4x the pipeline at 45% lower CAC, with hundreds of hours handed back to the team
Rebuilding how VRIFY found, warmed and converted accounts in a 2,400-company market where every misfire burns a share of the addressable market.
VRIFYVP MarketingMay 2025 – Present6 min read- 70300
- Leads per month
- −45%
- Customer acquisition cost
- 42.5 mo
- Time to value
- 5x
- LTV:CAC at peak
- 8-fig
- Net ARR increase
Context
VRIFY builds AI-assisted mineral discovery software: DORA, whose models reinterpret an exploration company’s existing geological data to surface new drill targets, and Viz, which turns that data into interactive 3D presentations. When I joined as VP Marketing in May 2025 the company was bringing in roughly 70 leads a month, and most of the market had never heard of us.
The constraint that shaped everything is the size of the market. There are roughly 2,400 companies worth selling to. In a market that small you cannot spray and pray. Get the message wrong with a handful of accounts and you have burned a full percentage point of your total addressable market, which is tens of millions of dollars in lifetime value.
I lead a 10-person marketing team, help oversee 4 sales reps and 3 external agencies, and manage a multi-seven-figure budget.
The thesisIn a 2,400-account market, volume is a liability. The edge is precision: the right account, the right persona and the right message at the right moment, with the busywork automated so people spend their time on the conversation.
Why precision beats volume here
Simple arithmetic on a small, finite market.
| Measure | Value | What it means |
|---|---|---|
| Addressable companies | ~2,400 | Every account is named and knowable |
| 1% of the market | ~24 companies | A bad campaign can burn this in a week |
| Leads per month, before | ~70 | Mostly unshaped inbound |
| Leads per month, after | ~300 | Targeted, tagged and routed on arrival |
Most demand-generation playbooks are written for markets with tens of thousands of potential buyers, where a weak campaign is just a wasted week. VRIFY’s market is closer to a list than a funnel. With roughly 2,400 companies in total, each account carries real weight in the forecast, and the people inside them talk to each other at the same handful of conferences.
That changes the economics of a mistake. A generic email or an off-target ad doesn’t just underperform; it teaches a buyer to ignore us, and in a market this small that buyer may not come back for years. So the bar for every touch became: would this be relevant if it were the only thing they saw from us this quarter?
Primary personas
Every asset was rebuilt for a specific role, company size, stage of exploration and goal. These are the four buyers it was built around.
- Cares about
- Discoveries, capital raises and the investor story
- Message
- Find more in the data you already own, and show the market
- Proof
- Client results, executive briefings, investor-ready visuals
- Cares about
- Whether the model actually works on their rocks
- Message
- Transparent AI you can interrogate, built by geoscientists
- Proof
- Technical write-ups, field validations, demos on their own data
- Cares about
- Stakeholder trust and moving a permit forward
- Message
- Show the whole project transparently, from exploration to reclamation
- Proof
- Role-specific one-pagers and immersive 3D presentations
- Cares about
- Portfolio-wide value, security, process
- Message
- One platform across every asset in the portfolio
- Proof
- White-glove pilots, security documentation, multi-asset cases
Before the rebuild, most content spoke to a single generic buyer. In practice a purchase involved several people with very different questions: an executive thinking about discoveries and the investor story, a geologist who needed to believe the model before trusting it, and, increasingly, permitting and community-relations leaders who needed to explain a project to stakeholders.
Each persona got its own messaging, proof points and assets, and each asset was further tailored to company size and stage of exploration. The permitting one-pager is a good example: one persona, one problem (stakeholder trust) and one product angle, written in that buyer’s language rather than ours.
The system
From the first signal to a paying customer, with every stage feeding data back into the next campaign.
Read the diagram in order. It starts with signals that tell us an account is worth attention right now: a press release, an upcoming conference, or engagement with our content and website. Those accounts are matched to an ICP and tiered, which decides how much attention they get and which program they enter.
From there, accounts are warmed through ABM and a low-friction educational journey before anyone asks for a meeting. Conversion happens through whichever route fits the account: SDR agents for scale, events for relationships, or white-glove moments for the largest opportunities. Sales takes over with weekly opportunity reviews, and onboarding is built to deliver value in about 2.5 months.
The dashed line underneath is what makes it a system rather than a sequence. Every touch is tagged and measured, and those results decide who we target and what we say next.
The ten moves
- 01Rebuilt the messaging around the buyerRe-identified our ICPs and rebuilt every asset for the reader’s role, company size, stage of exploration and goals. Conversion rose by several percentage points and time to value fell from about 4 months to about 2.5.
- 02Replaced “build it and they will come” with educationA low-friction, multi-touch educational journey warms accounts before any sales ask, then hands them to SDR agents, sales and events for conversion.
- 03Tripled the events programGrew from ~15 to ~45 events a year and changed how we attend them. That story has its own case study.
- 04Rebuilt ABM from the list upScraped and enriched the accounts we wanted, then ran campaigns against them with messaging tailored to each ICP.
- 05Made marketing proactive, not reactiveMarketing now brings events, conferences and enablement to sales instead of waiting for requests. I hold weekly opportunity check-ins with every rep to keep the pipeline replenished and shorten time to close.
- 06Launched paid, LinkedIn firstA prospecting-to-retargeting structure, with dedicated campaigns aimed at the ABM account lists.
- 07Built a brand studio in houseDesign moved in house and every asset moved to top production quality: video, PDFs, sales decks and investor decks. Later, an AI tool generated these documents from templates within the guardrails we set.
- 08Made everything templated and replicableMoved the website to Webflow with programmatically built pages. Every inbound is tagged, written to the CRM and announced to every connected tool for follow-up.
- 09Became the industry’s source of truth on AIBrought on technical writers, made the software transparent about what it can and cannot do, and backed every claim with results from work in the field with clients.
- 10Instrumented everythingData capture went from nascent to granular, down to email subject lines and how we followed up each conference lead, so every part of the system could be optimized continuously.
None of these moves would have worked on its own. Sharper messaging needs a way to reach the right people, ABM needs content worth engaging with, and events need follow-up that actually happens. The order mattered: the messaging and ICP work came first, because every later move depended on knowing exactly who we were talking to and what they cared about.
How it was measured
| Metric | Why it mattered | Cadence |
|---|---|---|
| Leads and pipeline by source | Where to double down and where to cut | Weekly |
| CAC by channel | Spend only scales where payback holds | Weekly, automated |
| LTV:CAC | The guardrail on every budget decision | Monthly |
| Conversion by persona | Proof that the messaging rebuild was working | Monthly |
| Time to value | Faster value means faster renewals and referrals | Per cohort |
| NPS | Whether the experience matched the promise | Quarterly |
The rule was simple: if a metric didn’t change a decision, we stopped reporting it. Leads and pipeline by source told us where to invest; CAC by channel and LTV:CAC told us whether that investment was paying back. Conversion by persona was the check on the messaging rebuild, and time to value and NPS told us whether the experience after the sale lived up to the promise before it.
Several of these were automated, with channel performance calculated continuously and recommendations surfaced directly, so budget decisions could happen weekly instead of quarterly.
Results
Before and after, drawn to scale. Revenue figures are under NDA.
Also: LTV:CAC reached 5x at its peak, NPS rose by double-digit percentages, and the program delivered a net eight-figure ARR increase.
Monthly leads grew from roughly 70 to roughly 300 while customer acquisition cost fell by 45%. Growth usually comes at the price of efficiency; here both moved in the right direction at the same time, because the same precision that improved conversion also stopped spend from leaking into accounts that were never going to buy.
Time to value dropped from about four months to about two and a half, which matters as much as the top of the funnel: customers who see value sooner renew, expand and refer.
Working as a tag team with VRIFY’s commercial leadership, we also brought 80% of Tier 1 accounts into active coverage, and account-based programs came to source about a fifth of all pipeline.
How LTV:CAC held while we scaled
We built systems that calculated channel performance and made recommendations, then acted on them every week. Budget followed the evidence: channels that paid back got more, and the ones that did not were cut or reworked. Holding the ratio was constant optimization, not one big decision.
When the market is small, treat every account like a named relationship and automate everything that is not the relationship. Precision compounds; volume burns the list.