One set of numbers everyone in the business trusts.

Four blocks of work. An audit of measurement, the CRM, lead management and the martech stack, with findings ranked by revenue impact. A data model: objects, fields, lifecycle stages and attribution defined once. A build: tracking corrected, lead scoring and routing live, core marketing automation programs rebuilt and tested. Then a handover with documentation, named owners and a maintenance calendar. Typically six weeks, fixed scope, built inside your own systems.

They overlap and the distinction is mostly about scope. Marketing operations owns the marketing side: campaign infrastructure, the automation platform, lead management up to the handoff, and marketing reporting. Revenue operations spans marketing, sales and customer success as one system, with shared definitions, one pipeline model and one forecast. Our work starts in marketing ops because that is where most of the broken data originates, but the deliverables are written so they hold at revenue operations level, which is why we insist the sales lead is in the room from week one.

A fixed fee, quoted after a scoping call, with the deliverables and dates written down before you commit. The number moves with how many systems are in scope and how much history has to be migrated, so publishing a rate would mislead most readers. For budget context, Gartner puts average marketing budgets at 7.8% of company revenue, with martech at a five-year low of 19.4% of that budget. Book a meeting for a scope and a number.

Owning the platform is usually why the work is needed. Gartner measures martech utilization at 49% of purchased capability, with only 15% of organizations qualifying as high performers, and only 49.7% of companies say their marketing teams have the skills and training to use their own marketing systems, down from 54.1% in 2022. The gap is rarely the software. It is that nobody defined the data model, so each new program is built on a different set of assumptions.

The common mid market and enterprise combinations: HubSpot end to end, Salesforce with either Marketo, Pardot, or Salesforce Marketing Cloud Account Engagement, Microsoft Dynamics, and the lighter automation platforms that ecommerce brands run beside their store. On the measurement side, analytics, tag management, server side tracking, the ad platforms and call tracking. Where your stack includes something we do not work in daily, we say so before you sign and either scope the vendor in or leave that piece out rather than learn it on your budget.

Rarely, and never as an opening move. Migrations are expensive, they consume a quarter of your team's attention, and most stacks we audit fail on configuration and ownership rather than capability. The usual recommendation is to consolidate two or three overlapping tools, cancel what nobody logs into, and configure properly what you already pay for. When a platform genuinely cannot support the data model, we say so, scope the migration separately, and take no reseller margin on whatever you choose instead.

A marketing audit looks at the whole marketing function, including channels, creative, budget and team, and ends with findings and a plan. Marketing operations consulting is narrower and deeper: it takes the operations layer and rebuilds it. Many clients run the audit first and arrive here because the audit found that the numbers cannot be trusted. If you already know the measurement is the problem, you can start here.

Only if you ask, and it is quoted separately. This engagement deliberately excludes running paid media, content and lifecycle campaigns, because the operations recommendation has to be free to say that a program should stop, a tool should be cancelled, or your current agency is doing fine. If you do want execution afterwards, the retainers are separate agreements, and plenty of clients take the documentation and run it with their own team.

We can make attribution honest, which is more useful than making it confident. That means one agreed model, consistent definitions, deduplicated conversions, offline and phone revenue included where it exists, and a written statement of what the model cannot answer. Uncertainty is the norm: only 40% of marketing and finance leaders say their measurement tools make it much easier to act decisively. Where the question needs incrementality testing rather than attribution, we say so, and our analytics team can design the test.

The design work is fast; the cleanup depends on volume and how many years of history you want corrected. Inside the six weeks we fix the forward-looking rules, migrate the fields that matter, dedupe and standardise the records that feed reporting, and archive the rest so it stops distorting the numbers. Retroactively rebuilding several years of source data is a separate project and usually poor value, because the decisions you make from this point only need the current data to be right.

Less time than most people expect, but from specific people. We need an administrator for each system for access and change approval, whoever produces the current reporting, the sales lead or head of revenue for the qualification and routing decisions, and someone senior enough to settle a definition when two teams disagree. Expect a kickoff, two working sessions in the design weeks, a test window and the handover. Everything else is ours.

Then the process was designed without them, which is why the sales lead sits in the design sessions rather than receiving the output. In practice adoption comes from three things: routing that gets reps a lead in minutes, a qualification definition they helped write, and reporting that shows their pipeline the way they already think about it. We also keep the number of required fields as small as the reporting genuinely needs, because every extra mandatory field is a bet against adoption.

It is the prerequisite. Gartner found a lack of integrated marketing data is the second most cited barrier to AI-driven efficiency, ranked first by 13% of CMOs and in the top three by 30%, and 62% of organizations already lose revenue to poor CRM data quality. Models trained or prompted on a broken data model repeat the errors faster. If AI strategy is the reason you are here, our AI marketing advisory engagement usually runs after this one, not before.

Everything, and in a form your team can maintain. The audit with its evidence, the data model and field map, the lifecycle definitions, the scoring and routing logic as built, the rebuilt automation programs in your own instance, the reporting, written runbooks, recorded walkthroughs and a maintenance calendar with an owner per process. Nothing sits in a tool of ours and nothing requires us to keep working with you.

It comes first. A dashboard built on fields nobody has defined simply publishes the confusion faster, which is why we fix definitions before visuals. Once the operations layer holds, reporting is straightforward, and it can be delivered by your team or through our data visualization and CRM analytics work. If your real need is a weekly set of numbers leadership will use, marketing scorecard advisory is the cheaper starting point.

Often yes, eventually, and this engagement makes that hire far more likely to succeed: the platform is configured, the model is documented and the role has a scorecard rather than a mess to inherit. Where the question is the shape of the team as a whole, marketing team structure advisory answers it properly, and where you need ongoing leadership over the revenue system, a fractional head of growth is the better fit. We take no fee from any recruiter.