11 June 2019

Resumes are killing your hiring predictability and this is why.

”It’s not about what you’ve done. It’s about who you are.”

We all love predicting. We want to predict tomorrow’s weather, next month’s revenue and yes, of course we want to predict next year’s hiring success. After all, a bad hire is one of the most expensive mistakes your company can make.

So predictive hiring is on its way to become the new standard. One serious bottleneck we need to get rid of: resumes. In this blog I will tell you why.

This is what to expect:

  1. What is predictive hiring;
  2. Three reasons why we need predictive hiring;
  3. The one thing you can’t get out of a resume;
  4. How to implement predictive hiring.


What is predictive hiring?

Predictive hiring basically means trying to find patterns in historical data and using these patterns to predict future outcomes.

An example: We’ve recently hired Tim, our very first Customer Success Manager. Based on all data worldwide we can conclude that certain skills are necessary to perform successfully in this role, such as impulse control, task prioritizing, accuracy and flexibility. Besides we’ve seen in our company that certain personality traits are needed to fit into our company’s culture and way of working.

Based on these patterns we were able to setup a ‘formula’ for a successful Customer Success Manager. And luckily we found Tim within a few weeks.

So we basically used historical data from both databases and our own company, recognized a pattern in this data and used this pattern to predict whether Tim would fit this pattern. Now we’re measuring his performances (which are more than good, thanks to the predictive data) to see whether our pattern (i.e. prediction model) is as good as we believe it is.

Three reasons why we need predictive hiring.

Reason #1: Quality of hire

When making hiring decisions based on a resume and an interview, the chance of making the best decision is 48%. Auch. However, when making a hiring decision based on predictive data, the chance of making the best decision is 92%. I don’t think this needs any further explanation, right?

Reason #2: Hiring efficiency

Let’s imagine that you’ve just posted a new job opening. One day later you open your inbox et voilà: 25 new applicants. Now these are the two options:

  1. Manually reading all resumes and ranking every single applicant;
  2. Reading the report that shows you the key statistics per applicant, based on predictive data.

I already know your answer (or at least I really hope you will go for option 2). Going for this options prevents you from spending a lot of time on evaluating (or even interviewing) applicants that will likely turn out to be a bad hire.

Reason #3: Earn money

Alright, I know that this reason is quite complementary to the first two reasons, but I believe it still deserves a well-earned spot because it makes every entrepreneur (including me) extremely happy. Earning money!

Yes, you read that correctly. Earning, not saving.

Let’s start with reason 2, hiring efficiency. Imagine that your hiring team can work 40% more efficiently by using predictive data (and therefore prevent themselves from focusing on the wrong applicants). This is 40% more time your team can spend on further improving processes and innovating strategies that will actually make a difference on the long run. Win-win, isn’t it?

Cool, right? Well, it gets even cooler by focusing on reason 1, hiring quality. Let’s make a quick calculation.

Company X hires 10 Sales Executives per year.

  • These Sales Executives earn an annual salary of €33.000;
  • When trusting on a resume, a Sales Executive stays in the company for 14 months;
  • A Sales Executive is up and running within 4 months and earns €25.000 per month;
  • This means that a Sales Executive will earn €250.000 for company X.

Now when trusting on predictive data, a Sales Executive stays in the company for 2 years (43% longer). This means that a Sales Executive will earn €350.000 (40%) more. Multiply this by 10 and there you go. Quite a lot of money, isn’t it?

The one thing you can’t get out of a resume.

(C) Equalture, 2019

This is Emma, your newest applicant. And this is what her resume tells you: her track record. So now you know what she’s done so far. But how well did she actually perform in those jobs? And does she actually have the skills and personality traits required for your job? I wouldn’t have a clue based on this information, so good luck with making a decision.

(C) Equalture, 2019

See the green aspects I’ve added? That’s Emma. And that’s the one thing you can’t get out of a resume: the person behind it. A resume doesn’t tell you who someone is; it just tells you what someone has done.

It’s like stepping into an airplane. Yes, the airplane is there, but if there isn’t a pilot to fly you to your destination, then what’s the point of the airplane? A resume is the airplane. And you’re not hiring an airplane. You’re hiring a pilot to make your business fly.

Why is this so important?

So why it is so extremely important to learn more about the actual person behind the resume? Although this is actually a rhetorical question, I’m gonna answer it anyway:

  1. Intelligence and cognitive skills: These two aspects will reveal whether you have what it takes. Research has shown that employees that perform below expectation are likely to leave the company within one year or end up with a burnout. Why? Well, simply because it’s killing to work above your capacities. And of course this is also how if works the other way around: a lack of challenges/a learning curve will as well lead to unhappiness and turnover.
  2. Personality: This aspect will reveal whether you fit the team and the organization. Even though your skills might be more than perfect, if you’re not able to work with your team members or according to a company’s way of working, success will never follow.

How to implement predictive hiring

Equalture is a pre-hiring technology that predicts an applicant’s future performance, so we’re all about predictive hiring. Besides Equalture there are more tools on the market which are fully focused on predicting future performances of applicant, so I would definitely recommend you to google and dive into the world of predictive hiring.

So implementing predictive hiring will likely mean you’re going to implement a new hiring technology. However, I also want to give you a look behind the scenes so that you know how a predictive model is built up.

There are three ingredients to make predictive hiring possible:

  1. A complete dataset;
  2. An algorithm;
  3. A feedback-loop.

(1) A complete dataset

Equalture is a pre-hiring technology that predicts an applicant’s future performance, so we’re all about predictive hiring.

The first step to make predictive hiring possible was gathering a list of all relevant data needed to predict an applicant’s success. We’ve categorized this data in two categories:

  • Past performance (What have you done):
    • Education
    • Work experience
    • Hard skills
    • Other activities
  • Potential (Who are you):
    • Personality
    • Intelligence
    • Cognitive skills.

So that’s all possible data we could think of to predict every single applicant’s performance (from junior to senior positions). Please note that not everything is relevant for every role.

We want this data already in the very first step of the application process. That’s why we’ve built a brand-new job application flow to ensure that you gather the data you actually need to create a predictive model.

(2) An algorithm

So now we have all the data. Cool! Step two is to build an algorithm to translate your input (the dataset) into the desired output: a success prediction. In this stage it’s all about searching for relations between the different variables. We’ve built our algorithm based on thousand of researches and databases and based on our own experience as a recruiter.

(3) A feedback loop

This is the last and maybe most important step: a feedback loop. Why? Well, every single algorithm is partly based on assumptions. The only way to improve an algorithm is by telling the algorithm whether its outcome was correct yes or no.

In our case we provide clients with a success prediction per applicant. The only way to find out how good our predictions are is by tacking how successful an applicant actually is after he/she is hired. So we’re working on building a feedback loop: we feed the algorithm with the data it needs to become better at predicting an applicant’s future performance.

So that’s how we aim to help our clients to predict an applicant’s future performance.

Willing to have a chat about how we measure all these datasets or how we continuously improve our algorithm? Just drop your contact details, happy to help!

Cheers, Charlotte