What we learned about early-stage B2B Saas companies

3 min readJan 3, 2023


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Sales is hard. We’ve been doing it (across multiple pivots) since we launched out of Antler in 2021. Our philosophy has been to sell first, build later, so that we don’t waste time building something nobody will buy.

Over this time, there were a few things about sales that really bugged us, in particular the prospecting process (finding potential fit customers, and reaching out to them).

Our challenges with prospecting

  • 😡 There are too many competing best practices out there for running a sales process, as early stage founders we didn’t know what approach to prioritise
  • 😡 Existing tools to discover and scrape our target audience (Sales Navigator, Apollo) contain a lot of noise in their database, that requires lots of manual effor t to clean (precious time that scales with the size of the list)
  • 😡 New and advanced solutions on the market such as Ocean.io are out of reach for the majority of SMBs in the B2B space because of high costs and annualised pricing

These problems bugged us so much that we decided to build a start-up to fix them. But we knew our experiences would differ from the real world, and we really wanted to understand other people’s experiences in the prospecting space — do they align to ours? What challenges have they faced when they prospect? So we decided to talk to over 70 people across 3 core personas that we assumed would feel the pain of prospecting most acutely — Early stage founders at B2B Saas start-ups (people like ourselves), Lead generation agencies (who sell prospecting as a service), and sales people at B2B Saas start-ups (who need to execute on their servicable markets). Over the course of 3 weeks we built up a picture of a typical sales process and challenges faced for each of these personas.

What we learned about prospecting at early stage B2B start-ups


  • We spoke to 18 founders
  • 90% started their business with considerable domain expertise
  • 90% rely on their network and network effects as a core component of their sales strategy, of this 70% is inbound
  • 50% were hiring VAs or interns to create and manage lists of prospects
  • 30% were manually desk researching to find prospective customers
  • 10% were purchasing lists of prospective customers


  • Founders were struggling to scale their sales process away from personal networks, towards repetable, reliable channels of outbound (such as email or LinkedIn). Even though most of you have started experimenting with this process, there is hesitancy to lose the “relationship building” aspect of 1–2–1 sales — founders want to more volume without sacrificing the personal relationships they develop with propsective and early customers
  • Founders (or people they’ve hired such as VAs/interns) are spending 50% of their prospecting time manually researching accounts and leads to pre-qualify them prior to reaching out, this involves looking for “signals” on an account or prospect level that would indicate that they may suffer from the problem their start-up trying to solve — something we call “proxy pain”, e.g. do they have integrations? How ubiquitous are those integrations? Is something missing?
  • Even though they’ve found a market sweet spot, Founders are experimenting with multiple use cases and ICPs, however because of the number of variables involved (each ICP will require its own messaging, channel(s), product variation) they’re unable to do this effectively or quickly, affecting their ability to clearly demonstrate growth potential
  • Founders have a desire to capture more information from their sales outreach so they can better optimise their sales approach, however they are not currently capturing metrics from their inbound or outbound as it requires considerable time and effort to track multiple channels and variables — they are also unwilling to pay for solutions as they require extensive set-up processes

What is Noki?

Noki is an AI sales qualification tool — we turn your newly prospected accounts into qualified opportunities, using machine learning to automatically filter for companies that match your qualification criteria

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