Mid-market is exactly where revenue intelligence has the most impact. Enterprise businesses have teams of analysts and six-figure BI platforms. Early-stage businesses don’t yet have enough data to model. Mid-market businesses — with real marketing spend, real sales teams, and real growth targets — are the organisations where clarity on where revenue comes from makes the most direct commercial difference. If you’re spending five or six figures on marketing and you can’t confidently attribute it to revenue, the intelligence pays for itself.

Yes — and it’s one of the most common reasons teams come to us. When you can show the board a credible model that connects marketing spend to pipeline and revenue, budget conversations change fundamentally. You’re no longer defending activity. You’re presenting evidence. We build the intelligence layer specifically so it can be used in commercial conversations at leadership level — clear, defensible, and free of the vanity metrics that undermine credibility.

A first-pass view of channel performance and attribution — something genuinely useful — is typically achievable within four to six weeks. A full revenue intelligence build with forecasting, cohort analysis, and a live reporting layer takes longer, usually three to four months depending on the complexity of your stack and the state of your data. We give you a clear timeline at the audit stage and flag anything that might extend it.

This is actually the most common starting point. A messy CRM isn’t a blocker — it’s a brief. We’ve built revenue intelligence on top of fragmented, incomplete, partially-duplicated datasets more often than clean ones. We start with a data audit, establish what’s trustworthy and what isn’t, and build the intelligence model on solid foundations rather than papering over the cracks. Where the data genuinely doesn’t exist, we’ll tell you — and tell you how to start capturing it.

It should change what you do — otherwise it’s just a more expensive dashboard. The output of revenue intelligence is a set of commercial decisions: which channels to invest in, which to reduce, which campaigns to scale, where the funnel is losing deals it should be winning. We don’t hand over a report and disappear. We stay in the analysis with you, turn the insight into a prioritised action plan, and check whether the decisions we recommended are generating the outcomes we expected.

Attribution disagreements are usually a symptom of the same underlying problem: different teams are looking at different data with different definitions, and calling it a methodology debate. We start by aligning on what revenue attribution is supposed to answer for the business — not what’s technically achievable — and build the model backwards from that commercial question. The result is a framework the whole leadership team can read from the same dashboard and act on, rather than a modelling exercise that ends in a spreadsheet argument.

You probably have most of the raw data — but having data and having intelligence are different things. GA4 tells you about sessions and conversions on the website. HubSpot tells you about contacts and deals in the CRM. What neither tells you, out of the box, is the full journey from first touchpoint to closed revenue across both systems and everything in between. Revenue intelligence is what happens when you connect those platforms, validate the data, and build a model that tells the whole story. Most teams who go through this process discover that what they thought was working wasn’t — and what they were undervaluing was doing the heavy lifting.

No. We design solutions scaled to your team size and maturity — from a simple reporting dashboard to a full predictive analytics stack — with room to grow.