The scenarios below are illustrative composites of problems B2B teams commonly bring to us and how we would approach them. Company details, figures and timelines are representative examples, not reported client results. Ask us for references on a call.
Law Firm
£5M Revenue
Revenue Diagnostics
2 weeks
A mid-sized law firm was struggling with a HubSpot implementation that had been in place for 18 months but was largely ignored by partners and fee earners. The CRM contained thousands of contacts but no one could reliably identify which client relationships were active, which prospects were warm, or which referral sources were most valuable. Forecasting was done in spreadsheets outside the CRM.
A two-week Revenue System Diagnostic, including full audit of CRM data quality, process workflows, and adoption patterns; interviews with partners, business development team, and marketing; analysis of pipeline from initial enquiry through engagement and renewal; and assessment of integration health. Leaks this kind of diagnostic typically surfaces: stalled proposals, decaying referral relationships, and missed cross-sell opportunities.
The deliverable is a prioritised 90-day roadmap that sizes each revenue leak in £ from the firm's own pipeline data, plus a ranked list of adoption barriers, so the partnership gets clear priorities rather than an overwhelming "fix everything" list. Success means leadership can approve and fund the roadmap with confidence.
B2B Technology
£8M ARR
CRM as a Product
4 months
A B2B SaaS company had implemented HubSpot two years prior but adoption had flatlined at 35%. Sales reps complained the CRM "didn't match how we sell" and maintained their own spreadsheets. Marketing tracked MQLs but couldn't prove which campaigns drove closed revenue. Forecast variance was ±40%, making planning nearly impossible.
Rebuild the CRM with product thinking over roughly 4 months: Discovery (mapped actual customer journey, defined ICP, redesigned lifecycle stages to match PLG motion); Build (integrated product usage data, built scoring model, created automated workflows, designed dashboards); Enablement (role-based training, established feedback loops, created internal champions).
Daily CRM usage by role, forecast variance against actuals, customer acquisition cost, and marketing-attributed pipeline, each baselined at the start so progress is measured against the company's own numbers. The aim is one agreed definition of "product-qualified lead" so sales and marketing stop arguing about lead quality.
Hospitality & Events
£12M Revenue
CRM as a Product
5 months
A boutique hotel and events group had corporate clients generating 60% of revenue but no systematic way to manage those relationships. Each property manager kept their own records. Conference and event bookings were tracked in spreadsheets. There was no visibility into repeat booking patterns, renewal timing, or cross-sell opportunities across properties.
Rebuild the CRM with hospitality-specific lifecycle design over roughly 5 months: Discovery & Design (mapped corporate client lifecycle, defined qualification criteria, created account hierarchy); Build (migrated and deduplicated contact data, built enquiry routing workflows, created renewal tracking); Enablement (trained property managers, established weekly pipeline reviews, created feedback loops).
Database accuracy, enquiry response time, repeat booking rate, and multi-property cross-sell revenue, each baselined before the rebuild. The aim is a single source of truth for every corporate relationship across properties.
B2B Services
£15M Revenue
Sales Operations & Automation
3 months
A B2B services company had a 12-person sales team spending 15+ hours per week on manual pipeline updates, proposal generation, and administrative tasks. Lead routing was manual—hot inbound leads sat in a shared inbox for hours. Proposals took 3-5 days to generate. Territory management was a spreadsheet nightmare.
Automate sales operations over roughly 3 months: Pipeline Automation (automatic stage progression, stale deal alerts, automated task creation); Lead Management (instant lead routing, round-robin assignment, automated follow-up sequences); Proposal Automation (integrated PandaDoc, templated proposals, e-signature); Reporting & AI (forecasting dashboard, AI deal scoring, at-risk alerts).
Rep hours spent on admin, lead response time, deal cycle length, proposal turnaround, and forecast variance, measured before and after so the time handed back to sellers shows up in the team's own numbers.
Financial Advisory
£18M Revenue
Marketing Operations & Attribution
4 months
A financial advisory firm was spending £400K annually on marketing but couldn't prove which activities drove new client acquisition. Marketing reported on vanity metrics whilst leadership wanted to see pipeline and revenue impact. Lead scoring didn't exist. The marketing director was under pressure to justify the budget but had no attribution data.
Build marketing operations and attribution over roughly 4 months: Foundation (integrated all marketing tools, standardised campaign tagging, defined lead lifecycle stages); Attribution Model (implemented multi-touch attribution, connected campaigns to revenue); Lead Scoring (developed scoring model, created MQL threshold, automated lead handoff); Automation (built nurture workflows, automated campaign execution).
Marketing-attributed pipeline, campaign execution time, lead-to-MQL conversion, channel-level return, and monthly reporting effort. The aim is budget decisions based on attribution data and an agreed definition of "marketing qualified lead".
B2B SaaS
£25M ARR
Data Governance & AI Strategy
5 months
A B2B SaaS company wanted to implement AI for lead scoring, churn prediction, and expansion identification—but their data was a mess. 30% duplication rate, inconsistent data entry, no clear data ownership, GDPR compliance concerns, and previous AI initiatives had failed due to poor data quality.
Build a data governance foundation and AI strategy over roughly 5 months: Data Assessment (audited quality across all systems, identified patterns, assessed GDPR compliance); Governance Framework (established data ownership, created quality standards, implemented GDPR protocols); Data Remediation (deduplicated CRM, standardised fields, enriched records); AI Strategy (readiness assessment, prioritised use cases, designed agents); AI Implementation (deployed lead scoring, churn prediction, expansion identifier).
Database accuracy, consent and retention coverage, MQL-to-SQL conversion, early warning on at-risk accounts, and expansion pipeline surfaced. AI models only go live once the underlying data meets agreed quality thresholds, with automated validation to keep it there.
Start with a free 30-minute audit. We'll look at your current setup, identify what's working and what isn't, and recommend a path forward based on what we see across B2B revenue teams.