Technologist's brain.Anthropologist's heart.Marketer's results.

This came up when I was launching VMware's Kubernetes product line. "Cloud Administrator" wasn't a real job title at the time — people called themselves anything from "Datacenter Admin" to, in one memorable case, "Cloud Monkey." You can't do traditional audience targeting when your audience doesn't exist as a defined category yet.

The answer is to stop targeting by who people are and start targeting by what they're doing. We looked at the sites and resources people researched before making Kubernetes purchasing decisions and bought advertising on those specific articles. We found our buyers by their behavior, not their business cards. It worked.

If your buyers exist but don't have a name yet, find them where their curiosity already lives.

The first question I always ask is whether it's actually a commodity — or whether it's just being described like one. I worked with a commercial aquatics company that wanted to launch a recurring revenue service. When they described it to me, they called it "pool cleaning." That phrase was going to be a problem.

After talking to customers and looking at market data, I realized they were leading with the most replaceable part of their offering — the part any 18-year-old with a pool skimmer could replicate. What customers would actually pay serious money for was preventative maintenance on complex aquatic systems: catching small problems before they became expensive ones.

We rebuilt the messaging from scratch. The service hit $7 million ARR in nine months.

The product didn't change. What changed was understanding which part of the product actually mattered to buyers.

I start by talking to customers who've already bought — and specifically asking them what they think they bought. The gap between how a company describes its product and how customers describe the value they received is almost always where the positioning problem lives.

I've found this to be true across industries. With a new infrastructure maintenance service, the company was describing it as "pool cleaning." The customers who loved it were describing it as "never having to deal with an expensive surprise again." Those are completely different products with different price tolerances and different buyers. Once you know how your best customers describe the value, you build your messaging from their words, not yours.

The first thing I do is separate "stuck because of messaging" from "stuck because of something in the sales process." They look the same from the outside: pipeline not moving, but the fix is completely different.

I worked with a client whose pipeline was stuck at a very specific dollar threshold. The messaging was solid; I was confident in that from pilot customer conversations. So, I ran an AI-assisted analysis on the anonymized pipeline data and found that everything above a certain deal size stopped dead at the same point in the sales cycle. Digging in with the sales team, we figured out why: many customers were planning to flip their buildings in five years, so large maintenance investments were hard to justify.

The solution wasn't a new campaign. It was two sales tools: a tiered proposal framework and a "cost of waiting" asset that quantified what happens when small problems go uncaught (up to 42x more expensive, as it turned out). Those two tools moved 35% of stuck opportunities forward.

If your pipeline is stuck, the answer is often in your CRM, not your ad spend.

Get them in a room with data. Not opinions — data. Sales and marketing tend to disagree most loudly when they're both operating on gut feel. When you show a sales team that a specific type of customer converts at 3x the rate of the ones they've been chasing, the conversation changes fast.

At VMware, I needed buy-in from 12 separate business units to overhaul the SaaS trials experience. Each team had built its own process and wasn't excited about giving up control. I didn't try to convince anyone with vision or strategy. I built a business case showing the conversion rate improvements we should expect, created a shared dashboard that tracked every team's performance through the same gates, and let the numbers do the talking. Deal velocity improved by 39% and conversion rates by 22%.

Alignment follows shared accountability. Give both teams visibility into the same metrics and they'll find their way to the same priorities.

Working in this industry taught me something that's easy to miss from the outside: regulated industries often have the most urgent need for cloud infrastructure products precisely because of their constraints. They can't easily refactor legacy applications to run in the cloud, but also can't support growing infrastructure needs internally — which means they have real, expensive problems that many cloud products solve. But the marketing rarely talks to them directly.

When I was running GTM for VMware's Cloud Platform, the team was marketing to multi-cloud customers because that was the product's natural fit. Reasonable assumption. The problem was that multi-cloud customers represented about 8% of the addressable market at the time.

A win-loss analysis and a lot of sales conversations revealed that healthcare, financial services, and government customers had just as urgent a need — for compliance reasons, not just preference. We pivoted the GTM to focus on regulated industries. Lead count went from 4,000 to 23,000 in a year.

If you're selling cloud or infrastructure software, regulated industries are worth a very close look.

I worked for a long period in EdTech marketing during the time when schools were first figuring out how to put technology in classrooms. Not the refinement phase. The "we have no idea what we're doing" phase, which is honestly where the most interesting marketing problems live.

I was doing product marketing for Dell's laptop division in the K-12 space during that period. What I learned: education buyers make decisions on a combination of budget cycles, IT constraints, student outcomes data, and what neighboring districts are already doing. Features matter, but fit matters more — and "fit" in K-12 is a much more specific thing than most tech companies assume.

After a lot of customer and sales conversations, I realized we were losing deals because we were missing a specific feature set. I built the business case for Dell's first K-12-focused laptop, convinced sales to take quota on a product that didn't exist yet, and got engineering to design it. It grew to a $100 million per year product line that's still in Dell's lineup today.

As CMO of NextEdge, I built and launched the marketing program for a new clean energy line of business from the ground up — brand identity, content strategy, social media, and an event series, all in an industry where relationships and reputation drive decisions and traditional demand generation has little precedent.

The event series turned out to be the most effective investment we made. The key was building a room where clean energy decision-makers actually wanted to show up, rather than a room designed to sell to them. We focused on content and conversations they couldn't get elsewhere. The ROI on that series was 227x.

The clean energy space rewards genuine thought leadership and community-building in ways that a lot of other B2B markets don't. That's either a challenge or a competitive advantage depending on how you approach it. We chose to treat it as an advantage.

You have to be genuinely curious about both sides. The mistake most technical product marketers make is stopping at the translation — "feature X means benefit Y." That gets you partway there, but it's not enough. You also have to understand the specific pain the buyer is already aware of and connect your product to that pain in language they'd actually use themselves.

When I joined Dell's desktop virtualization effort, engineers were explaining these solutions in engineering language to buyers who needed business language. I worked with the engineers to break down the functionality into terms I could actually understand — which meant the terms my customers could understand — and then found the industries where those capabilities solved expensive, specific problems. I created a simple whiteboarding strategy to walk customers through solutions, that adjusted to many, complex variations of the technology as it evolved.

We went from technical gobbledegook to clear, benefit-based messaging with real customer relevance. The business grew from customer #1 to $1 billion in four years.

Technical accuracy and customer relevance aren't opposites. You need both, and neither one alone is enough.