Opportunities in AI Native Vertical SaaS
This article was originally published on Medium on Jan 9, 2024For years many VCs believed companies developing vertical SaaS solutions — products tailored to the unique requirements, processes, and workflows of specific sectors — could not generate venture scale returns. The markets appeared too small, especially relative to massive, cross-industry horizontal SaaS markets such as CRM, Marketing Automation, ERP, and HR. In the last decade, however, these concerns have been debunked, and we believe a new generation of AI-native Vertical SaaS solutions will continue this trend of enduring, valuable companies being built across many vertical markets.
Vertical SaaS companies and their founders have demonstrated they can drive success by capturing outsized shares of their markets, continually increasing ACVs by layering in new products and services, often including embedded finance offerings (or as our as our partner Jeff Bussgang notes, starting with finance and adding SaaS), and ultimately building billion-dollar companies in “niche” sectors. As of December 2023, there were 19 public vertical SaaS companies, 8 of which reported $1B+ in LTM revenue. Toast is a classic case study. Founded in 2012, the company started with a single product — an app that gave customers a way to link a credit card and start a tab at a restaurant. This has grown into a suite of 24 integrated products to help restaurateurs better manage, grow and finance their operations.
Source: CRV
From an investment perspective, vertical SaaS companies embody many highly attractive business attributes, including:
Massive market penetration potential. By addressing sector-specific problems with unique care and precision, the best vertical SaaS solutions become essential tools within their respective industries. This unlocks massive market penetration rates (e.g. 40%+), as compared to horizontal SaaS, where even great companies struggle to achieve 2%+.
Product sickness and low churn. Tools that easily and dramatically improve core workflows are incredibly sticky. While horizontal products can sometimes be necessary evils (does anyone actually love Salesforce?), we routinely hear from vertical SaaS founders that customers couldn’t operate without their solutions. Users of Owners, a Flybridge portfolio company that offers business-in-a-box tooling for home service professionals, have shared that the product is “essential” and even “transformative”. This customer love results in low churn, an increased willingness to buy additional products from the same vendor, and strong customer referral networks.
Capital efficiency. Vertical SaaS companies sell into a uniform customer base. Their operating systems and “hair on fire” problems are often the same, or very similar. This continuity of systems and needs translates into efficient GTM motions and customer management post-sale, leading to better operating margins. It can also help ignite network effects via integrations of other products and services widely used across a specific market.
Insert AI
To date, vertical SaaS products have primarily served as enablers of specific functions (e.g. payments, advertising, workflows etc.) Moving forward, AI-enabled vertical SaaS products will generate the outputs these systems previously enabled humans to perform. To contextualize this with an example, let’s take EliseAI, a Series C startup that automates conversations between tenants and multifamily property operators. Historically, property management companies have utilized workflow software to help leasing agents manage customer interactions. EliseAI uses property data to automate responses to tenant inquiries, fundamentally reducing (or eliminating) the need for human agents. Importantly, EliseAI and other vertical SaaS applications go beyond automation, improving information quality and the overall consumer experience.
This shift from business process management to output-oriented vertical SaaS has important implications for founders and investors alike. In a world where building software and AI applications has become increasingly commoditized, many of the “traditional” best-in-class vertical SaaS evaluation criteria are increasingly important — attacking massive markets, founders with deep domain expertise, unique distribution and GTM strategies, and clear product expansion opportunities. This commoditization has also introduced newer critical evaluation criteria, such as unique data access and analysis capabilities. My colleague Daniel provides a deep dive on this topic here.
Looking to the Future
The best AI-native vertical SaaS companies will be those that can access, grow, and train LLMs against unique datasets over time, leveraging the outputs to create straightforward and unequivocally valuable products for customers.
There is an evolving discussion in the venture and technology community more broadly about where true value will accrue in the AI-enabled vertical SaaS market — will it be with incumbents, often sitting on years of historical data, or with new entrants, who can build AI-natively from inception. We believe the answer is both, with new entrants having a leg up to seize unique opportunities in markets where:
First, the existing incumbents are not poised to implement AI intelligently. This could result from core company infrastructure (e.g. legacy system architectures, poorly organized and low-quality data, etc.) or team capabilities (e.g. inadequate technical talent).
Second, the need for software, in general, or for specific use cases, is new. Climate, broadly speaking, is an excellent example of this market archetype. Flybridge portfolio company Porosity uses AI to monitor and manage methane leak detections, an enterprise need that didn’t exist several years ago.
Third, customers are open to testing and buying new products because AI has the ability to be fundamentally transformative. There are a host of vertical SaaS markets that remained relatively unpenetrated by technology for years, such as law. Now, the value of augmenting your legal team with AI is too clear to ignore, as we’ve seen with our portfolio company Noetica.
Fourth, target customers don’t have deep internal application development talents. While most technology companies can leverage off-the-shelf applications to build AI assistants for their needs, companies in non-technical industries (e.g. real estate, construction, industrials and well as fashion, e-commerce, etc.) may be required to “buy” vs “build”, and consider new software offerings when they’ve been historically reluctant. Flybridge portfolio company Syrup, focused on AI-enabled inventory management, has benefited from this dynamic selling to e-commerce retailers.
Fifth, datasets are newly available and can be intelligently deciphered and leveraged using AI. This could be the result of new data collection mechanisms (e.g. sensors, cameras, satellites, etc.) or consumers and enterprises proactively sharing data they’ve previously kept confidential. Deco portfolio company Hansa, for example, enables SMBs to share financial and operating data to find and access new fintech products and services tailored to their needs.
We’re excited to back this new generation of vertical SaaS AI companies, where we believe $1.2T+ of revenue is up for grabs in the next decade. Please reach out if you’re building in the space — julia@flybridge.com.
