If Talent Is So Important,Why Is Our Approach To Acquiring It So Damn Old?

Mark Jacobson - 17 Aug 2021

“Most of the world will make decisions by either guessing or using their gut. They will be either lucky or wrong.”  Suhail Doshi, Founder


Over the course of my 20-year career as an entrepreneur and executive recruiter, I have always been fascinated with startups and venture capital. The ones who dared to dream of the “Next Big Thing” and had relentless determination to bring it to market. Much like Hollywood, fortune favored the bold and it was easy to see why investors bet on “A teams” with “B ideas” rather than vice versa. Talent mattered... a lot.

Historically investors poured money into companies started by visionaries. Large corporations hungrily scooped up talent through organic and inorganic (M&A) strategies. And talent was qualified by obvious markers like pedigreed academic institutions, branded companies and proximity to core tech markets (such as SF, Boston and NYC), with access being limited to carefully cultivated relationships. But, for the most part, despite its involvement in the Tech Industry, the Talent Acquisition industry, much like the Venture Capital industry, worked with the most cutting-edge companies, but rarely adopted tech for itself.

As part of the founding team at SignalFire, I knew from experience that every startup really valued the ability to recruit great engineering talent. We believed that by recruiting top talent, we could work our way onto the cap table even when no one really knew who we were. Turns out, we were right! SignalFire was among the first “quant VCs” to build proprietary software that structured a vast amount of data and surfaced insights enabling their talented investment team to interpret data, identify opportunities programmatically and use data to better serve their portfolio.

In 2017, when we spun Terrain out of SignalFire, I fundamentally believed that there was an opportunity to make a bigger impact in job creation and economic growth by using data much in the same way that advanced analytics had been applied in other ways, such as sales & marketing automation, asset utilization, demand forecasting, and as evidenced by SignalFire’s success, the venture capital industry.

THE EARLY DAYS

Initially, we experimented with ideas of algorithmic M&A to rapidly identify exceptional “acquihire” targets. Companies like Facebook, Yahoo!, Google, Apple, Cisco and others made acquihires popular by snapping up small companies just to get their hands on the best teams. We wanted to democratize this capability for the rest of the tech ecosystem.

Yet, while deals could create an opportunity to upgrade organizational talent, mismanagement of talent could negatively impact the success of those deals. We frequently heard executives recount stories of how they entered markets via acquisition. Some succeeded, but more times than not, costly mistakes were made. Talent churned out or the acquirer entered a market with a limited talent pool they didn’t understand and ended up not being able to scale their teams as envisioned.

It’s incredibly challenging to identify and retain the most valuable individuals in large-scale deals and to know which ones were at risk of churning out. In some cases, companies had completed a “talent to value” exercise to try to understand these issues more deeply and deploy retention strategies. But most of what we saw was tech stack diligence, interviews with executive teams only, and product/revenue analysis as the baseline for most decisions.

In other cases, companies frequently believed they had an “inside edge” in recruiting talent in lesser known talent pools, for example, Austin, Denver and Vancouver, or through “unique” relationships on a university campus.

What was striking though was how often we observed the dependency of Boards and Leadership teams on the CTO’s inherently biased judgment to vet the quality of the engineering team and tech stack, Corporate Development team’s connections to top tier VCs to identify “rockstar talent” to acquire, or even worse, generic (and bad) advice like finding talent with the closest proximity to the most Fortune 500 companies. These were smart people who used all kinds of data to make key decisions in every part of their business, except when it came to building a workforce plan to meet their specific goals and needs.

OUR A-HA MOMENT

In 2017, when Amazon received bids from 238 cities and regions for their HQ2 site selection process, we were shocked to think that there were even upwards of 20 cities that could support hiring upwards of 25,000 skilled workers for one company. A handful intuitively made sense, but 200? We watched with fascination as the RFP process increasingly received national attention - and then we asked ourselves, “what would our data tell us?”

We decided to take an outsider's viewpoint and only look at corporate data rather than factor in traditional data points like tax incentives, certain other businesses owned, personal residences, or proximity to universities - data that were commonly used by Site Selection consultants. We also chose to ignore obvious signs that maybe, just maybe, there was a slight chance that DC just might get the nod!

We narrowed our analysis to focus on where the best technical talent worked. From there, we could identify which city would present the best value of talent and whether or not there would be enough available high-quality talent to actually scale the company. Then we created our top 5 list and socialized it with a small subset of customers and partners to gauge their reactions. The positive feedback we received was encouraging and indicated there was a meaningful gap in the market.

Further market research showed us that the de facto process consolidated fragmented data and was virtually entirely manually stitched together. It relied upon common data sets (such as Census data), layered in other data points (number of universities, direct flights in/out of the city, commute times, etc), and relied on local broker market intelligence. Reports spoke mostly to macro-level data and didn’t do a good job of answering specific questions that companies had pertaining to talent in their respective industries. Definitions on “tech talent” were broad and all-encompassing - sales, finance and engineering tech workers were all lumped into one category. Classifying all tech workers in one generalized bucket felt as modern to us as using AOL dial-up to access the Internet.

Curiosity pushed us to go beyond the obvious. Wouldn’t it be more helpful to know how many software engineers, for example, were in a particular market? What was the quality of those engineers? And how would one city compare to another? In virtually every other function and discipline, we use technology and big data to formulate insights on how to optimize decision making, so why not in Workforce Planning? Going further down the rabbit hole, we came to understand the huge search costs associated with opportunity identification and the potential risk in loss of value in picking the wrong market. No one could clearly answer how talent concentration in Denver was different to Austin or Chicago. Most answers were based on industry reports on venture funding data or unicorn creation (proxies for talent, but vague at best). Data sets were incredibly siloed and inconsistently classified.

Where should emerging growth tech companies locate? Should they go near customers or well-known universities? And what about other industries like Life Sciences that depended not only on Lab space but also increasingly require technical talent, such as data scientists. What are the best cities with converged talent? And how and where would they compete for talent with the FAANG companies and hot unicorn startups? Tons of questions and very few answers supported by data.

Every company says Talent is their most important asset, but virtually every company uses the exact same talent strategy (use LinkedIn, deploy ATS system, and pay massive fees to recruiting agencies with local market knowledge). And, in most cases, this is compounded by heavily underfunding Talent Acquisition relative to other departments leaving non-technical yet super busy people to cobble together disparate information and build a proprietary, bespoke workforce strategy to fuel growth. While Sales and Marketing technology can analyze funnels and conversion rates down to the penny, this kind of felt like playing darts blindfolded.

THE ARRIVAL OF TERRAIN

After a few years in R&D mode, we are proud to introduce Terrain, a Data and Analytics platform, designed to help companies solve the business planning challenges for Site Selection and Workforce Analytics in a new way.

Terrain provides companies with insights they need to find the best value markets with the deepest and most recruitable talent pools. It allows them to differentiate talent among cities and neighborhoods, create bespoke hiring strategies and improve diversity through better data and analytics.

And while COVID-19 has impacted the “Future of Work” by accelerating the adoption of technology across the board, and multiplied by the number of remote workers and distributed teams across the globe, it has blown up the talent concentration in major tech supercluster cities. In the past 12 months we’ve seen the “rise of the rest” in cities such as Denver, SLC, Atlanta, Nashville and Raleigh. With people being located anywhere and everywhere, and with companies scrambling to address the potential “Great Resignation” ahead, it has become nearly impossible to create long term recruiting strategies. The game is no longer about relying upon talent centrality of major tech hubs to create “pull” for your business. You need to know where talent is located now, how big that market is, the depth & cost of the talent pool, and whether or not your company can truly compete effectively for that talent. Human networks can’t do this alone. Technology is the push you need to propel you into the right markets and compete more effectively for the #1 asset in your business… your people.

Terrain’s approach takes capabilities that were historically only available to the richest tech companies with sophisticated tech teams that build proprietary in-house solutions and large talent acquisition and real estate teams, and levels the playing field for companies of all sizes and industries to compete more effectively for talent that can drive outsized outcomes.

By combining structured data with human intelligence, we give CEOs, CHRO and all business leaders "superpowers" when it comes to finding and entering the best talent markets for their companies and successfully scaling their business. In parallel, this creates more jobs and economic prosperity to a broader set of cities rather than just doing more for the already talent-rich supercluster markets.

We believe the most successful companies will embrace this paradigm shift and adopt the tools and technologies needed to increase their velocity of making smarter decisions faster.

Learn why Terrain is the one platform that helps companies find markets that yield access to the best and biggest pools of talent for their business. We invite you to come explore our products!

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