Marketing analytics
consulting.

A dashboard that nobody trusts is an expensive opinion. Marketing analytics, in this practice, means the few numbers that tell you whether the work created pipeline and revenue, and a habit of reading them together.

This is the measurement practice behind the data science work we do with clients. It sits next to strategic consulting, which sets the plan, and marketing automation, which runs the follow-up those numbers have to describe.

Who this is for

You are a fit when marketing reports clicks, sales reports a different pipeline, and finance does not recognize either number. Common cases are B2B software and services firms with GA4, a CRM, and at least one paid or outbound channel. The data exists. The definitions do not.

If you want a team that also creates the campaigns and then reports on them, that is managed marketing. This engagement can stand alone, or it can be the measurement layer inside that retainer.

What the work covers

Definitions. What counts as a lead, an opportunity, and a customer. Which source you will trust when the ad platform and the CRM disagree. Written in language sales and marketing both accept.

A short scorecard. Pipeline created, win rate, sales cycle, cost to acquire a customer, and one quality measure you choose, such as meetings held or opportunities accepted by sales. We leave the rest off the first page.

The plumbing. What has to match between the site, the CRM, and the channels you pay for. Tracking that fires on the pages that matter. A check that revenue in the CRM and revenue in the report are close enough to manage from.

A monthly review. One meeting. The scorecard, what changed, and one decision: continue, fix, or stop. The review is part of the deliverable. A report that is only emailed does not change the next month's spend.

Where AI fits, if it fits. Models are useful for cleaning notes and drafting the commentary. They are a poor source of the numbers themselves. When the question is whether the foundation can support that, the AI readiness benchmark covers data integrity before any model work. AI marketing consulting is the separate engagement for workflows.

How an engagement runs

1. Understand. The reports each team already opens, the fields in the CRM, and the decisions you wish the numbers could support.
2. Strategize. The definitions, the scorecard, and the gaps in tracking that block those numbers.
3. Execute. The tracking fixes we can make with your team, the scorecard, and the first monthly review on live data.
4. Review. After a cycle, we check whether the definitions held and whether leadership used the scorecard to move budget or stop a program.

You leave with a scorecard the next marketer can inherit, and a written note of what each number means.