Data Science, Geography and Frontify: The Future of Venture Capital


One of our fundamental beliefs at Blossom is that great teams aren’t limited by geography.

Many of the best startups are emerging from the unlikeliest of locations: UIPath ($3bn unicorn) from Bucharest, Supercell ($9bn exit) from Helsinki, Farfetch ($7bn IPO) from Lisbon. No longer do you have to be in a hub to create a world-leading startup.

Despite the historic importance of startup hubs, we’re at the beginning of a fundamental shift: the very nature of the global startup ecosystem is changing — becoming more distributed.

Access to ‘knowledge’ is no longer the biggest challenge faced by startups outside the major hubs; the ever-widening availability of resources both online and at conferences has democratized best practices in getting to product-market fit.

Instead the major roadblock for these founders is sourcing venture capital financing from experienced investors. Investors who’ve seen first hand what it takes to build unicorns and will not only write cheques, but also offer the hard-won practical experience gained from scaling startups into global companies.

Unfortunately investors, especially the handful who’ve nurtured unicorns first-hand, tend to be clustered in a handful of cities around the world.

It’s understandable that VCs should choose to focus on hubs. The single biggest bottleneck at any VC firm is partner time — from an efficiency viewpoint it makes much more sense to double-down on the likes of SF, Beijing or London, where dozens of deals are happening at any given moment, rather than spending long days and shoe leather on the hundreds of cities which might only see a potential breakout startup once a decade.

We believe this approach is a mistake.

When we started building Blossom, our commitment from day one was to find the best early-stage startups in Europe — and we know from studying the data that means going beyond the hubs.

Naturally, like any other VC, we’re time-constrained, so we turned to the solution we always encourage our portfolio companies to follow: using technology to help us scale more efficiently.

Data Science in Venture

I was fortunate enough to build one of the first data-driven deal sourcing practices in venture capital when I joined Index Ventures back in 2012.

Having seen many of the seed stage startups my models identified at that time go on to raise mega-rounds and become unicorns, we had the confidence at Blossom that we could build a fund where data and technology was at the core of how we worked, rather than just an incremental edge.

The burgeoning European ecosystem at Series A is an ideal match for a data-driven approach, allowing us to algorithmically source companies on a level playing-field. No matter where a startup is born, using data to source means that we evaluate based on team, product and market rather than location and network.

Traditionally, VCs who’ve used data for deal sourcing have focused on using it to produce better search engines (with few exceptions like Social Capital). Building internal tools akin to CBInsights or Pitchbook to allow them to filter companies by web traffic or employee growth to speed up research. Yet these metrics fail to capture what really defines a future unicorn in early-stage investing: visionary founders, a strong team and a category-defining product in an important market.

So our approach is a different one; rather than taking widely available metrics and building models around them, we start from the opposite end of the equation. We take the methodology we use to evaluate deals during research and investment committee discussions, and build models to replicate the investor mindset — seeking data sources (or building them where they don’t already exist) that can feed these models.

This approach has empowered us to be geographically agnostic when it comes to sourcing, allowing us to scale far beyond what’s been possible with traditional venture capital approaches. Even within startup hubs it has enabled us to identify exceptional startups long before they hit our radar through traditional means.

We’re strong believers in the value of building relationships with startups in advance of fundraising. When we invest at Series A, we make a commitment to a long-term relationship knowing the average liquidity event is likely five to ten years in the future.

By using our models to guide us to the best startups long before they start fundraising, we ensure we have the time to establish those relationships on a strong bedrock.

The New Hubs

It’s against this backdrop that we’re delighted to announce we’re leading an $8.3m Series A in Frontify. With a best-in-class product, top-percentile metrics and a global blue-chip logo list that would make any enterprise SaaS company envious, Frontify’s platform is transforming how companies think about brand.

As soon as we started spending time with the Frontify team it was obvious this was a company we wanted to partner with. But what made them truly exceptional wasn’t just their extraordinary success so far; it’s where they achieved it from. They weren’t in the valley or even one the major European hubs.

Rather, they’re in St Gallen, an idyllic cobblestoned university town nestled between the Swiss Alps and Lake Constance, fifty miles east of Zurich (if this sounds like the kind of place you’d love to work they’re hiring across the board).

While this makes them exceptional today, we see them as the harbinger of the future, one where we will see unicorns flourish in countless cities around the world.

Frontify is our first ‘out-of-hubs’ investment sourced through data science; as the tech ecosystem expands beyond its traditional centres of gravity, it looks set to be the first of many.

You can follow me on Twitter @imranghory and the Blossom team at @blossomcap.

If you’re approaching Series A stage and our data science models haven’t yet guided us to your doorstep, you can still reach out to us the old fashion way either through a mutual introduction (Blossom on Linkedin) or email