The Data Advantage: How Skillit Turned 2.1M Data Points into a Competitive Moat

Discover how Skillit built a competitive advantage in construction hiring by collecting 2.1M proprietary data points through innovative worker assessments and engagement strategies.

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The Data Advantage: How Skillit Turned 2.1M Data Points into a Competitive Moat

The Data Advantage: How Skillit Turned 2.1M Data Points into a Competitive Moat

In traditional industries, data can be the difference between a nice-to-have tool and essential infrastructure. In a recent episode of Category Visionaries, Skillit founder Fraser Patterson revealed how they’ve built a defensive moat around their business by collecting and leveraging millions of data points about construction workers’ skills and experience.

Building a Data Collection Engine

Traditional construction hiring platforms offer what Fraser calls “a kind of online business card.” Skillit took a radically different approach by creating “extremely data rich, 360 degree profiles” of workers. This has resulted in “about 2.1 million proprietary skills, compensation and experience data points that we’ve managed to, that our workers have kind of directly into our platform.”

The key innovation was using knowledge as a proxy for skill. “As a former carpenter myself, I learned to ask really quick questions that were like, using knowledge as a leading indicator of skill,” Fraser explains. Unlike theoretical knowledge, construction knowledge typically indicates real experience: “That’s unlikely that you’re staying up at night reading nailing patterns or something before you go to bed. That’s reliable indicator of your level of skill.”

Turning Data into Product Features

This rich data set enables features that would be impossible with basic profile information. “They can filter job boards to get the exact workers they want,” Fraser notes, describing how employers can now search based on detailed skill profiles rather than just job titles or years of experience.

The platform’s assessment system was born from this data-first approach. “The insight really was when I would be having calls with those carpenters,” Fraser shares. “I learned to ask really quick questions that were like, using knowledge as a leading indicator of skill.” This insight was systematized into “digital craft assessments that could assess a worker’s skill using knowledge as a leading indicator.”

Creating Network Effects Through Data

Skillit’s data moat grows stronger with each user. The platform enables “recruiters to collaborate with one another and share with their subcontractors who have the same recruiting challenges,” creating what Fraser calls “network effects baked into it.”

This collaboration generates even more data, as each interaction adds to their understanding of worker skills, employer needs, and market dynamics. The result is a flywheel effect where better data leads to better matches, which attracts more users, who generate more data.

Leveraging Data for Future Growth

The data advantage positions Skillit for ambitious expansion. “The big vision here is, can we use that proprietary data and that digital infrastructure to help train and upskill the talent network… to become increasingly valuable to employers?” Fraser explains.

This vision is particularly powerful given current market conditions. With the need to “build 7 million housing units in the US” and tackle infrastructure and climate challenges, having detailed data about the construction workforce becomes increasingly valuable.

Measuring the Data Advantage

The effectiveness of this data-driven approach is evident in Skillit’s growth metrics. Their platform achieved 540% year-over-year customer growth, with worker adoption growing 1000% year-over-year. Most importantly, their core engagement metric of connections between customers and workers “grew 400%” with “three quarters of those occurred in Q4.”

For B2B founders, Skillit’s approach to data collection offers valuable lessons. Rather than treating data as a byproduct of their platform, they’ve made it central to their value proposition. By understanding what data actually indicates skill in their industry, they’ve built a assessment system that generates valuable data with every interaction. This creates a virtuous cycle where more data leads to better matches, more users, and even more data – exactly the kind of defensive moat that creates lasting competitive advantages.

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