How to use talent data for better decision making in Venture Capital and Private Equity

Gartner predicts that by 2025, more than 75% of venture capital and early-stage investment decisions will be made using a combination of AI and data science and shift away from gut feel and qualitative decision making to a more platform-based quantitative process.

Ott Niggulis

The truth is that you don’t have to wait this long. Data-based decision tools or talent analytics are already here, and private equity and venture capital firms are already actively using them to make better, data-backed decisions.

While there are a plethora of different personality tests, competency assessments, and the like that have been used in various settings for years, they are usually looked at as separate pieces and not as a cohesive system.

This is now changing with a new breed of talent analytics solutions that are science-backed, data-based, and give insights not only to individual members but how all these people fit together into a team and where their relative strengths and weaknesses lie both individually and as a team.

In the world of VC and PE investing, talent analytics has two prominent use cases:

Analyse founders and teams in the pre-deal phase to get a better overview of the team

Make better hiring decisions and build stronger teams post-deal

“Talent analytics take the bias out of the hiring process, and instead of looking back at past experience, they look at the holistic picture – the potential the candidate can bring, along with value, motivations and strengths, as well as gaps.”

Elizabeth Wallace, Head of Portfolio Talent, Hg

Analysing teams in the pre-deal phase

In the pre-deal phase, talent analytics is used to get a clearer picture of the founders and the team to understand their relative strengths and weaknesses individually and as a team.

For example, the data might show that the overall team is excellent at achievement orientation (driven to meet or exceed a standard of excellence) and setting strategic goals but lacking in planning and organisation.

This is valuable information for investors as now they know who they need to add to the team to make it stronger. Same with values and personality.

In general, research has shown that teams that share common values and are different in everything else tend to work together stronger and longer. Talent analytics gives you the insights needed to evaluate this.

It gives investors a chance to deeply get to know the founders and the team much faster. And while talent analytics is no magic wand, it creates the necessary preconditions needed for success down the line.

After all, you'll be spending a lot of time with the team, and having a good value-fit ensures a better long-term relationship, leading to better results.

For Karma Ventures, talent analytics is a tool to get in-depth knowledge into the team that is otherwise hard to get reliably. Over the years, they have experimented with many different solutions for finding fit in terms of values, personality, and competencies.

In their experience, assessments and/or tests used must be:

  • Been proven to work
  • Based on science
  • All sides understand how it works, how it's used, and why

The last part is critical. Before implementing any talent analytics solution, everyone needs to understand what it is, how it works, and why it’s used. Without this in-depth knowledge and understanding, it’s easy to dismiss it and not get the full benefits - you need to understand how talent analytics and the science behind it work.

Building better teams

Every founder needs great people, and almost all of them experience struggles in hiring those people. Of the 50+ VC firms we've talked with, 90% agreed that the main post-investment struggle for startups is hiring executives and maintaining their culture through scaling.

Post investment, companies are expected to take a significant leap forward. The knowledge and competencies that got them this far are not necessarily the same ones they'll need to start scaling their operations. They'll need to hire for new roles, roles that they don't necessarily know a lot about.

“We work with founders who, quite often, have limited hiring experience. After a funding round, they need to start filling new positions at scale and at the same time make sure that every new hire is a good fit with the founding team culturally and complements the organisation with their skill set and personality. Choosing the right persons at the beginning of one's startup journey can and will make all the difference.”

Henri Treude, Managing Partner, Spring Capital

Ben Horowitz has compared founders hiring executives to hiring a Japanese interpreter without knowing any Japanese yourself. How can you hire a CFO or head of HR when you don't even know what the job entails?

Luckily, founders can learn to understand the difference between a good CFO and a great one. They can learn to judge candidates' past experiences and know if that's a fit for their company.

Another part of the story is understanding if a candidate will be a good fit for the team. We are social animals; emotion and personal feelings often play a part in hiring decision-making, and rarely do they make the decisions better. Just because you like one candidate over another or feel more comfortable with them, doesn't necessarily mean that they'll be the best person for the role.

Without any other supporting evidence, the candidates you like the most are more likely to resemble your own personality.

This is a problem.

Why? Having a team of people who closely resemble each other is not a recipe for success. For a genuinely bias-free hiring process, hiring must be evidence-based and use a candidate scoring mechanism to enable fair candidate evaluation.

Like knowing the difference between a good CFO, and a great one, hiring is a skill that can be learned, but in a fast-moving blitz scaling world, there's rarely time for founders to take the time and understand the fundamentals of hiring and the secrets to great interviewing. This is where a clearly defined process comes into play.

"In the scaling phase, there is very little room for hiring mistakes. It's crucial to get the right people or risk underperforming. Talent analytics gives additional insights and confidence in making those decisions."

Piotr Łupiński, Market One Capital

A clearly defined hiring process

A good hiring process is not rocket science, but it does follow a predefined process. Daniel Kahneman, a world-renowned psychologist and economist notable for his work on the psychology of judgment and decision-making, has defined the hiring process in the following way:

  • Define and agree on the hiring criteria (3 – 9)
  • Use set criteria for interview questions
  • Based on relevant data, rate each criterion separately (immediately after the interview – everyone individually)
  • Combine and summarise the ratings
  • Hire the candidate with the best score*

*There are situations where that might not be possible or it’s overruled by an executive decision. Whatever the reason for this, this decision must have a good explanation.

Start by getting crystal clear on who you're looking for and what are the required previous work experiences, knowledge, skills, and abilities needed?

Next, use that for interview questions, rate each of the criteria separately, and finally combine and summarise the ratings and hire the candidate with the best score. For more in-depth information on the hiring process highlighted above, read this.

While the above process makes it easier to choose between candidates in terms of their knowledge and other skills, it lacks making sure that a candidate will fit in with the existing team and work well together with others.

In assessing that, talent analytics really comes to its own. With it, leaders and founders have a science- and data-based tool to use when evaluating all aspects dealing with fitting in and getting a better picture of the candidates in terms of their values and personality.

Using the combined forces of a predefined hiring process and talent analytics ensures that hiring decisions are based on science and data and not on luck and gambling.

And it's not just for startups either. Teams of any age and size can take advantage of the analysis. For example, Karma Ventures has been using Teamscope decision tools to better understand and manage its leadership team.

"It helps us better understand and accept each other's strengths and areas where we complement each other, as well as the areas that we can strengthen the team."

Margus Uudam, Founding Partner, Karma Ventures

Conclusions

Talent analytics is not a magic wand, but it does bring science and data into processes that are often decided based on personal feelings and arbitrary information collected during free-flowing interviews.

It gives VC and PE firms a deep inside look into the inner workings of startups teams. It accelerates the getting to know process between founders, teams, and investors as well as brings structure into hiring decision making.

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