We have all seen how data-driven learning is changing education. Data gives educators more insight into individual student performance, and access to data has started to change the market as a whole. To be able to sell into the education market, you must give your customers access to data which drives improved educational outcomes.
Yet, when we polled many of our partners (and prospective partners) to ask how hiring practices have changed with the market to utilize data, we were surprised to hear that very few of our partners have clear answers as to how they collect and use data to make hiring decisions. If data is king in the education space, why do education companies fail to use data in their hiring practices?
Similar to the debate within the education space on data, there are two main reasons why companies struggle to use data in their hiring processes: 1) Companies do not know what data they should measure and 2) Companies do not know how to measure data meaningfully.
To make data-driven hiring decisions, we first need to determine what we should measure when evaluating candidates. We recommend to our partners that they measure all of the following during their candidate evaluation process:
- Past-Performance
- Internal Drive
- Cognitive Skills
- Behavioral/Personality Characteristics
- Cultural Fit
With a structured interview process, hiring managers can easily uncover past performance data. As an example, a hiring manager can ask sales candidates to walk through the last five years of their sales quota and attainment numbers, breaking these down even further into new business and renewal goals to determine if a candidate is not only capable of performing against a goals but is skilled at hunting, farming, or both. For marketing candidates, hiring managers can quantify a candidate’s ability to meet marketing qualified lead (MQL) goals. Many of these items can be verified through a traditional reference check. Past performance tends to be the easiest data point to measure for most companies.
“With a structured interview process, hiring managers can easily uncover past performance data.”
What becomes more difficult to measure are traits like internal drive, cognitive skills, behavioral/personality traits, and cultural fit. If you have either said to yourself or heard from your hiring managers that a candidate “seems driven because he was high energy in the interview” or “seems capable of solution-selling as she was able to clearly articulate how she met a key customer’s needs”, then you are falling short of a data-driven decision. While we recommend our clients build interview score cards to quantify answers to questions on key hiring criteria, this type of data is still soft.
“What becomes more difficult to measure are traits like internal drive, cognitive skills, behavioral/personality traits, and cultural fit.”
Just as technology has helped integrate data into educational practices and policies, technology has improved our ability to assess candidates against quantifiable, hard data. Online behavioral assessments, when integrated as a step in your candidate evaluation process, can give hiring managers the data needed to make stronger data-driven hiring decisions. For instance, when evaluating sales candidates most hiring managers evaluate candidates for internal sales drive, solution sales methodology, ability to build pipeline, and negotiation skills. We can see how candidates score on traits such as assertiveness, responsiveness, and intensity; these three traits are leading indicators of internal sales drive. We can look at cognitive skills such as logical problem-solving to uncover solution selling indicators.
Candidates who score as highly optimistic tend to miss cues that deals will not go through; therefore, these candidates tend to fail to build pipeline. Last, a key indicator of a sales candidate who will bend on price before pushing through a price objection during negotiations is a heightened level of adaptability; candidates who are highly adaptable may be so concerned with making the customer happy that they forget your bottom line.
Reference checks can also be conducted in a manner which can offer true data-centric feedback on a candidate. Most organizations state that they have nearly a 100% positive reference response to a traditional reference check (when candidates select colleagues with whom they are very close for a hiring manager to call upon.) Of course the references will say kind things about the candidate, as they A) are close to the candidate and B) do not want to experience repercussions for giving any negative feedback. If a reference check’s goal is to reduce hiring risks, the traditional reference check, which results in a nearly 100% positive response, is useless. In contrast, confidential, blind reference checks provide much more accurate data on a candidate. Blind reference checks collect data in a confidential manner by conducting a blind survey on the candidate that measures candidates on a series of key areas including: performance, cognition, inter-personal skills, cultural fit, drive. Because the references responses are confidential, references will give more accurate feedback. An evaluation report is generated through an algorithm to compare collected data against thousands of similar candidates’ reports worldwide. Even a one-hundredth of a point difference in score can be a key indicator of a candidate weakness. At The Renaissance Network, we created a company-wide initiative for 2015-2016 to decrease the number of candidates who pass the reference check by using data. We were able to go from 95% passing checks to 65% passing by implementing a blind reference check process.
As the education market continues to be at its height of competitiveness, and as you strive to be cognizant of the data required for your customers to buy and see an impact on student and teacher effectiveness, consider the role that data could play for you in your hiring initiatives. A stronger candidate evaluation process with a focus on data-driven hiring decisions can have a significant impact on your revenue growth for 2016.
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