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Show rate at 51% was the fastest fix with zero media spend dependency. A four-touch pre-call confirmation sequence - confirmation email with agenda, 24-hour case study matched to the prospect's role, two-hour SMS from the closer, 30-minute final nudge - brought it from 51% to 74% within 30 days. MQL-to-SAL at 38% was the second constraint, fixed with a qualifying form that filtered by ICP before any lead reached the sales calendar. Churn at 19% was flagged to the CMO as a retention problem outside paid scope but factored into every LTV calculation.

The CPL target was deliberately raised. Fewer, better-qualified MQLs were the goal, not cheaper volume. Each persona got dedicated Google Search campaigns on exact and phrase match only. LinkedIn ran a three-stage account-warming sequence: problem-framing content to cold ICP accounts with no conversion ask, anonymised proof content to engaged accounts, and a direct demo CTA only to accounts with 3 or more prior touchpoints. Five offer angles were tested per persona at 200 qualifying form completions each, with budget moving only to the variant with the lowest cost per ICP-confirmed SAL.

A two-step qualifying form replaced every generic contact form. Sub-ICP submissions routed to a five-email nurture sequence, not the CRM. Server-side CAPI was configured and 14 months of closed-won records were imported into Google Ads as an offline conversion event - so the algorithm trained on the job titles, company sizes, and queries that became signed and retained customers. HubSpot was configured to report cost per SAL, cost per opportunity, show rate, close rate, CAC, and LTV:CAC weekly to both marketing and sales.

Show rate at 47% was addressed first. A four-touch pre-call sequence went in during week one: confirmation email with a clear agenda and what to prepare, a 24-hour pre-call email with one relevant anonymised case study matched to the prospect's sector, an SMS two hours before, and a final nudge 30 minutes before the call. Show rate moved from 47% to 73% in six weeks. The qualifying form came second: three ICP-qualification questions gating the booking calendar, asking for company size, current spend, and primary objective. Sub-ICP completions routed to a nurture sequence. MQL volume dropped from 393 to 177. SALs barely dropped because the 177 remaining MQLs were converting to SALs at 57% versus 29% before.

CPL targeting was raised from £280 to a target of £600-£650. Google Search was restructured by persona, removing broad match and running exact and phrase on the query clusters that had historically produced closed clients. Meta ran two layers: a cold layer to lookalike audiences built from closed clients only (not all form completions), and a warm layer to companies that had visited the pricing or case study pages. Three offer angles were tested per persona at 150 ICP-confirmed SAL completions each. Risk reversals on every landing page: a 30-minute no-commitment diagnostic call, a written brief delivered within 48 hours regardless of whether the prospect proceeded, and two sector-matched client references on every page.

Server-side CAPI replaced pixel-only tracking on Meta. 16 months of closed-won records were imported into Google Ads as offline conversion events tagged by persona cluster and service tier. HubSpot was reconfigured to track and surface CAC, cost per SAL, show rate, close rate, and LTV:CAC on a weekly basis to marketing, sales, and the managing director. The first weekly review with the new dashboard was the first time the sales director had ever seen the firm's actual CAC.

Churn was the first conversation - not with the ad account but with the product and CS teams. A 31% annual churn rate in B2B SaaS is a product-market fit signal, not a marketing problem. I flagged this to the CMO immediately and ring-fenced the LTV calculation to the 20-100 employee segment only. Show rate at 46% was addressed with the same four-touch pre-call sequence: confirmation with agenda, 24-hour role-matched case study, two-hour SMS, 30-minute nudge. Show rate moved from 46% to 71% in five weeks. A qualifying form added company size and current toolstack as ICP gates before any lead reached the calendar.

CPL target raised from £190 to £400-£450. Google Search restructured by company size signal - filtering out single-person and micro-business queries at keyword and audience exclusion level. LinkedIn ran an ICP-only targeting layer by company size (20-200 employees), job function (operations, finance, IT), and seniority (manager and above). Three offer angles tested per segment. Budget moved only to angles producing ICP-confirmed SALs from the 20-100 employee segment. Risk reversals added to all landing pages: a 14-day full-access trial with no credit card required, an onboarding call within 24 hours of sign-up, and three case studies from companies of matching size and sector.

Server-side CAPI configured. 18 months of closed-won records from the 20-100 employee ICP segment imported into Google Ads as offline conversion events. Churned customer records excluded from the conversion set. HubSpot configured to surface CAC by ICP segment, churn rate by acquisition source, and LTV:CAC weekly. For the first time, the business could see that paid was producing the highest-churn customers in the CRM - not the most customers, the most expensive ones.

Break-even ROAS was calculated per category and shared with the client before any campaign change. Categories with gross margin below 35% were flagged for budget reduction or removal. The Meta budget split was inverted: 70% to cold new customer acquisition (lookalike audiences built from the top 20% of customers by LTV), 30% to retargeting. Klaviyo was audited to ensure retention email sequences were in place for categories being reduced in paid - so existing customers in those segments continued to receive purchase prompts via email rather than paid retargeting.

Google Shopping was restructured into three campaign tiers by product margin: Tier 1 (margin above 50%, target ROAS 300%), Tier 2 (margin 35-50%, target ROAS 400%), Tier 3 (margin below 35%, budget capped at 10% of total). Performance Max was rebuilt with asset groups by product category and customer intent signal, with audience signals built from purchasers of high-margin products only. Meta prospecting ran Value-Based Lookalikes built from the top 20% of customers by 12-month LTV. Creative testing ran five angles per category at £3,000 per angle, called at 50 purchases each.

A custom label field was added to the product feed mapping each SKU to its margin tier. This allowed automated budget rules to cap spend on Tier 3 SKUs. GA4 was configured to track revenue by product category with margin data appended, enabling contribution margin reporting at the campaign level for the first time. Server-side CAPI configured on Meta to improve signal quality for high-AOV purchases with longer consideration periods.

Show rate at 44% was fixed first - same four-touch pre-call sequence, adapted for a financial services context: confirmation email with a clear agenda and a statement of the call's scope, a 24-hour pre-call email with a relevant educational resource (no performance claims), SMS two hours before, final reminder 30 minutes before. Show rate moved from 44% to 72% in seven weeks. Qualifying form redesigned to ask three ICP-proxy questions: primary business objective, time horizon, and current structure - questions that identified genuine ICP prospects without requiring direct AUM disclosure.

Google Search restructured around intent signals specific to the ICP, with negative keyword lists filtering out terms associated with retail investors and sole traders. LinkedIn ran a company size and seniority targeting layer - directors and C-suite at companies with 50+ employees only. All ad copy was reviewed for FCA compliance before publishing. No performance claims, no specific return figures, no testimonials - instead, creative ran problem-framing angles (regulatory complexity, tax efficiency, succession planning) that resonated with the ICP without making any regulated claims.

Server-side CAPI configured. Offline conversion import set up with a 90-day attribution window to allow the full sales cycle to feed back into the algorithm. HubSpot configured to track time-to-close by source, cost per opportunity, show rate, and LTV:CAC on a weekly basis. The 90-day feedback loop was managed with cost per SAL as the leading indicator metric - reviewed weekly while waiting for closed revenue data to mature.

An in-app onboarding sequence was built to drive trial users to the activation event within 48 hours: a welcome email with a single CTA (connect your first data source), an in-app tooltip sequence on login, a day-3 email with a how-to video specific to the user's stated use case, and a day-6 email with a case study from a company of matching size and sector. Trial activation rate within 7 days went from 19% to 67%. This change alone, before any campaign change, moved trial-to-paid rate from 6% to 14% within the first month.

The algorithm was retrained on paid conversions (not trial starts) using offline conversion import. Trial starts from users who had activated and converted were imported as the primary conversion event. Trial starts from users who had not activated were excluded from the conversion set. Google Ads restructured around intent signals indicating product-aware users (comparison queries, specific feature searches, competitor alternatives). Meta ran lookalike audiences built from the top 20% of customers by lifetime value, not from all trial starts. Cost per trial start went from £91 to £172. Trial start volume fell from 1,099 to 581. Paid customers acquired per month went from 65.9 to 127.8.

Server-side CAPI configured on Meta with a custom conversion event for trial activation (not trial start). In-app activation tracking built in Intercom and passed to GA4 and HubSpot as an event. HubSpot configured to show CAC, trial-to-paid rate, activation rate, churn by acquisition source, and LTV:CAC on a weekly dashboard. For the first time, the product and marketing teams were looking at the same funnel in the same tool.

Clay was the central enrichment layer. For every account in the TAM, Clay pulled: current org structure via LinkedIn company data and Apollo contact database; technology stack via Clearbit and BuiltWith enrichment tables inside Clay; recent funding rounds, headcount changes, and job postings as buying event proximity signals; news and trigger events including leadership changes, new office openings, product launches, and compliance news in their sector; and 6sense intent topic scores showing which solution categories and competitor names the account was actively researching. Clay waterfall logic ran Apollo first for contact data, then LinkedIn scrape for org chart, then Clearbit for firmographic enrichment, with a fallback to manual SDR research for accounts where automated enrichment returned below 70% confidence on key fields. This data fed two outputs: the tier assignment score automated in HubSpot, and the personalisation brief used in Heyreach sequences and LinkedIn ad creative. Apollo managed the contact database - identifying 4-6 buying committee members per Tier 1 account (economic buyer, technical evaluator, champion, end-user lead, and procurement for contract-stage accounts). 6sense monitored all 500 accounts continuously and triggered automatic tier promotions when intent stage changed. An average of 22 accounts per month promoted from Tier 3 to Tier 2, and 8 accounts from Tier 2 to Tier 1.

Heyreach ran LinkedIn outreach for Tier 1 and Tier 2 accounts simultaneously across multiple contacts per account. Tier 1 sequences ran 5 touches over 14 days: day 1 LinkedIn connection with a single-line contextual note referencing a specific trigger event from the Clay brief; day 3 (on accept) a LinkedIn message with a relevant insight specific to their industry and problem - no ask; day 5 a LinkedIn voice note from the SDR referencing the previous message and asking one specific qualifying question; day 8 an Apollo email with an anonymised case study from a company in the same vertical and size band with one CTA; day 14 a LinkedIn follow-up closing the loop. Different messages went to different buying committee roles simultaneously: economic buyer received ROI and business outcome framing, technical evaluator received integration and security documentation, end-user champion received workflow improvement and product capability content. Tier 2 sequences ran 8 touches over 45 days at lower intensity - two insight-sharing messages, one webinar invite, one research piece - with no direct demo ask until 6sense signalled a stage change. The rule across all sequences: never pitch before the second touch.

LinkedIn Ads ran account-matched campaigns using three-tier account lists exported weekly from HubSpot into LinkedIn Matched Audiences. Tier 1: direct response creative with decision-stage messaging and a book-a-call CTA, 8-12 impressions per account per month. Tier 2: demand creation creative with problem-framing content and no direct CTA, 4-6 impressions per account per month. Tier 3: brand awareness and thought leadership only, 2-3 impressions per account per month. Google Ads ran intent-capture campaigns for branded and category terms with audiences filtered to the 500-account TAM using Customer Match lists updated weekly from Apollo - so when a Tier 1 account searched for the brand or a direct competitor, they were served a tailored ad rather than a generic homepage ad. The coordination rule: paid and outbound hit the same account in the same week with complementary but non-redundant messages. SDR outreach was personal and human. Paid creative was proof and credibility. Neither channel tried to do the other's job.

6sense monitored all 80 accounts continuously for buying stage signals. Intent topic clusters were configured specifically for this engagement: solution category keywords, competitor brand searches, implementation and RFP-related searches (indicating an evaluation already underway), and regulatory triggers relevant to the sectors served. Clay re-enriched every account weekly, pulling trigger events - leadership hires, funding announcements, new office openings, product launches, and procurement job postings (a reliable signal that a buying process is being formalised). When 6sense showed an account moving from Consideration to Decision stage, three things happened automatically: a Salesforce alert fired to the account owner and sales director; the account's LinkedIn Ads creative rotated to Decision-stage messaging with direct CTA; and the Heyreach sequence for that account's economic sponsor escalated from insight-sharing to a direct meeting request. This timing layer was the most significant individual change in the program. Before it existed, outreach intensity was calibrated to the SDR's personal read of relationship warmth. After it existed, it was calibrated to actual buying signal.

Heyreach ran simultaneous LinkedIn sequences to all mapped contacts within each account, with role-specific messaging for each contact type. Sequencing was staggered deliberately: economic sponsor contacted first, technical lead 3 days later, end-user champion 5 days later. Within a two-week window, multiple people inside the account were receiving independently personalised outreach that all pointed toward the same conversation. The personalisation was built from the Clay brief for each contact and included: their most recent LinkedIn post or article with a substantive response (not a compliment - a genuine addition or counterpoint); one specific stat relevant to their role from a recent industry report; and one reference to a trigger event at their company framed as relevant to the problem being solved. Apollo managed email sequences in parallel for contacts who had not connected on LinkedIn within 7 days - shorter, more direct, with a single insight and a single question. The rule for all sequences: never pitch before the second touch. First touch establishes relevance. Second touch demonstrates understanding of their specific situation. Third touch makes an ask.

LinkedIn Ads ran account-matched campaigns to all 80 accounts with six distinct audience segments mapped to the six buying committee roles, using job function and seniority targeting within the matched account list. Economic Sponsor creative: business outcome data, board-level risk framing, peer reference from named industry figures where available. Technical Lead creative: integration architecture, security and compliance documentation, implementation timeline data from previous deployments. End-User Champion creative: workflow improvement examples, day-in-the-life narratives, user satisfaction data from existing clients. Legal/Procurement creative: compliance certifications, contract flexibility, named legal frameworks relevant to the client's sector. Creative for each role was built from the Clay enrichment data - a financial services economic sponsor saw different specific stats and framing than a healthcare economic sponsor, even within the same campaign. Frequency targets: 10-15 impressions per contact per month for accounts in active buying stage, 5-8 for earlier-stage accounts. Google Ads ran branded and category intent capture for all 80 accounts via Customer Match, with Tier 1 accounts served a tailored landing page referencing their specific industry and use case. Every paid impression, LinkedIn connection, email open, website visit, and content download was logged at account level in HubSpot so the account owner had a real-time view of buying committee engagement across all channels simultaneously.