B2B GTM · Demand Generation · Marketing Operations
I’m Ron Kagan, a hands-on B2B marketing consultant who combines demand generation, automation, data, persuasion and AI to find buyers, create conversations and grow revenue. Lately that includes the newest surface: making sure AI answers know you exist.
What I do
I work where marketing strategy meets technical execution: building the data, automation, messaging and operating systems that help B2B teams identify demand and turn it into pipeline.
Use first-party and real-time data to identify the right buyers instead of relying on stale lists and broad targeting.
Connect APIs, automation and AI so good GTM work scales without becoming generic.
Pair clean targeting with strong persuasion, then measure conversations and revenue, not vanity metrics.
Experience
The loop below shows organizations connected to Ron's work, among them OnPay, Code Climate, Pared and Admiral Insurance Group.
Selected work
Prospecting system
I built workflows that uncover qualified buyers and source prospect information programmatically, using live signals instead of depending on third-party data brokers. The thinking behind it lives in my demand generation resources.
Result: contact lists that performed 3× better than purchased alternatives.
Pipeline generation
Clean data and persuasive outreach turned targeted prospecting into conversations with decision makers at scale. Systems like this are what marketing operations consulting builds.
One cohort: 14% conversion across 350+ prospects, producing 53 scheduled meetings.
GTM engineering
I use code and automation when manual marketing operations become the bottleneck, building repeatable systems that give sales teams better inputs.
Another build
A hands-on example of turning messy external data into usable GTM intelligence. The interactive walkthrough below shows the build: collecting competitor data, structuring it, and delivering it to the team automatically.
The answer layer
When buyers ask ChatGPT, Claude, Gemini or Google's AI Overviews for a recommendation, the engines cite a handful of sources and skip everyone else. I publish open-source tools that measure whether AI answers name your brand, and I wrote up how answer engine optimization actually works. The shift shows up in spending data too: about half of AI-adopting businesses now pay both OpenAI and Anthropic.
What people say
Let’s build
I’m interested in problems where strategy, technology and distribution have to work together.