Earlier this year, we surveyed a number of companies with the intent of establishing a useful set of benchmarks against which vertical-specific software startups could measure their own performance, to see how they stack up against their peers, and to have a better sense of how they’re being evaluated by potential investors.
Up front, let us say that we didn’t get the volume of responses we were hoping for – even extending our outreach through our partners on this project. Based on feedback we received throughout the process, we attribute this to a number of different factors, but some of the more interesting ones include a lot of confusion around what actually constitutes ‘vertical software’ products and companies.
Based on our experience with this survey, we’re adapting our approach in future along two different trajectories: First, we want to invite ongoing discussion and spearhead a collaborative data-gathering effort with the community of those building these enterprises to establish a better understanding of the dynamics at play for all involved. Rather than trying to deliver a high-resolution, single-moment-in-time snapshot on an annual basis, we think a living repository and ongoing analysis approach will prove more fruitful. To that end, we’re going to reconfigure our microsite at https://verticalsoftwarebenchmarks.com as an always-on resource (stay tuned for more on that front).
Second, we did learn some interesting things about current trends and patterns among vertical software startups, including:
1. AI plays a ‘crucial’ role in the products of 75% of the founders who made up our respondents.
2. 76% of respondents said they had either direct or indirect experience in the vertical industry their products serve.
3. Vertical software companies with under $10 million in ARR demonstrate more efficient CAC payback periods relative to their peers in more generalized horizontal SaaS.
Let’s look at each of these in a little more detail, along with what insights we think we can derive from each.
AI is already almost universally important
Based on the responses we received, 55% of early-stage vertical software startups already have AI incorporated in their product in a meaningful way. 42% have it on the roadmap, and only 3% do not currently plan to incorporate AI.
It seems quite clear: the next generation of vertical software businesses will be characterized by AI. But not all of them are truly AI-native. Why? A few potential reasons come to mind:
ARR traction doesn't correlate with age, so some of the companies in our survey likely built on a tech stack predating this generation of AI.
For some industries/use cases, simple and incremental (vs. transformational) analog to digital transformation is necessary first before AI can be meaningfully applied. This is both a technical and a GTM hindrance.Developing and integrating AI requires specialized knowledge in data science, machine learning, and AI technologies. Not all vertical software companies have access to such expertise.
AI development can be expensive, involving significant investment in research, development, and infrastructure. Smaller or resource-constrained vertical software companies might not have the budget.
If the existing customer base does not demand AI-driven features or does not see the value in them, businesses may choose not to invest in AI.
Domain experience remains key
64% of early-stage vertical software startups were started by founders with direct experience – and 21% were started by founders with indirect experience – in the vertical they're serving. Only 16% have no prior experience working in their vertical.
Why is it so high? Many vertical software founders are inspired to start their businesses because of a personal pain point, and having experience in the industry has several advantages:
Network advantages for product discovery/development and also early pipeline generation, along with a more intuitive understanding of the wants/needs of the user, which can provide a quicker path to product-market fit.
A natural ability to ‘talk the talk’ and a deep understanding of the holistic industry forces/personas/other dynamics at play allows founders to truly immerse themselves (i.e., as an insider) and build trust with the community.
When they're deeply involved in the centers of influence driving the community (e.g., trade shows, facebook groups, etc.), founders can gain significant GTM advantages, especially in viral word-of-mouth communities.
With the advancement of AI, however, generalist founders might soon be able to ‘pass’ as specialist founders. AI can provide generalist founders with comprehensive insights and data about specific industries. Advanced analytics and machine learning algorithms can sift through vast amounts of industry data, trends, and patterns, helping generalists gain specialist-level knowledge. That being said there are a few caveats to consider:
While AI can provide a wealth of information and simulated expertise, it may not fully replace the value of genuine industry experience. Specialist founders often bring years of hands-on experience, networks, and intuition that AI cannot easily replicate.
Building trust and credibility in a specialized field often requires personal relationships and a human touch. Customers and partners might still prefer dealing with founders who have a proven track record in the industry.
Some industry-specific challenges might require nuanced understanding and creative problem-solving that goes beyond data and algorithms. Specialists with deep industry knowledge may still have an edge in navigating such complexities.
CAC payback periods are shorter for early vertical companies vs. horizontal
Much of the reason for this goes back to the founder-market fit points mentioned above, along with the fact that vertical software companies are selling to smaller customers w/ shorter sales cycles and lower ACVs. Still, there are a few additional contributing factors to consider:
AI developments are enabling vertical software businesses to be built with enterprise customers in mind, as AI can be applied most effectively in this context.
Some industries and specific use cases will be best served by vertical-specific solutions rather than generic horizontal LLMs, creating opportunities for startups to target these niches.
Vertical software companies can potentially achieve shorter CAC payback periods and higher sales efficiency due to factors like focused marketing, higher pricing for specialized offerings, better customer retention, and strong network effects within niche markets.
Looking ahead, we anticipate a dynamic and evolving landscape for vertical software:
The integration of AI will likely become even more sophisticated, potentially transforming entire industry workflows and processes.
We may see a hybrid model emerge, where generalist founders leverage AI to compete effectively with industry insiders, leading to a broader range of perspectives and solutions in vertical markets.
The efficiency demonstrated by early-stage vertical software companies could drive increased specialization in the SaaS market, with more startups focusing on solving specific industry challenges.
As we transition to a more collaborative, ongoing data-gathering approach, we invite the vertical software community to continue sharing insights and experiences. This collective effort will help us all better understand the unique dynamics of vertical software and foster innovation in this critical sector.
By maintaining an always-on resource at verticalsoftwarebenchmarks.com, we aim to provide a living repository of data and analysis that will benefit founders, investors, and industry observers alike. We look forward to sharing how you can contribute to this evolving knowledge base as we work together to shape the future of vertical software.
