From product-led to marketing-led: building a moat when new competitors launch every two weeks
Moving from “build it and they will come” to going direct to the customer, with a multi-channel mesh and white-glove moments designed around trust.
VRIFYVP MarketingMay 2025 – Present3 min read- ~2 wk
- Pace of new AI competitors
- 95%
- Of the market that did not yet know VRIFY
- 2
- Conversion paths: inbound and white-glove
- −1.5 mo
- Time to value
Context
VRIFY grew up product-led. A strong platform and the founders’ reputation in the industry pulled customers in, and that “build it and they will come” approach laid a solid foundation.
Then AI became cheap and accessible. New competitors were appearing every couple of weeks, and roughly 95% of the market still did not know who we were. A better product alone would not keep its lead long enough to matter.
The thesisWhen anyone can build the product, the moat moves to distribution and trust. We had to reach the customer first, and earn their belief before a competitor earned their attention.
Where the moat comes from
Mapped to Hamilton Helmer’s Seven Powers: the defensible advantages marketing can actually build.
- BrandingBeing the industry’s source of truth on AI, with transparent claims backed by field results.
- Switching costsCo-marketing, visualization and integrations that embed VRIFY in how clients raise capital and report results.
- Counter-positioningBuilt by geoscientists for geoscientists, against tools built by industry outsiders.
- Network economiesMore client data improves the models, and better models produce more discoveries to market.
Hamilton Helmer’s Seven Powers describes the few advantages that let a company keep outperforming its competitors over time. When an AI product can be approximated by a new startup in weeks, technology alone stops being one of them. The powers that remain are largely the ones marketing builds: a brand people trust, switching costs from being embedded in a customer’s work, and a position competitors can’t copy without abandoning their own.
Designing for trust
Darius Contractor’s Psych framework treats every step of a journey as adding or draining motivation. I applied it beyond the website to the whole buying experience. For AI software entering a traditional, skeptical industry, the scarce resource is not attention. It is trust.
- + trustFirst exposurePeer results and field proof before any pitch
- + trustScrutinyTransparent technical content invites the hard questions
- + trustDemoRun on their own data, not a canned dataset
- − trustProcurementProcess friction drains trust; white-glove support offsets it
- + trustOnboardingValue in ~2.5 months instead of ~4
- + trustAdvocacyCo-marketed results become the next account’s proof
A conceptual model of trust across the journey, not measured data.
Mining is a traditional industry, and AI vendors arrive carrying the skepticism earned by tools built by outsiders that didn’t work. That means the scarcest thing in the buying journey isn’t attention; it’s belief. Every interaction either builds that belief or spends it.
The chart is a model of that journey, not measured data. Proof from peers and transparent technical content build trust early. A demo on the buyer’s own data builds the most, because it replaces our claims with their results. Procurement is where trust is usually lost to friction, so that is where white-glove support was concentrated. Fast time to value and co-marketed results then turn a customer into the next account’s proof.
The multi-channel mesh
No single channel carries the buyer. Each one reinforces the others, then hands off to one of two conversion paths.
A single channel can’t carry a skeptical buyer from first exposure to signature. Instead, the channels were designed to reinforce one another: someone who saw a LinkedIn post meets us at a conference, reads a technical write-up, and lands on a page written for their exact role.
From there, accounts take one of two paths. Smaller accounts convert through inbound, with agents tagging, routing and following up automatically. The largest opportunities get white-glove treatment: executive briefings and demos built on their own data.
Product-led works until the product stops being rare. Build the distribution and the trust while you still have the lead, because you cannot buy either quickly once you need them.