This report benchmarks B2B digital marketing performance across UK companies, drawing on programme data from 47 active client programmes plus published research from LinkedIn, Google, Gartner, and independent sources. Use these benchmarks to assess whether your programme is performing at, above, or below what comparable organisations achieve — and identify the specific investments most likely to close performance gaps.
Overview and methodology
Data covers the period Q1 2025 to Q1 2026. Programme data is anonymised and segmented by company revenue (Sub-£5M, £5-20M, £20-100M, £100M+). Sector representation: SaaS/software (38%), professional services (24%), fintech (16%), manufacturing technology (13%), other B2B (9%). All conversion metrics are based on agreed MQL-to-SQL definitions with client sales teams, not self-reported marketing qualification rates.
Key findings
The headline finding from the 2025-26 data is a widening performance gap between organisations that have invested in attribution infrastructure and those that have not. Top-quartile performers are not just spending more — they are making better investment decisions because they can see which channels are producing pipeline and which are not.
Median B2B programmes allocate 78% of paid media budget to demand capture — search, retargeting, and bottom-funnel LinkedIn. Top-quartile programmes allocate 38% to demand creation: brand awareness, thought leadership amplification, and upper-funnel account-based display. The pipeline ROI differential justifies the investment, but it requires 6-12 months of evidence accumulation before attribution data confirms the impact.
Programmes using third-party intent data (Bombora, G2, or 6sense) achieved median MQL-to-SQL conversion of 28% versus 14% for programmes without intent data overlay. The mechanism: intent signals enable ICP prioritisation and outreach timing that dramatically increases the proportion of leads genuinely in a buying window at the point of first contact.
Programmes with multi-touch attribution infrastructure in place for 12+ months consistently outperform those without — not because attribution improves campaign performance directly, but because it enables evidence-based investment decisions. Programmes operating without attribution systematically overinvest in last-touch channels (search, direct) and underinvest in the awareness channels that drive the buying behaviour those channels eventually capture.
Programmes that began structured AEO investment 12+ months ago are now generating measurable AI citation presence — brand appearances in AI Overviews and Perplexity responses for their primary category queries. Programmes starting AEO investment today face a 6-12 month lag before measurable citation impact. The first-mover window in AEO is still open, but it is narrowing as more B2B companies recognise and act on the opportunity.
Benchmark data by category
Performance by company revenue band
Benchmark norms shift significantly by company size. A conversion rate that is excellent for a £2M ARR company is mediocre for a £50M company with established brand presence and a mature sales motion.
At this stage, brand is weak and channels are establishing. Median MQL-to-SQL: 11%. Priority investment: content and SEO for organic pipeline, LinkedIn ABM for target account coverage, founder-led thought leadership. Expected pipeline ROI: 0.6-1.2× in year one, rising to 2×+ by year three as brand builds.
Brand establishing. Inbound beginning to supplement outbound. Median MQL-to-SQL: 16%. Priority investment: demand generation to build category presence, intent data for outbound precision, first ABM programme for enterprise segment if ACV justifies it. Pipeline ROI should be approaching 1.5-2×.
Established brand in primary ICP. Inbound and outbound in balance. Median MQL-to-SQL: 21%. Priority investment: ABM at enterprise tier, attribution infrastructure, content for new segments and geographies, RevOps for pipeline reporting maturity. Pipeline ROI: 2-3×.
Strong brand, established content engine, complex sales motion. Median MQL-to-SQL: 26%. Priority investment: account-level personalisation, multi-channel buying committee engagement, partner and channel programme development, geographic expansion. Pipeline ROI: 3×+ on mature programmes.
Implications for B2B marketers
- If your MQL-to-SQL is below 14%: You have a lead quality problem before you have a volume problem. Tighten ICP scoring criteria, audit lead sources, and raise your MQL threshold before increasing marketing spend. Adding volume to a broken qualification process compounds the problem.
- If your pipeline ROI is below 1×: You are not yet generating more pipeline than you are spending. This is not inherently a problem in year one — brand and content investment has delayed payback — but if you are in year two or three with a pipeline ROI below 1×, the programme structure needs review, not just additional budget.
- If you are not using intent data yet: The 2× improvement in MQL-to-SQL conversion makes it one of the clearest ROI-positive investments available for organisations with ICP account lists above 500 companies. The payback period on Bombora or G2 intent data is typically 3-4 months for programmes spending £10,000+/month on paid media.
- If organic traffic is below 22% of total: You are over-dependent on paid channels for traffic and pipeline. A programme without an organic engine is structurally fragile — any budget reduction or paid media efficiency decline creates an immediate pipeline problem. Building content and SEO to 35%+ organic traffic share is a two-year investment that dramatically improves programme resilience.