The situation
A £50M B2B professional services firm (management consulting, 300 employees, serving FTSE 250 clients) had a marketing team running content, events, LinkedIn, and paid search at a combined budget of £600k/year. The board-level frustration: marketing couldn't demonstrate its contribution to revenue. The CRM showed deals, the MAP showed email metrics, but there was no system connecting the two. The CRO's position: 'show me the revenue or I cut the budget.'
The challenge
The challenge was infrastructure, not performance. Marketing was probably contributing significantly to revenue - event attendees and content readers were appearing in deal histories - but the data systems weren't capturing these contributions in any reportable way. UTM tracking was inconsistent (only 40% of campaigns were tagged), CRM lead source fields were empty on 60% of records, and there was no MAP-to-CRM integration that would allow campaign history to appear on deal records. The result was a £600k marketing budget that appeared to produce almost no attributable revenue.
Our approach: infrastructure before campaigns
The operating principle for this programme was that you cannot optimise what you cannot measure. The first 60 days were spent entirely on RevOps infrastructure — attribution, data alignment, shared definitions — before any campaign investment was changed.
Complete UTM parameter implementation across all marketing channels. First-touch attribution cookie configured in HubSpot. Self-reported attribution ("How did you hear about us?") added to all forms and the discovery call script. Joint marketing-sales dashboard built with agreed definitions for MQL, SQL, and opportunity. MQL definition rebuilt from scratch with sales input — raising the threshold to require both ICP fit and one high-intent action. Result: MQL-to-SQL conversion rate rose from 11% to 27% within 30 days of the new definition, without any campaign changes.
With measurement in place, campaign investment was restructured based on evidence rather than assumption. Budget was shifted from broad Google Display (lowest measured CSQL) toward LinkedIn ABM targeting the company list (highest measured CSQL) and retargeting engaged website visitors (lowest CPL overall). A content programme was initiated targeting 12 high-commercial-intent keyword clusters identified in a gap analysis — none of which the client was currently ranking for.
A Tier 1 ABM programme was built for the top 80 target accounts, combining LinkedIn company list targeting, Bombora intent monitoring, personalised outreach sequences coordinated with SDRs, and account-specific content where ACV justified the investment. Monthly account review meetings between marketing and sales assessed engagement signal progress for each Tier 1 account and adjusted the approach accordingly.
Channels producing below-target CSQL were cut or restructured. Channels producing above-target CSQL received budget increases. Content that was generating organic MQLs was identified and additional cluster pieces commissioned to reinforce topical authority. The measurement infrastructure made every investment decision evidence-based — a significant change from the prior operating model where campaigns were run on instinct and reviewed annually.
Results: 18 months of the programme
The programme produced three headline outcomes over 18 months, each of which required the prior infrastructure investment to be measurable at all.
The result that had the most organisational impact was not the pipeline figure — it was the shift in how the CFO and board perceived marketing. With attribution infrastructure in place, the CMO could present pipeline contribution data that the finance team trusted. Marketing moved from a cost centre conversation to a revenue investment conversation in the quarterly board review.
Key learnings
Generating £1.8M of marketing-influenced pipeline in 18 months for a company that had generated minimal pipeline from marketing previously required fixing the infrastructure before optimising the campaigns. The learnings centre on what RevOps alignment actually means in practice.
In the first 60 days, the team built the measurement infrastructure: UTM parameters across all channels, first-touch attribution in HubSpot, self-reported attribution on all forms, and a shared marketing-sales dashboard. Before the infrastructure existed, nobody could see marketing's contribution to pipeline — which made it impossible to defend the budget or make investment decisions based on evidence. The campaigns came second.
Sales was rejecting 78% of marketing-generated MQLs — not because lead quality was poor, but because marketing and sales had different definitions of what qualified. Marketing was promoting contacts who scored high on engagement behaviour. Sales required confirmed budget authority and a defined project. Rebuilding the MQL definition with sales input — requiring ICP fit plus one high-intent action — raised MQL-to-SQL conversion from 11% to 27% within 90 days without changing a single campaign.
Of the £1.8M in pipeline over 18 months, £640k was directly sourced by marketing (inbound form fills and demo requests). The remaining £1.16M was marketing-influenced — deals where sales had originated the conversation but marketing had touchpoints in the 90 days before opportunity creation. Reporting only marketing-sourced pipeline would have understated marketing's commercial contribution by 65%. The CFO's view of marketing changed significantly once influenced pipeline was visible in the reporting.