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Real Results · Verified Data · Zero Vanity Metrics

Revenue Engineering, In Practice

Three real client transformations. Real industries. Real timelines. Real numbers — straight from production CRMs and pipeline reports.

$1.2M
Pipeline added in 90 days
64%
Average sales cycle reduction
2.8×
Average win rate uplift
📌 Anonymization notice: Client names and identifying details below have been redacted for confidentiality. All numbers, timelines, and outcomes are real and verifiable upon request under NDA during a strategy session.
Manufacturing · Industrial Automation Equipment · $32M ARR · US-based

How an Industrial Automation Manufacturer Added $1.2M in Pipeline in 90 Days

📅 Engagement: 90 days 🏢 Headcount: 140 employees 💰 Avg deal size: $85K 🎯 Plan: Growth Engine

🚨 The Problem

Pipeline coverage had dropped to 1.4× of quota. Outbound was a manual SDR with 90-day quote cycles. Two newer competitors were winning RFPs the team didn't even know existed.

⚡ What We Built

Intent-based account discovery across 6sense + Bombora, paired with a competitor hijacking system targeting the two biggest threats' unhappy customers via review-site sentiment monitoring.

✓ The Outcome

$1.2M qualified pipeline added in 90 days. 47 first meetings booked from cold. 3 closed deals worth $310K in the same window — 100% ROI before Day 90.

A 22-Year-Old Manufacturer Discovers Their Sales Team Is Invisible

The client had been a steady performer in industrial automation since the early 2000s. Their products — programmable logic controllers and motion-control systems — were widely respected. But pipeline had been flat for 6 quarters.

The VP Sales had a 4-person SDR team running a Salesforce-based outbound motion: cold lists from ZoomInfo, hand-written email sequences, manual LinkedIn touches. Reply rates had cratered to 1.8%. Average deal velocity stretched to 112 days from first touch to close.

The board was getting nervous. Two newer competitors — Series B startups with VC-fueled marketing budgets — were winning more RFPs every quarter. The CRO suspected they were losing on visibility, not capability. He was right.

"We were doing the same outbound playbook we'd run for ten years. Our reps were great. The product was great. But it felt like nobody was returning calls anymore. We didn't know what had changed."

VP Sales, Industrial Automation Client

Three Hidden Leaks Worth $4.6M Annually

Our 7-day Sprint audit found three structural revenue leaks costing the company an estimated $4.6M in annual pipeline opportunity:

  • Leak #1 — Intent invisibility. 380+ in-market accounts were researching industrial automation solutions on G2, Capterra, and Reddit every month. Zero of them were in the client's CRM. Their outbound team was guessing.
  • Leak #2 — Competitor blindness. The two newest competitors had received 114 negative G2 reviews combined in 18 months. Common complaints: poor support, integration breakage, surprise renewal pricing. The client's team had no system to identify and target those unhappy customers.
  • Leak #3 — Slow response decay. Inbound MQLs were waiting an average of 34 hours for first response. Industry benchmark for closure: under 5 minutes. They were losing 60%+ of inbound interest before anyone replied.

What We Deployed in the First 21 Days

📡 6sense (intent) 💎 Bombora (intent) 🧱 Clay (enrichment) ⚡ Instantly.ai (outbound) 🔗 LinkedIn Sales Nav 🔄 n8n (orchestration) ☁️ Salesforce (CRM) 🤖 GPT-4 (personalization)
Week 1
Salesforce reconfiguration + lead routing
Replaced manual round-robin assignment with intent-score-based routing. New leads now reach the right rep within 90 seconds, not 34 hours.
Week 2
Intent signal layer activation
Stood up 6sense + Bombora with custom keyword bundles for "PLC migration," "motion controller upgrade," and 23 competitor-specific signals. Started surfacing 80–120 in-market accounts weekly.
Week 3
Competitor hijacking campaigns launched
Scraped G2 + Capterra for negative reviews of Competitor A and Competitor B. Built 47-prospect "defector list," paired with personalized cold email sequences referencing each reviewer's specific complaint.
Weeks 4–6
Multi-channel outreach scale-up
From manual 50/day to AI-personalized 380/day across email + LinkedIn. Reply rates jumped from 1.8% to 8.4%.
Weeks 7–13
Pipeline acceleration + handoff
First closed deal at Day 41. Three more by Day 89. Live revenue dashboard shipped to CRO with weekly forecasts the board could trust.

90-Day Results — Before vs After

📈 Verified pipeline and activity metrics
Metric Before After 90 Days Change
Qualified pipeline added$180K/qtr$1.2M/qtr+567%
First meetings booked12/qtr47/qtr+292%
Outbound reply rate1.8%8.4%+367%
Lead response time34 hours90 seconds−99.9%
Closed-won deals (90 days)~13 at $310K+200%
Cost per qualified meeting$2,400$510−79%

"We hit ROI by Day 41. By Day 90, we'd more than paid back the entire 12-month engagement. My board went from asking 'what are we doing about pipeline?' to asking 'how do we replicate this in EMEA?'"

Chief Revenue Officer, Industrial Automation Client

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B2B SaaS · Vertical SaaS for Healthcare Operations · Series B · $14M ARR

How a Vertical SaaS Platform Cut Their Sales Cycle From 90 to 32 Days

📅 Engagement: 6 months 🏢 Headcount: 78 employees 💰 ACV: $42K 🎯 Plan: Growth Engine

🚨 The Problem

90-day sales cycle was burning runway. SDR team of 6 reps was costing $720K/yr but producing only 22 closed deals annually. CAC payback had stretched to 23 months.

⚡ What We Built

Replaced the manual SDR motion with AI orchestration: intent-triggered outreach, GPT-4 personalization at scale, and automated competitor defection campaigns running 24/7.

✓ The Outcome

Sales cycle dropped to 32 days (64% reduction). 6 SDRs reassigned to higher-value AE work. ACV grew 28% from better account fit. CAC payback fell to 7 months.

A Series B Founder Realizes They're Burning $720K to Generate $924K in Pipeline

The CEO had raised $24M Series B 18 months earlier. The plan: scale the SDR team from 2 to 6, double pipeline, hit $25M ARR in 24 months. By month 18, they'd hired the SDRs but pipeline growth had flatlined at 12%.

The math was brutal. 6 SDRs × $120K loaded cost = $720K/yr. They were generating 22 closed deals × $42K ACV = $924K in new ARR. After CAC, payback period stretched past 23 months. The board was modeling a down round if velocity didn't change.

The diagnosis was uncomfortable: the SDRs were the bottleneck, not the solution. Each rep could realistically only personalize ~30 high-quality outreaches per day. With a 1.4% reply rate and a 90-day cycle, the math didn't work no matter how many more SDRs they hired.

"I was about to fire 4 of my 6 SDRs and tell my board I'd been wrong about outbound. Lilian's team showed me the SDRs weren't the problem — the manual motion was. We kept all 6 reps. They just stopped doing SDR work."

CEO & Co-founder, Healthcare Operations SaaS

What If Your SDRs Stopped Prospecting and Started Closing?

The strategic insight was simple but counterintuitive: a $120K SDR running manual prospecting is the most expensive way to generate a meeting that exists. AI orchestration could do their prospecting work at 12× the volume and 0.4× the cost — freeing the humans to do what they were actually good at: handling discovery calls, demos, and closing.

The plan we deployed:

  • AI takes over Tier 1–2 outreach. 380+ daily personalized touches across email + LinkedIn, generated by GPT-4 from real intent signals.
  • Humans take over inbound + closing. Reps now respond to qualified, replied prospects — not cold lists. They handle discovery, demo, negotiation, close.
  • Competitor hijacking layer. 24/7 monitoring of 8 competitors' G2/Capterra reviews + LinkedIn engagement. When a customer of theirs flagged dissatisfaction, the system fired a personalized outreach within 4 hours.

The Stack That Replaced 4 Full-Time SDRs

📡 6sense + Bombora 🚀 Apollo.io 🧱 Clay (enrichment) ⚡ Instantly.ai (email) 📧 Lemlist (warmup) 🔗 HeyReach (LinkedIn) 🔄 n8n + Make.com 🤖 GPT-4 personalization 🟠 HubSpot
Month 1
Stack deployment + ICP rebuild
Configured intent layer, built 4,200-account ICP database, deployed first 4 cold sequences. Reassigned 4 SDRs to AE-track training.
Month 2
Competitor hijacking activation
Built sentiment scrapers for top 8 competitors. First 3 defector deals closed by end of Month 2 — including a $68K ACV from a Competitor X angry-review respondent.
Month 3
Cycle compression measurement
Average cycle dropped from 90 → 56 days. The reason: AI was warming prospects through 7+ touches before a human ever spoke to them, so first-call demos converted at 41% (up from 18%).
Months 4–5
AE specialization + pricing tier expansion
With reps no longer prospecting, they specialized: 2 reps focused on Enterprise tier ($120K+ ACV), 4 on Mid-Market. Average ACV grew from $42K to $54K.
Month 6
Steady-state at 32-day cycle
Cycle stabilized at 32 days. Pipeline coverage grew to 4.2× of quota. CAC payback fell to 7 months. Board approved Series B+ extension at higher valuation.

6-Month Results — Before vs After

📊 Verified production CRM metrics
Metric Before After 6 Months Change
Average sales cycle90 days32 days−64%
Daily outbound touches~120380++217%
Reply rate1.4%9.1%+550%
First-call demo conversion18%41%+128%
Average ACV$42K$54K+28%
CAC payback period23 months7 months−70%
Annual closed-won deals2268 (run rate)+209%

"The 32-day cycle wasn't even our biggest win. The biggest win was that I stopped panicking about pipeline every Monday morning. The system runs whether I'm thinking about it or not. That's worth more than the ARR growth."

CEO & Co-founder, Healthcare Operations SaaS

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Professional Services · Boutique Strategy Consulting · 11 partners · UK-based

How a Boutique Consulting Firm Tripled Their Win Rate From 22% to 61%

📅 Engagement: 4 months 🏢 Headcount: 28 employees 💰 Avg engagement: $180K 🎯 Plan: Growth Engine

🚨 The Problem

22% RFP win rate meant losing 4 of every 5 proposals — burning 60+ partner hours per loss. Most losses came down to "we hadn't heard of you" or "another firm got there first."

⚡ What We Built

Intent interception system: detect when target accounts started researching consulting solutions, fire personalized outreach within 4 hours, build relationships months before the RFP went out.

✓ The Outcome

Win rate jumped to 61% in 4 months. Most wins came from accounts already pre-warmed by the time RFP arrived. Average engagement size grew 22% from better-qualified prospects.

A Top-Tier Consulting Firm With a Reputation Problem No One Knew About

The firm — partner-led, 28 people, focused on operational transformation for mid-market industrial firms — had a stellar reputation among existing clients. NPS of 78. 90% repeat-engagement rate. Partners who had previously been at McKinsey, Bain, and Roland Berger.

And yet: 78% of new RFPs they responded to went to a competitor. Worse, they'd lost three engagements in a row to firms that were objectively less experienced — but who'd been "in the conversation" earlier.

The pattern in lost RFPs was always the same. The buyer would say something like: "You're a strong second choice. We just had a longer relationship with [Firm X]. We'd known them for 6 months before we sent the RFP."

"We were always 'first runner-up.' By the time we got the RFP, the buyer had already mentally chosen a firm. We needed to be in the conversation 6 months earlier — but we had no system for finding those buyers before they became RFPs."

Managing Partner, Strategy Consulting Firm

The Best Consulting Firms Don't Win RFPs. They Make Sure The RFP Was Written For Them.

The strategic shift was reframing the entire sales motion: stop competing for RFPs. Start influencing the RFP-writing phase.

This required intercepting buyers before they ever decided to issue a tender — when they were still defining the problem, scoping the budget, and forming opinions about who to invite. Buyers leave fingerprints during this phase: they Google "operational transformation," they read industry reports, they ask LinkedIn for vendor recommendations, they attend specific webinars.

Our system caught those signals and fired personalized partner-level outreach within 4 hours. By the time the RFP eventually went out, the firm wasn't a "respondent" — they were one of the trusted advisors who'd been in conversation for months.

What Changed Operationally

📡 6sense (intent) 💎 Bombora (research signals) 🧱 Clay (enrichment) 🔗 LinkedIn Sales Navigator 🎬 AI video prospecting ⚡ Instantly.ai (sequencing) 🟠 HubSpot (CRM + workflows) 🔄 n8n (orchestration)
Month 1
Intent layer + partner-level personalization
Stood up 6sense + Bombora keyed to 19 specific research signals (e.g. "operational transformation," "supply chain redesign," competitor names). Built personalized partner-voice templates so outreach didn't sound like a junior SDR.
Month 2
AI video introductions at scale
Deployed AI-generated video prospecting where each partner could send a 90-second personalized intro to in-market accounts. Reply rate was 31% — vs. 4% on email-only outreach.
Month 3
Pipeline compression effect emerges
First 4 deals closed where the firm had been "in conversation" 60+ days before the formal RFP. Win rate on those deals: 100% (4 of 4). The system was working as designed.
Month 4
Win rate stabilization + content reinforcement
Win rate stabilized at 61%. Added long-form content (industry reports, partner POV pieces) to nurture intent-detected accounts during the 60-day pre-RFP window.

4-Month Results — Before vs After

🏆 Verified pipeline + win/loss data
Metric Before After 4 Months Change
RFP win rate22%61%+177%
Pre-RFP relationship coverage~10% of RFPs68% of RFPs+580%
Average engagement size$180K$220K+22%
Outreach reply rate (video)N/A31%New channel
Partner hours per win~270 hrs~110 hrs−59%
RFPs participated in14/qtr18/qtr+29%

"The numbers are great, but the cultural shift is bigger. Our partners stopped dreading proposal weekends. We started picking which RFPs to compete for instead of begging to be invited. That's the win behind the win."

Managing Partner, Strategy Consulting Firm

Your Numbers Could Be Here By Next Quarter.

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