decisions — not a warehouse beautification project
• Common next-step path: often routes into Data Foundation when the source and modeling layer need repair before the metrics can be certified

A common example: CAC, pipeline, NRR, expansion, and product-qualified-account definitions all look “close enough” until finance, RevOps, product, and marketing use them in different dashboards. The sprint forces one certified definition, one source path, one owner, and one caveat policy before the number is reused in reporting or AI answers.

Predictive GTM Models with Lift Proof

• Typical range: $25,000-$150,000
• Best for: teams that are ready to build or repair lead, churn, expansion, budget, lifecycle, or next-best-action models and need evidence that the model changed behavior
• What you are buying: model evaluation, activation rules, workflow handoff design, and holdout/lift proof where the decision stakes justify it
• Common next-step path: often routes into Data Activation when the model needs to reach CRM, lifecycle, product, or CS workflows safely

A score is not a strategy. The value appears when the model changes who gets routed, which account gets attention, which budget moves, or which intervention happens — and when the team can tell whether that change actually helped.

Optional Managed Run + Measure

• Typical range: $5,000-$25,000 per month
• Best for: teams that have already proven a diagnostic, semantic layer, model, or measurement system and need it kept reliable after launch
• What you are buying: ongoing governance, drift monitoring, model evaluation, quarterly metric recertification, and a recurring read on what your AI, dashboards, or board packet would get wrong this month
• Availability: limited; month-to-month, cancel anytime, usually added only after a project has proven its worth

Use Managed Run + Measure only when the work has earned ongoing attention. It is not a cold front-door ask, and it is not a mandatory retainer after every project.

Broader service engagements

When the problem is already clear but does not yet fit one of those two build packages, teams usually move into one of the service lines below.

Revenue Analytics

• Starts at: $5,000
• Most common range: about $5,000-$25,000
• Best for: attribution cleanup, revenue-trust alignment, finance-adjacent reporting, and board-defensible growth measurement

Use Revenue Analytics when the attribution question is tied to the broader revenue reporting system. A good signal: campaign influence, pipeline quality, forecast confidence, and finance reporting all need to line up before leadership will trust the growth plan.

SaaS Marketing Attribution

• Starts at: $5,000
• Most common range: about $5,000-$25,000
• Best for: B2B SaaS teams that need attribution logic cleaned up enough to defend spend, pipeline influence, and board-facing growth reporting

Use SaaS Marketing Attribution when the attribution problem is the work, not just one symptom inside a broader revenue analytics rebuild. A good signal: marketing, sales, finance, and leadership all read the same campaign or pipeline report differently.

RevOps Consulting

• Starts at: $5,000
• Most common range: about $7,500-$35,000
• Best for: CRM handoff cleanup, revenue operating rules, pipeline definition alignment, and the messy middle between RevOps, finance, sales, and marketing

Use RevOps Consulting when the problem is broader than a one-time metric-definition diagnostic. A good signal: the team is not just asking which number is right; they need the operating model, ownership, CRM handoffs, and cleanup path to stop the same disagreement from coming back next month.

Fractional Analytics Consultant

• Starts at: $5,000
• Most common range: about $5,000-$25,000
• Best for: teams that need senior analytics judgment, sequencing, and hands-on execution before the full-time role or larger service path is obvious

Use Fractional Analytics Consultant when the team needs more than a task-level freelancer but is not ready to hire a permanent analytics leader. A good signal: the backlog mixes dashboards, stakeholder translation, metric trust, and first-hire questions, and someone senior needs to decide which work should not be started yet.

Data Foundation

• Starts at: $5,000 for focused assessment or cleanup work
• Build-package path: GTM Metric-Certification & Governance Sprint, typically $40,000-$150,000 when the semantic layer, source precedence, metric ownership, and evaluation rules need to become decision-grade
• Best for: governed semantic-layer work, warehouse cleanup, dbt implementation, testing, source precedence, pipeline reliability, and upstream trust work

Data Activation

• Starts at: $5,000 for focused activation cleanup or workflow repair
• Build-package path: Predictive GTM Models with Lift Proof, typically $25,000-$150,000 when the model, workflow, evaluation, and holdout/lift burden are material
• Best for: reverse ETL, scoring workflows, predictive GTM model activation, warehouse-to-tool handoffs, and operational analytics that need to prove value quickly

How pricing usually works in practice

A few value drivers matter more than the exact number:

1. A clear decision lowers the cost of proof

If the team already knows which decision is blocked, which systems are involved, and who owns the call, we can usually scope faster and more tightly.

That reduces ambiguity because the work is not trying to solve every data problem at once. It is trying to make one expensive decision safer.

2. Certified metrics cost more when they have to survive real use

A CAC, NRR, pipeline, win-rate, or product-qualified-account definition is cheaper when it only appears in a dashboard. It costs more when finance, RevOps, product, marketing, a board packet, and an AI agent all need to reuse the same answer without silently changing the rules.

That is the value of the governance work: one definition, one source path, one owner, and one caveat policy before the number starts driving decisions.

3. Lift proof changes the price band

A model or activation workflow is cheaper when the deliverable is a score. It moves into the Predictive GTM Models band when the buyer needs workflow delivery plus proof that the change mattered.

For example, the B2B SaaS lead-scoring case increased qualified pipeline 40% while holding back a control group, so the readout could separate model-assisted routing from normal baseline movement. That proof burden is what turns a model build into a revenue decision system.

4. Foundation work expands when the number must become reusable

A common pattern is somebody thinking they need “one dashboard fix” when the actual work is source precedence, warehouse logic, testing, ownership, and definitions across several systems.

That is why a smaller diagnostic or translation sprint can be a better first purchase than jumping straight into a broader build.

Which route should you choose?

Common pricing questions

Why do you show ranges instead of exact package pricing for everything?

Because the operating mess matters.

A clean attribution cleanup for one team is different from a cross-functional revenue-trust repair involving CRM stages, finance logic, and warehouse modeling. The range gives you a realistic order of magnitude without pretending every situation is the same.

Do you bill hourly?

For scoping and implementation, Domain Methods uses fixed-fee pricing with clear deliverables — not open-ended hourly billing.

If you want us to keep a measurement or activation system governed after the project, that is a separate optional Run + Measure path: month-to-month, cancel anytime, and only recommended when ongoing monitoring is actually useful.

If a situation is too fuzzy to scope responsibly, that is usually a sign to start with a diagnostic or translation sprint first.

Can we start small?

Yes. In many cases, that is the smartest move.

A focused diagnostic or scoped sprint is often the right first purchase when the team needs clarity before it needs a broader implementation plan.

What happens after a diagnostic?

Usually one of three things:

1. you have enough clarity to fix the issue internally
2. the next step becomes a clearly scoped service engagement
3. the right answer is to stop, wait, or clean up something smaller first

All three are acceptable outcomes. The goal is better decisions, not forcing a bigger project.

What if we need a working tool before we know the scope?

Use the Operator Tools library when the next meeting needs a worksheet, scorecard, benchmark, or checklist before anyone is ready to buy a diagnostic. A good tool will not replace scoping, but it can expose whether the real question is spend confidence, revenue definitions, source-of-truth repair, AI readiness, or workflow activation. If the open question is how the work gets scoped, aligned, delivered, and handed off, use the Engagement Framework before a scoping call.

If the immediate pricing question is a discount, contract concession, or renewal exception, start with the Discount Approval Confidence Check before asking for a larger scope. It gives RevOps, sales, and finance a practical way to test whether the exception is safe enough for forecast, margin, or board-facing revenue decisions.

Want the fit answer before the pricing answer?

If you are still not sure whether Domain Methods is the right kind of shop for your team, read Who We Serve.

If the fit looks right and you want to talk through the likely entry point, book a discovery call.