How can we improve interviews?

Real talk: Most interviews are not great, or scientific, or anything you need them to be.
Consider the 10 Most Common Interview Questions as defined by Hubspot, which is based on some Glassdoor research about the 50 Most Common Interview Questions. Close your eyes for a moment and see how many of the 10 or 50 you could identify as a recruiter. We’ll give you a few seconds.
Ready?

Here are 5 that are sure to not set your process apart:

• What are your strengths?
• What are your weaknesses?
• Why do you want to work here?
• Where do you see yourself in five years?
• Can you explain these gaps in employment?

Now, at a certain level all these questions are necessary to ask — although at every other level, they’re generic, they’ve been around for years, and most candidates know how to “game” them. It’s very rare to get at specific skills or ways of thinking through these common interview questions.

There have been evolutions in interviewing over the years, yes. Behavioral interviewing is/was a major trend line for some companies, but for it to be successful, it needs to stretch out of the traditional behavioral approach and into something more in-depth. (That’s the approach we use for technical screenings.) There are also concerns that both could create cultures of sameness, homophily, or birds of a feather flock together. Those cultures perform worse in economically-uncertain times.  When an objective third party does the technical interview (a service we provide), homophily is reduced inherently: said third party won’t know your internal culture well enough to recommend “sameness” hires.

Here’s the essential pain point, then: you need a way to evaluate potential employees that get at whether they will be effective. Will the cost of the hiring process and the eventual salary be justified to a point? Or will it be a complete miss?

The initial solution: Better interviewers and better questions
One of the issues with how many interviews are structured is that there’s inherent information asymmetry between the sides. An HR rep might be doing an initial screen for a marketing role, but knows nothing about marketing aside from a few bullet points of “what to look for” via the hiring manager. In such a situation, it’s nearly impossible for the interview to be effective. One side understands one concept (marketing) and one side does not. It has to be generic by design. Admittedly that’s a top of funnel interview and it can be argued the design is generic, but in the last 10 years, we’ve seen this problem grow.

Why? Because the tech stack’s advancement is outpacing managerial knowledge (AI, AR, VR, Data Mining, Blockchain, etc.) Managers may know they need a skill set, but they don’t know how to interview for it. They might not even be sure what to ask to figure out if this person knows what they need to know. This creates flawed interviews. The hiring manager may have decided to do the interview anyway because it’s a role for his team, but he might have no clue how to vet the success of a tech candidate. When this happens more than once, poorly-constructed tech teams come together, potentially managed by someone who clearly didn’t understand the tech in the first place. That’s a recipe for the turnover.

This is the specific problem eTeki fixes, by providing expert interviewers so recruiters can assure their hiring manager’s peace of mind. A hiring manager benefits from knowing candidates know/don’t know their stuff because someone that does know that discipline just vetted them. You can now avoid bad hires off technical interviews.

That’s one way to solve the interviewer problem. As for the question problem …
… we live in an era dominated by data and information, but interviews haven’t yet moved in that direction writ large.

Michael Schrage, a research fellow at MIT Sloan, has noted that in tech hiring, whether you’re qualified stems directly from how you’re quantified. This is also eTeki’s approach. As noted above, we’re rooted in behavioral questions, but we extend those questions into in-depth technical screening. We want to make sure every candidate we work with knows the basics (and the advanced content) of their discipline, but also need to assure they know how to solve problems personally and professionally. A flow might look something like this:

• Hey, tell us a problem you’ve seen about yourself (“I was missing deadlines due to trying to achieve too much with my releases”)
• Hey, tell us how you decided to fix it (“Communicate better with manager and team members on the core priorities of the specific release so that we hit deadlines”)
• Hey, tell us what you measured and how you knew you were fixing it (“Percentage of deadlines made, while also factoring in bugs, re-releases, and customer satisfaction scores”)
• Hey, give us some broader context around it (“We also managed to develop a new Kanban process around how we slot out work during heavy workflow periods”)

If we all want our companies to become these big, bad Big Data and Analytics organizations that really make measurable decisions (!) about consumer tendencies (!) and all the other unicorns we’ve been promised for the last half-decade, then don’t we need to get people in the door who actually understand data, tracking, self-awareness, and what metrics can be used for? People who can even apply those concepts personally?

Additional resources
We’ve put together a guide to 13 Technical Interviewing Fails (noooo) and how to fix them. You can grab it here if interested in improving your processes: