From $400K to $5M in Marketing-Sourced Pipeline. With Two Sales Reps.
- Apr 24
- 10 min read
11.5× Pipeline Growth (14 months) | $1K Average CAC | $90K Average LTV | 90× CAC:LTV Ratio |
84% Deals with Clear Attribution | 73% AI Query Visibility | 48 Domain Authority | 2 Sales Reps. No New Hires. |
A category leader in waiting — with no marketing to show it.
Our client manufactures a proprietary home respiratory monitoring device used in the diagnosis and management of sleep-disordered breathing. Their technology was clinically validated, FDA-cleared, and demonstrably superior to legacy alternatives. Their commercial team consisted of two seasoned medical device sales reps with deep physician relationships in their home markets.
What they didn't have was a marketing function. Literally none. No paid campaigns. No content strategy. No CRM attribution. No presence in any search channel — traditional or AI-powered. Growth came entirely through rep relationships and conference attendance. Pipeline was whatever those two reps could carry, and forecasting was educated guesswork.
When they engaged Carmine, annual marketing-sourced pipeline was approximately $400,000 — almost entirely from trade show follow-up. The company had a product that deserved a market ten times its current size. The only thing standing between them and that market was the absence of a commercial marketing system.
"We had a great product and two great reps.We just had no way to scale what was working or prove what wasn't."
What we found in Month 1.
The Diagnostic phase revealed a company in a common early-growth trap: all revenue attribution was manual and anecdotal, all pipeline was relationship-dependent, and the two reps were spending significant selling time on tasks marketing should own — education, credibility-building, and follow-up with cold contacts.
Area | Status at Engagement Start |
CRM | HubSpot — installed but unmapped. No pipeline stages. No source attribution. Contacts entered manually by reps after calls. |
Website | Informational only. No conversion paths. No tracking pixels. No lead capture beyond a generic contact form. |
Paid Advertising | None. Zero historical campaign data. |
SEO / Content | 3 blog posts published over 18 months. Domain authority: 14. Zero AI search presence. |
Email Marketing | Monthly newsletter to ~220 contacts. Open rate: 18%. No segmentation. No sequences. |
Marketing-Sourced Pipeline | $400K — trade show follow-up and inbound referrals. 0% digital attribution. |
Sales Rep Bandwidth | ~35% of rep time spent on marketing-adjacent tasks (educational follow-up, content sharing, cold outreach). |
The opportunity was clear: the reps were good at selling to physicians who understood the product. The gap was everything upstream — awareness, education, and re-engagement that should have been handled by marketing before a rep ever got on a call.
Build the full system. Connect every component to revenue.
The strategic brief was straightforward: remove the ceiling on what two reps could produce by building the marketing infrastructure that would fill their pipeline with educated, pre-qualified physicians — and give the company the attribution data to know which investments were producing it.
We structured the engagement in three parallel tracks that would eventually operate as one connected system:
Track 1 — Paid acquisition on Google and LinkedIn to build awareness and re-engage site visitors
Track 2 — Content development to educate the market and establish category authority
Track 3 — SEO and Answer Engine Optimization to capture demand in both traditional and AI-powered search
The architecture principle: every component was designed to feed the others. Content informed ad creative. Ad traffic fed retargeting audiences. Retargeting drove content consumption. Content consumption was tracked through HubSpot. HubSpot attribution closed the loop back to pipeline. Nothing operated in isolation.
Infrastructure, architecture, and first launch.
Before a dollar went into media, we rebuilt the foundation. HubSpot was reconfigured from scratch — pipeline stages defined, source attribution fields mapped, lifecycle sequences built. Google Tag Manager deployed across the site with full conversion event tracking. LinkedIn Insight Tag installed. A custom UTM framework standardized across every channel.
We built two core audience sets in parallel:
Physician audiences on LinkedIn: Sleep medicine physicians, pulmonologists, internal medicine / primary care (top-of-funnel), and a retargeting pool of site visitors and video viewers.
Search intent audiences on Google: branded queries, competitor device keywords, clinical symptom queries (home sleep testing, OSA diagnosis, respiratory monitoring), and HCP-specific search patterns.
By Day 67, the first paid campaigns were live. Initial ad spend: $8,500/month across Google ($5,000) and LinkedIn ($3,500).
Paid acquisition running. Attribution revealing what was actually working.
The first 60 days of live paid data immediately surfaced something the team hadn't known: Google Search — specifically competitive keyword terms for the category's legacy device leader — was producing demo requests at a CPL of $280. The same query type had been flagged internally as too expensive to pursue. Attribution proved otherwise.
Google — What We Ran
Branded search: Protecting the company's own name against competitor conquest campaigns.
Competitor conquest: Targeting queries that included the brand names of the two largest legacy device competitors. CPL: $310 average. Close rate from these leads: 31%.
Clinical symptom queries: Capturing physicians searching for diagnosis workflow tools and patient management options. Top performer: 'home sleep test device comparison' — $190 CPL.
Performance Max: Deployed in Month 5 using first-party CRM data as audience signals. Drove 22% of total lead volume at 18% lower CPL than manual campaigns.
Retargeting: Site visitors who consumed clinical content but hadn't converted were served follow-up ads featuring the clinical validation study. Conversion rate from retargeting: 4.1× baseline.
LinkedIn — What We Ran
Awareness video (60 seconds): Clinical workflow before/after. Targeted to sleep medicine physicians, pulmonologists, and sleep lab directors at practices with 2+ physicians. View-through rate: 38% — 2.1× LinkedIn benchmark.
Thought leadership sponsored content: Repurposed from pillar content pieces. Drove warm traffic to the clinical evidence hub. Avg. time-on-page from LinkedIn visitors: 4 minutes 12 seconds.
Retargeting: Video viewers (25%+ watch time) served a 'Request a Demo' ad in a second touchpoint. Demo request rate from this audience: 6.8%.
InMail to decision-makers: Sent to sleep lab directors and practice administrators (not physicians — operational buyers). CTR: 3.1%. Converted to 4 enterprise partnership conversations.
$13.2K Average Monthly Ad Spend (peak) | $262 Blended CPL (all channels) | 41% Demo Requests from Google | 33% Demo Requests from LinkedIn |
Content that educated the market and closed the credibility gap.
The core commercial problem for a disruptive device: physicians are inherently skeptical of new clinical tools. Before they'll book a demo, they want to understand the clinical evidence, the workflow implications, and how peers are using it. That education burden had been falling entirely on the reps.
We built a content architecture to carry that burden at scale.
The Pillar Assets
Asset | Performance |
Clinical Evidence Guide | 14-page deep-dive on the clinical validation data behind home respiratory monitoring. Written for the skeptical pulmonologist. Downloaded 847 times in 6 months. Used by reps in 73% of follow-up sequences. |
The Diagnosis Gap Report | Original survey data from 200 sleep medicine physicians on current home sleep test protocols, device satisfaction, and workflow friction. Positioned the company as a category researcher, not just a vendor. Covered in 3 industry publications including Sleep Review. |
ROI Calculator | Interactive tool: practice revenue impact of diagnosing X% more patients per month. Average session time: 6 minutes. 31% of calculator users converted to demo request within 14 days. |
Case Study Series | 4 anonymized practice case studies showing before/after on diagnostic volume, billing workflow, and patient outcomes. Average read time: 8 minutes. Highest-performing piece of content across all formats. |
Video: Clinical Workflow Walkthrough | 4-minute product demonstration designed for a physician audience. Scripted, produced, and distributed across YouTube, LinkedIn, and the site. 14,200 views. 11% click-to-demo from YouTube end screens. |
Every content piece was mapped to a HubSpot workflow. Contact engagement with content triggered automated follow-up sequences, updated lead scores, and notified the rep assigned to the account — with context on what the physician had read or watched.
The result: reps entering calls with physicians who had already consumed clinical validation content closed at 2.9× the rate of cold-entry conversations.
"The reps stopped having to explain why ourdevice was different. The content had alreadydone it. They just had to close."
Becoming the answer — in Google and in AI.
When we started, the company had a domain authority of 14 against a competitive set that ranged from 28 to 67. They appeared in zero AI-generated answers across ChatGPT, Perplexity, or Google's AI Overviews for any relevant clinical query. In practical terms: the company did not exist in search.
Fourteen months later, domain authority sat at 48 — above three of the five major competitors — and the company appeared in 73% of monitored AI queries related to home respiratory monitoring, sleep diagnostics, and OSA management workflows.
SEO — Traditional Search
We targeted three query tiers:
Clinical decision queries: 'home sleep test device comparison,' 'OSA diagnosis device options,' 'home respiratory monitoring for sleep labs.' These represent active purchase intent. We now hold a top-3 position for 14 of the 22 priority terms.
HCP education queries: 'home sleep testing workflow,' 'OSA diagnosis billing codes,' 'sleep medicine practice management.' These capture physicians in research mode. Pillar content ranks for 34 terms in this category.
Competitor alternative queries: Queries including competitor brand names + 'alternative' or 'comparison.' Now ranking top-5 for 8 such terms, producing 19% of organic traffic.
Organic traffic grew from 340 monthly sessions to 4,870 — a 14.3× increase. More importantly, organic traffic converted to demo requests at 2.1× the rate of paid traffic, because visitors had self-selected through search intent.
AEO — AI Search Presence
We conducted a baseline audit across ChatGPT, Perplexity, Claude, and Google AI Overviews in Month 2. The company appeared in 0 of 47 monitored clinical queries. The top three competitors appeared in a combined 68% of those queries.
The strategy to close that gap:
Content restructuring for citation-readiness: Every major content piece was reformatted with structured headings, explicit claim-evidence pairs, and FAQ sections that directly addressed the query language AI systems were responding to.
Third-party authority building: We secured editorial coverage in Sleep Review, Respiratory Care journal, and two HCP-facing digital publications. These external references are the primary trust signals that AI models use to determine citation-worthiness.
Schema markup and technical SEO: FAQ schema, HowTo schema, and MedicalCondition schema deployed across clinical content pages. Google AI Overviews began featuring excerpts from the site within 6 weeks of implementation.
Proprietary data publication: The Diagnosis Gap Report (original survey data) was published with a citable permalink and picked up by two industry newsletters. Original data is significantly more likely to be cited by AI models than derivative content.
0 → 73% AI Query Visibility | 14 → 48 Domain Authority | Top 3 Rankings on 14 Priority Terms | 14.3× Organic Traffic Growth |
By Month 11, AI-sourced pipeline was a measurable category in HubSpot. Contacts who came in through AI-referred traffic — captured via UTM tagging on links within AI citations — converted to demo requests at a 3.4× rate compared to paid traffic. The explanation: a physician who found the company through an AI answer had already received an implied endorsement from a system they trust.
The data that changed every budget conversation.
Before the engagement, 0% of closed deals had verifiable marketing attribution. Sales credited relationships. Marketing had no seat at the revenue table because it had no data to bring to it.
We built a full attribution architecture in HubSpot, connected to ad platforms via native integrations and UTM frameworks, with manual touchpoint logging for rep-sourced interactions. By Month 4, attribution data was reliable enough to use in commercial decisions. By Month 8, it had already changed three budget decisions.
What Attribution Revealed
Google Search was producing 41% of demo requests — and had been informally tagged as 'unclear ROI' internally. The data ended that conversation.
LinkedIn video retargeting had a demo rate of 6.8% — the highest of any paid touchpoint. Budget shifted accordingly.
Physicians who consumed 2+ content assets before their demo closed at a 64% rate vs. 22% for physicians who came in cold. This finding changed how reps prepared for calls.
Trade shows — previously the primary marketing investment — produced $400K in pipeline at a CPL of $1,840. Digital channels produced $4.6M in pipeline at a blended CPL of $262. The company did not attend two events in Year 2.
The ROI calculator was the highest-converting single asset. 31% of calculator users converted to demo within 14 days. It now sits on the homepage.
CAC and LTV — The Real Numbers
Across the 14-month engagement, total Carmine retainer and ad spend managed came to approximately $312,000. Total new customers acquired through marketing-sourced pipeline: 62. Average CAC across all marketing channels: $1,020.
Average customer lifetime value — calculated on device subscription revenue, consumable reorders, and platform licensing — came to $88,400 over a 36-month average customer relationship.
$1,020 Average CAC | $88,400 Average LTV (36-month) | 86.7× LTV:CAC Ratio | 62 New Customers, 14 Months |
For every dollar spent acquiring a customer, the company received $86.70 in lifetime revenue. In a business where the unit economics were previously invisible, this number became the foundation of their Series A fundraising narrative.
14 months. Two reps. No new hires. $5M in pipeline.
The full results of the engagement across all tracks:
Metric | Result |
Marketing-Sourced Pipeline | $400K → $5.1M (11.5× growth) |
New Customers Acquired | 62 net new physician practices enrolled |
Average Deal Size | $18,400 (up from $11,200 at start of engagement) |
Sales Cycle Length | 42 days avg. (down from 74 days at start) |
Demo-to-Close Rate | 38% (up from 19%) |
Rep Time on Marketing Tasks | Reduced from 35% to 9% of weekly time |
Paid Advertising CPL | $262 blended across Google and LinkedIn |
Google Campaign ROAS | 14.2× on direct demo conversions |
LinkedIn Campaign ROAS | 9.8× including pipeline influenced |
Organic Traffic | 340 → 4,870 monthly sessions (14.3×) |
AI Search Visibility | 0% → 73% of monitored queries |
Domain Authority | 14 → 48 (3 of 5 major competitors now below) |
HubSpot Attribution Coverage | 0% → 84% of deals with first-touch source |
Content Asset Downloads | 3,400+ across all pillar pieces |
Email Sequence Open Rate | 34% avg. (vs. 18% prior newsletter) |
CAC | $1,020 average across all marketing channels |
LTV | $88,400 average (36-month customer relationship) |
LTV:CAC Ratio | 86.7× |
"For the first time, I could walk into a board meetingand tell them exactly which channels were producingpipeline — and prove it. That changed everything."
What two sales reps can do when marketing does its job.
The most important outcome of this engagement isn't the pipeline number. It's what the two sales reps were able to do differently.
Before Carmine, they were doing everything: cold outreach, education, follow-up, content sharing, and closing — with no leverage and no measurement. After the system was running, they entered every conversation with a physician who had already been through an education sequence, had consumed clinical evidence, and had been retargeted enough times to know the product before the call started.
Close rates doubled. Sales cycle compressed by 43%. Average deal size grew because physicians were coming in already convinced of the premium value. The reps didn't get better at selling. The system gave them better opportunities to sell into.
Two reps. No new headcount. $5M in pipeline. The delta was the marketing system.