ICONIQ's Tengbo Li: How Verticalized Data and AI/ML Will Drive a New Wave of SaaS
Thesis BriefsTengbo LiICONIQ CapitalAI/MLVertical SaaSHorizontal Data SolutionsVerticalized Data SolutionsVerticalized Data

Terra Nova Insights Team,

ICONIQ's Tengbo Li: How Verticalized Data and AI/ML Will Drive a New Wave of SaaS

Contents


ABSTRACT

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Vertical SaaS platforms target niche problems, which often aren't solvable through industry-agnostic software. From helping retailers predict stock-outs to improving manufacturing processes for auto companies, organizations across industries often require tailored solutions and data offerings to fit their needs. Tengbo Li, partner at ICONIQ Growth, dives into how vertical SaaS companies can evolve to leverage modern, cloud-based data infrastructure and AI/ML to enhance the value-prop of their solutions, and the massive opportunity for companies operating in this category.

KEY POINTS FROM TENGBO LI's POV

Why will vertical SaaS evolve to include data- and AI/ML-driven features?

  • Many organizations lack the resources to leverage data in meaningful ways, though it is essential to the growth of their businesses. “Data-driven decisionmaking is increasingly the norm at organizations large and small, yet many organizations do not have the means to access the right data or properly analyze it,” says Li. This is particularly true of industries that have experienced slower tech adoption, he says, where business-critical data can be stuck in spreadsheets and legacy ERP systems, unused.
  • Horizontal data solutions are often too inflexible and resource-intensive to solve bespoke pain points. “Even when this data is accessible, organizations may have highly specific data types and analytical needs that horizontal data solutions may not seamlessly support. For example, the same applications that easily analyze loss ratios in consumer lending may be less appropriate for claims data in healthcare or invoice data in shipping,” says Li.
  • “As the need for vertical solutions is growing more acute, the tools available to build verticalized data and AI/ML offerings have become widespread,” he says. Vertical SaaS companies can develop data offerings on top of cloud data warehouses and open-source databases. They can also use third-party frameworks that process any data type — analytical or streaming, structured or unstructured. “At the visualization layer, the rise of embedded analytics tooling will enable vertical SaaS companies to deliver insights without requiring their end-customers to build queries or write code.”

What are the applications or use cases that might be attached to this category?

  • Verticalized data platforms can power numerous data-driven business processes across multiple industries. As vertical SaaS companies tighten the feedback loop between data insights and workflow automation, end-customers can move with more agility around both revenue-generating and back office activities.
  • Layered, vertical data offerings that add on to and improve existing systems can bolster business operations. “There are likely plenty of opportunities for entrepreneurs to establish a foothold in the market by layering their data offering onto existing solutions, whether CRMs or ERPs or other tools, instead of trying to displace them entirely at the outset,” says Li. Over time, going to market as a data- or AI/ML-first vertical SaaS platform may be the most effective way for newer companies to challenge incumbents in a particular vertical. In emerging, high-growth verticals, such as in renewable energy and aerospace, the modern vertical SaaS incumbents that will emerge in 5-10 years’ time will likely be data- and AI/ML-first offerings, he adds.

Going to market as a data- or AI/ML-first vertical SaaS platform may be the most effective way for newer companies to challenge incumbents in a particular vertical.

Tengbo Li~quoteblock

What are some of the potential roadblocks?

  • These are not yet ‘must-have’ solutions for several spaces. “Organizations in some verticals may not yet need a heavy-duty data or analytics solution; ‘first-generation’ workflow automation tools may suffice for the time being,” says Li.
  • Bottom-up, holistic platforms face significant friction around legacy deployments. “Building a wholly new vertical SaaS company with a data or AI/ML offering may be challenging given the stickiness of incumbent workflow solutions,” he says.

IN THE INVESTORS OWN WORDS

saidbyblock~ via email correspondence
Tengbo Li

SaaS has replaced on-premise solutions and spreadsheets in life sciences, construction, energy, logistics, and other large verticals. Much has been written about the compelling opportunity for verticalized fintech to drive future iterations of these vertical SaaS companies.

In parallel, we believe that there exists an opportunity for vertical SaaS companies to offer novel, verticalized data and AI/ML platforms to help end-customers make better decisions by leveraging their own data, customer and supplier data, and industry datasets. Verticalized data platforms can be tailored to help retailers predict stock-outs, medical device companies target prospective customers, solar installers optimize panel installations, and automotive companies improve their manufacturing processes.

The advancements we are seeing in modern data infrastructure have caused equity value to accrue first to horizontal data platforms, and we are likely to see massive spillover into vertical applications soon as well.

Verticalized data and AI/ML offerings are not likely to be just another add-on. They could fundamentally change the value proposition, and corresponding ARPUs, for vertical SaaS.

Tengbo Li~quoteblock


MORE Q&A

Q: What is something that other market participants or observers often misunderstand about this category?

A: That verticalized data and AI/ML offerings are not likely to be just another add-on offering. They could fundamentally change the value proposition, and corresponding ARPUs, for vertical SaaS companies.


WHAT ELSE TO WATCH FOR

  • Improved data-driven insights via vertical SaaS companies could expand budgeting allocations toward data investments. “There are longstanding, very successful companies in verticals like financial services and healthcare that charge large amounts for generic industry data sets. The budgets for data can be enormous, and we think that SaaS companies combining workflow automation with tailored, intelligent insights for real-time decision-making can expand these budgets even further,” says Li.

The 2022 EVC List honors the top 50 rising starts in venture capital. Terra Nova’s Thesis Brief series showcases each investor’s insights and category expertise.

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