How to use big data to predict the success of your startup

Kes Thygesen is the co-founder and head of product at RolePoint,a recruitment platform where the pipeline is driven by high-quality employee referrals.

Big data. You’ve heard about it. You’ve read about it. Maybe you’re building tools that are shaping it. No matter if you’ve taken advantage of it or not, big data has the power to let you analyze success with more accuracy.

However, can big data predict the success of your startup – more specifically the success of your employees and how they play into your growth? Absolutely.

Larger players like Xerox have already used big data to cut employee turnover in half while hiring the best talent for their company by implementing cognitive and personality-skills testing. The team also looked at elements unrelated to the actual job description and found correlations between retention and engagement. Big data helped them get there.

The 💜 of EU tech

The latest rumblings from the EU tech scene, a story from our wise ol' founder Boris, and some questionable AI art. It's free, every week, in your inbox. Sign up now!

There’s no reason why your startup, no matter what stage of growth it’s in, can’t emulate the success of companies like Xerox in your own hiring strategies. Here are some ways you can do it.

Implement better screening tools

If you screen candidates better from the get-go, you can gauge how well they’ll perform for the foreseeable future. For example, it’s not enough to assume that someone who’s had lots of jobs in the past is automatically a bad employee. In fact, big data research challenges this by noting that candidates with frequent job changes did not perform better or worse than those with long-term employment.

Instead, think about implementing better screening tools based on attributes that matter most to your startup. Evolv, a small company out of San Francisco, has developed tests that show how much of a match a potential employee could be based on predetermined factors.

For example, retail sales candidates are tested on decisiveness, spatial orientation, and persuasiveness. Customer service personnel are tested on rapport and relationship building. The outcome of these screening tools allow for better hiring, performance assessment, and overall management.

Go social

As a small company, you may not have the resources to pay for expensive tests and assessments. That’s where social networking comes to the rescue.

It’s clear that today’s job seeker is connected to nearly every social network. While it was once seen as a distraction, social platforms can provide you with a treasure chest of big data, such as how candidates interact online and what they share on their News Feeds.

Don’t think you have to go at it alone, either. Many smaller companies are using platforms such as Entelo to go through social data collected from Facebook, Google+, LinkedIn, and Twitter. These so-called “talent search engines” can help your startup to find the right candidates based on elements that are not only important to you, but also proven to lead to success.

Here’s another great example: Studies show happy employees are more likely to be engaged at work. If you sift through social data and find those candidates who promote a more positive sentiment, especially when it comes to their professional lives, it can translate into work performance once they are hired.

Supplement your referral programs

While assessments and personality tests are great, they still don’t beat the No. 1 source of hire and retention: employee referrals. In fact, referred workers have a 46 percent retention rate after one year and 45 percent retention rate after two years.

However, when big data is used in conjunction with your referral programs, the possibilities are endless. For example, our company found that on LinkedIn, a user thinking about a change will update their profile, seek and add new recommendations, increase their connections with recruiters, and follow more companies.

Big data shows that due to heightened activity on a professional social network, users may be more receptive to referral messages.

Here’s another example: When you apply big data to referral networks by mapping the social graph of the organization and employees, you can highlight the best sources for distributing targeted messages and opportunities. As the average number of first degree contacts is 630 across the major social networks, there are ample connections that are relevant to every organization.

You can also track employee activity and response to messaging, which lets you know which employees are more likely to participate in employee referral programs. Collecting ongoing data over user behavior and outcomes allows you to identify your most active referrers with the strongest relationships.

All of this contributes not only to the success of your referral program, but also shows you which employees are going to be your strongest asset when referring quality startup workers.

If utilized correctly, big data can be a huge predictor of success, both in terms of your organization and the types of workers who’ll stick around and create results. While you may be an up-and-coming startup now, don’t discount that immense value big data can provide you with for the long-haul.

What do you think? What are some other ways big data can predict startup success?