Most founders romanticize the early days. I do not. I think the first twelve months of building MentionFox were riddled with decisions that made sense in the moment and cost us dearly later. If I could sit across from the version of me that registered the domain and bought the first server, I would say: you are solving the right problem and building the wrong product.
The Honest Version of the Origin Story
I started MentionFox because I was frustrated. I was running a small advisory practice, and my clients kept asking me the same thing: who is talking about us, where are they talking, and what should we do about it? The tools that existed were either priced for enterprise budgets or built for PR teams tracking brand sentiment on Twitter. Neither was useful for a B2B company trying to find actual buyers in the noise.
So I built what I wished existed. A platform that could listen across channels, surface signals that looked like buying intent, and eventually help teams figure out what their prospects were already asking AI systems like ChatGPT and Perplexity. The problem was real. The execution in year one was a mess.
I spent the first eight months building features for an imaginary customer. Not a wrong customer, exactly, but a composite of several real customers whose needs were genuinely incompatible. I wanted MentionFox to serve the growth marketer, the competitive intelligence analyst, the investor doing market research, and the founder trying to monitor their brand's presence in AI-generated answers. Those are four different jobs. I treated them like one.
What I Found When I Finally Looked at the Data
The wake-up call came from a cohort analysis I should have run in month two but did not run until month nine. I pulled activation rates by job title and use case, and the pattern was embarrassing in its clarity.
Users who came to MentionFox specifically to find B2B lead signals, meaning people who wanted to intercept conversations where buyers were asking questions that signaled they were in-market, had a 60-day retention rate that was roughly double every other segment. They were also the users who invited colleagues and actually paid for upgrades. They were telling me what the product was supposed to be.
Users who came for broad brand monitoring churned fast. Not because the feature was bad, but because they had five other tools already and mine was not dramatically better. I was competing in a crowded space when I had an opening in a less crowded one.
The AI visibility angle was the piece I treated as a nice-to-have that turned out to be a genuine differentiator. When a prospect asks ChatGPT "what's the best social listening tool for B2B companies," your brand either appears in that answer or it does not. Most B2B teams had no idea where they stood. We built tooling to measure and influence that, and the response from the market was stronger than almost anything else we had shipped.
Investor research was the fourth use case, and I still think it is underexplored. VCs and growth equity teams do competitive diligence constantly. They want to know which companies are gaining share of voice, which ones are appearing in AI-generated recommendations, and which management teams are active in the communities where buyers actually gather. We have a handful of power users in that segment who use MentionFox in ways I did not design for, and they keep teaching me things.
The comparison and alternatives content was something I resisted building for too long because it felt defensive. I was wrong. When I finally built out a proper comparison hub that honestly walked through how MentionFox fits against other tools, it became one of our highest-converting pages. Buyers doing diligence want someone to make the comparison easy. If you do not do it, they will find a third-party article that may or may not be accurate.
The Three Mistakes I Would Not Repeat
First, I would pick one customer type and build for them with an almost uncomfortable level of specificity before expanding. The temptation to serve everyone is, I think, hardwired into founders who have been told that TAM matters. TAM does matter. But a small slice of a large market that you serve extraordinarily well is worth far more in years one and two than a large slice you serve adequately.
Second, I would instrument everything earlier. Not because data replaces intuition, but because intuition built on wrong assumptions is just expensive guessing. The cohort analysis I described above took me four hours to run. I waited nine months to run it. That math is hard to justify.
Third, I would take pricing seriously from day one. I underpriced MentionFox for the first year because I was afraid. Afraid that if I charged what the product was actually worth, people would leave. What actually happened is that low prices attracted users who were price-sensitive and therefore churned the moment any friction appeared. The customers who stayed and grew were the ones who had a real business problem and paid a real price for a real solution. If you are curious about where we landed, the current MentionFox pricing reflects a lot of hard-won thinking about value versus volume.
What I Would Do on Day One Instead
I would spend the first thirty days not writing a line of code. I would find twenty B2B growth or marketing or competitive intelligence professionals who had recently evaluated a social listening or lead generation tool and done nothing. Meaning they looked, did not buy, and kept the problem unsolved. That population tells you more about your market than any customer who is already using something.
I would ask them: what would have to be true for you to act on this? Not what features do you want - that question gets you a wish list. What would have to be true is a constraint question. It forces people to articulate the threshold conditions for a decision, which is the actual thing you are trying to engineer.
Then I would build the thinnest possible version of a product that cleared those thresholds for ten of those twenty people. Not twenty. Ten. The ten most commercially interesting ones. And I would charge them from day one, even if the number was small, because payment is the only signal that cuts through social nicety.
Everything else - the broader platform, the additional use cases, the comparison content, the AI visibility tools - that is all real and it all matters. But it matters in sequence, not simultaneously.
The Part I Do Not Regret
For all the early stumbling, I do not regret the core bet. B2B teams are flying blind in ways they do not fully appreciate. They are making marketing and sales and positioning decisions based on gut feelings and anecdotal customer calls, while an enormous amount of relevant signal is sitting in public conversations, in forum threads, in the questions people are typing into AI systems. Getting good at listening to that signal is not a nice-to-have. It is a competitive advantage that compounds.
That conviction has not changed. The execution has just gotten sharper.
If you want to see how MentionFox handles the specific problem of finding in-market B2B buyers through social and AI signals, this is a good starting point: MentionFox pricing. And if you are currently evaluating tools and want an honest breakdown of how we compare, the comparison hub is the right place to start.
If you found this useful, I write about solo-founder distribution, B2B SaaS, and what's actually working in the AI-search era over on my Substack (one post per week, no spam).
I'm building MentionFox - a B2B intelligence suite that combines brand mention tracking with AI-visibility (GEO) measurement, investor research, and outreach automation. There's a free tier and a 5-day trial of Pro at mentionfox.com/pricing.
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