Hello my people! First-time GTM leader at an early-stage startup. Selling a SaaS product B2B to engineering teams. And I don't know what I'm doing! Would love to chat with anyone similarly situated to share learnings and best-practices, especially for outbound. Most of what I find that's publicly available is a sales-pitch in disguise and/or is severely outdated for selling in the age of AI. Help! Feel free to reply below or shoot me a DM. Thank you kindly!
Michael, very happy to help. I have led growth and GTM across early-stage startups, particularly B2B SaaS, and now work with founders and first-time GTM leaders through We Scale Startups. I have seen the same problem, a lot of outbound advice is either outdated, overly generic, or written to sell you another tool. AI has also changed both the opportunities and the level of noise buyers are dealing with. Happy to share what I have learned, including what is working, what is not, and how I would approach building an outbound system from scratch. Ideally, we can keep as much of the conversation public as possible so others in the community can learn and contribute too. Feel free to share a little more about the product, target customer, deal size and what you have tried so far.
Hey Daniel J.! Thanks so much - I love the idea of working through this in public. Product: FriskAI is runtime intelligence for AI agents. We help developers optimize their agents by analyzing behavioral data and surfacing findings. Target Customer: Enterprises building AI agents. Economic buyer is an engineering leader. Deal Size: Currently in design-partner phase, so deal size is bespoke. Tried So Far:
Once we had an MVP, we engaged an agency to do a 3-month outbound email and linkedin campaign. It was not successful. My strong suspicion was that the targeting, positioning and messaging was all broken. In retrospect, the messaging in particular was very AI-coded.
Since then, I've been running manual outbound campaigns through SalesNav for about four months, where I'm getting a 20% acceptance rate, a 4% positive response rate and a 2.5% meeting rate. Importantly, all the messaging here is curiosity led (I want your perspective) rather than sales led. I'm changing that this week.
When I book meetings, 10% are qualified, and about half of those move forward to a demo and exploring a fit as a design partner.
I need help with everything, but of particular interest is whether to return to some email outbound and the best way to do it.
Also curious to learn the application of AI in outbound. I have mostly used CHATGPT to curate my target prospect list, then used Google Search to look fo lookalike prospects in my target industry, sector and subsector, then of course used excel to keep a record of contact details, location and estimated value of the client_ prospect qualification and grading before outreach, used phone recording for my outreach calls, used CHATGPT for my telescripts and email sequences
Thanks, Michael. This is actually a much better starting point than it might feel. Your top-of-funnel numbers are not terrible. A 20% acceptance rate and 2.5% meeting rate shows you can generate interest. The bigger issue is that only 10% of those meetings are qualified. My guess is that the curiosity-led messaging is doing exactly what it is supposed to do. It gets people interested enough to chat, but not necessarily because they have an urgent problem or any intention to buy. Before scaling email again, I would focus on a few things. First, review the last 20 to 30 meetings and write down exactly why each person was or was not qualified. You will probably start to see patterns around whether the agent is actually in production, the volume of usage, the size of the engineering team, who owns the problem and how painful it is. Second, I would narrow “enterprises building AI agents” quite significantly. For example, companies with customer-facing agents already in production, enough usage for behavioural issues to matter and an engineering leader who is accountable for reliability or performance. Third, I would move away from asking for general feedback and towards testing a clear problem hypothesis. Something like: “We are speaking to engineering teams running AI agents in production who can see individual traces, but still struggle to understand recurring behavioural patterns across thousands of interactions. Is that something your team has experienced, or do you already have it covered?” I would definitely test email again, but I would not jump straight into another large automated campaign. I would start with 50 to 100 carefully selected accounts, identify two or three relevant people within each and test a few different problem hypotheses. I would also judge it on qualified conversations and confirmed problems, not just opens, replies and meetings. The fact that you are still in the design-partner phase is also useful. You do not need to pretend the offer is fully standardised. You can be direct that you are looking for a small number of teams with the right environment and problem to help shape the product. Happy to work through this publicly. Share one of the messages you are currently using, plus the most common reasons people turn out not to be qualified, and we can pull it apart.
Thanks so much, Daniel J.. Super helpful! On LinkedIn, my best-performing message for the connection request is "Hi XXX, I've been thinking a lot about runtime intelligence for AI agents. Would love to connect." And once the connection is made, I go with "Hi XXX, I would love to get your POV on the value of runtime intelligence for agents and what I'm building to provide it. Might you have 15 mins this week?" Disqualifications are split 50/50 between: (1) the lead not being the decision-maker (most of the times their boss is, sometimes they aren't on the team that is building agents) and (2) the company not feeling the pain we solve for. For the latter category, that's split evenly between (a) the company is very mature in their agentic development, and has been building internal visibility and control measures for quite some time; (b) the company is very early in building agents, so the problem is not yet ripe; and (c) the company is in our sweet-spot, but is using a traditional tracing tool and they don't believe our runtime intelligence platform is incrementally valuable enough to pursue further. The traction we've have so far is with a few companies that sit in between (b) and (c), where they have been building agents for several months, the intelligence gap is growing, and they don't have a solution yet. But that TAM is too small, and it needs to expand. But that seems more of a product challenge than a messaging issue.
Hi Michael M., 20+ Senior Sales Trainer here. Happy to chat if it would help. I've spent a lot of time working with B2B SaaS teams on sales strategy, training, and go-to-market challenges, including selling to technical audiences. No pitch, just happy to share what's worked, what hasn't, and maybe help you avoid a few mistakes I've seen along the way. Feel free to send me a DM.
