Marketing Automation Suite
Lead scoring, email sequences, social posting, and campaign analytics on autopilot.
Client
B2B services firm (mid-market consultancy)
Industry
Professional Services
Region
Global (US, EU, India)
Duration
7 weeks build + 12 weeks optimisation
A global B2B services firm was generating leads but losing sales trust - 70% of leads were unqualified. We built an end-to-end marketing automation suite: ML lead scoring, AI-personalised email sequences, social content automation, and unified attribution. Result: 60% more qualified leads to sales, 45% lower cost per qualified lead, 4× content output without new hires, and marketing-sourced pipeline doubled.
Headline results
The client is a mid-market B2B services firm with offices in three regions and ~12 marketing team members. Inbound was strong on volume but quality was inconsistent - sales had stopped trusting marketing leads. Content production was capped by the team's hours. Attribution across paid, organic, email, and referral was a manual spreadsheet exercise quarterly. Every conversation about budget allocation was guesswork.
What we built and shipped
ML lead scoring & routing
ML-based lead scoring combining firmographic data, behavioural signals, and intent data - feeding only qualified leads to sales while continuing to nurture the rest.
- Score components: firmographic fit, engagement, content depth, intent surge
- Score updated in real time on every behavioural event
- Auto-route over a threshold; auto-nurture below; review queue for grey-zone
Personalised email & LinkedIn sequences
AI-drafted sequences customised to each prospect's industry, role, and pain points - sales reps approve before send or let high-confidence drafts go automatically.
- Per-prospect personalisation, not per-segment
- LinkedIn voice-note generation for high-value prospects
- Sequences adapt mid-flight as the prospect engages or stays silent
Social & content automation
AI-generated social content built from internal expertise, case studies, and thought-leadership pieces, scheduled across channels with engagement tracking and follow-up flows.
- 10× the social output with the same content team
- Thought-leadership repurposed across LinkedIn, X, and newsletter
- Engagement-driven follow-up sequences for commenters
Unified multi-touch attribution
Multi-touch attribution model pulling from ads, web, email, CRM, and sales call recordings - giving leadership a single source of truth for what's actually working.
- Server-side attribution that survives ad-blockers and iOS 14
- Account-level views, not just contact-level
- Budget reallocation suggestions every Monday based on real ROI
How it actually works
Lead scoring engine
Real-time scoring service running an XGBoost model over firmographics, behavioural signals, and intent data - updating scores on every event and publishing to HubSpot and Slack.
Generative content pipeline
Per-prospect personalisation by combining CRM data, content library retrieval, and brand-voice prompts. Approval gates for the first 3 sends per prospect, then auto-send on high-confidence drafts.
Social automation orchestrator
Per-channel formatting and scheduling, with engagement tracking and automated follow-up triggers tied back to the CRM.
Attribution layer
Server-side event collection (Segment + custom collectors), multi-touch model with weighted credit allocation, and an executive dashboard updated daily.
Phased delivery timeline
Data & scoring foundation
Mapped data sources, built the unified event store, trained the first lead scoring model on 18 months of historical conversion data.
Content + sequence build
Shipped AI-drafted email sequences and the brand-voice prompt library. Reviewer dashboard for sales to approve sends.
Social automation + attribution
Built the social orchestrator and the multi-touch attribution model. Connected the executive dashboard.
Iteration on signal & trust
12 weeks of weekly tuning - scoring thresholds, sequence variants, attribution weights - until sales reported >85% trust in inbound leads.
Before vs after
Same business, same team - measurably different operating model after the engagement.
- Sales trust in marketing leads30% deemed qualified
- Cost per qualified leadBaseline
- Content output (social + email)1× capacity
- Attribution clarityQuarterly spreadsheet guesses
- Marketing-sourced pipeline (qtr)Baseline
- Average sales-cycle lengthBaseline
- Sales trust in marketing leads85%+ deemed qualified
- Cost per qualified lead-45%
- Content output (social + email)4× same team
- Attribution clarityDaily executive dashboard
- Marketing-sourced pipeline (qtr)2× lift
- Average sales-cycle length-22%
What changed and by how much
Operational and revenue metrics tracked from go-live, measured against the pre-engagement baseline.
Composition of impact
Approximate breakdown of how this engagement contributed to the business outcome - the headline metric is a roll-up of these levers.
- Qualified-lead lift & sales trust32%
- Content production scaling22%
- Cost-per-lead reduction20%
- Attribution-informed budget reallocation16%
- Sales-cycle compression10%
What we built it with
Marketing platforms
- HubSpot CRM + Marketing Hub
- LinkedIn Sales Navigator
- Klaviyo for transactional
AI & ML
- Custom XGBoost scoring model
- Anthropic Claude for content
- Vector retrieval over content library
Data & attribution
- Segment for event collection
- Custom server-side attribution model
- GA4 + Looker for reporting
Automation & routing
- Slack alerts to sales
- Custom Node.js orchestration
- Looker dashboards for execs
What we de-risked along the way
AI-drafted content sounds off-brand
Mitigation: Brand-voice eval suite, approval gates on the first 3 sends per prospect, per-channel quality checks weekly.
Sales loses trust if scoring is wrong
Mitigation: Score recalibration weekly against actual closed-won data; transparency in the score breakdown so sales can challenge bad scores; rapid iteration the first 12 weeks.
Attribution becomes a political football
Mitigation: Multi-touch model published with assumptions documented; methodology approved by CFO and Heads of Marketing/Sales before go-live; quarterly review.
What we'd carry into the next build
Sales trust is built in 90 days, not 9 weeks
The build was 7 weeks; the trust-building was 12 weeks of weekly score tuning, dashboard reviews with sales leadership, and lost-deal analysis. Without that phase, sales would have ignored the leads regardless of how good the scoring was.
Personalisation per prospect beats segmentation
We tried segment-based content first. The lift was marginal. Per-prospect personalisation with AI drafting was where the conversion lift actually came from - and the marginal cost of per-prospect was nearly identical at scale.
Multi-touch attribution must be ratified by finance
Attribution models always have assumptions. Getting the CFO to ratify the weighting before go-live made it impossible to dismiss as marketing's vanity model. That governance step was as important as the model itself.
Content automation lifts when the source is real
AI-generated content from no source is filler. AI-generated content built on real internal expertise, case studies, and partner-level thinking is genuinely useful - and the team became advocates because the output was good, not because we asked them to be.
ROI & payback
Investment
Mid-six-figures one-time build + low-five-figures monthly run cost
Payback period
Inside 4-5 months on incremental marketing-sourced pipeline
Year-1 ROI
Estimated 5-7× ROI in year one, with the bigger compounding from sales-cycle compression and sales-team trust
“Marketing went from generating leads to generating pipeline. Sales finally trusts what we send them, and we can show exactly where every customer came from. The fights about budget allocation became data-driven instead of opinion-driven - that alone changed how we run the function.”
Questions about this engagement
How does AI lead scoring actually work?+
We trained an XGBoost model on 18 months of historical conversion data, using features across firmographics (size, industry, region), behavioural signals (pages visited, content downloaded, email engagement), and intent (third-party intent data, sales-call signals). The model outputs a score in real time on every new event. Above threshold goes to sales; below is auto-nurtured; grey-zone goes to a human review queue.
How is the AI-drafted content kept on brand?+
A brand-voice eval suite that runs on every prompt change, a curated content library that the LLM retrieves from rather than inventing, and approval gates on the first 3 sends per new prospect with sales sign-off. After 50 approved sends, high-confidence drafts go automatically.
What is multi-touch attribution and why does it matter?+
Multi-touch attribution credits every meaningful interaction a customer has before they convert - paid ads, organic content, email, referral, sales calls - rather than giving all credit to the last touch. For a B2B services firm with 6-12 month sales cycles, multi-touch is the only honest way to see what's working and reallocate budget accordingly.
How long until pipeline impact showed up?+
Marketing-sourced pipeline doubled in the second full quarter after go-live. The first quarter was about scoring accuracy and sales-team trust; the second quarter was when sales started prioritising marketing leads and the cycle compression flowed through.
Did the marketing team get smaller?+
No - they got more strategic. The 4× content output and the 60% lift in qualified leads came from the same team focused on higher-value work instead of execution. Headcount was preserved; output scaled.
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