Visier – taking HR analytics into talent acquisition

Brian Sommer Profile picture for user brianssommer November 16, 2016
Visier has built out an impressive analytics suite amines at better understanding talent acquisition. Here's my take.

Visier, a pioneer in HR analytics technology, recently announced a predictive Talent Acquisition solution. It’s not the typical recruiting offering as the product represents a collection of insights and analytics around the recruiting methods, practices and beliefs that firms possess. Machine learning and in-memory database technologies are used to deliver some of this value to customers.

This new solution should be particularly helpful for firms competing in tight labor markets or looking for people with scarce skillsets.  One aspect of the solution helps firms understand the ‘leakage’ in their recruiting process. Another helps them understand how well they may be doing in creating a more diverse workforce. And still another helps with predicting (and improving) time to hire and quality of hire.


Visier's waterfall graphic (below) helps companies understand where people are dropping off in the recruiting process. 


Why is this important? The longer a company takes in deciding whether or not to hire an individual, the more likely the better candidates will be hired away by a faster, more nimble competitor. Here is one data point on this issue from The Balance:

Average Amount of Time to Get a Job Offer

The amount of time from interview to job offer varies. For college grads, the National Association of Colleges and Employers (NACE) Recruiting Benchmarks Survey reports that, on average, employers hiring new college graduates may take 22.9 business days to extend a job offer after an interview.  Once the offer has been made, the candidate is given less than two weeks (an average of 13.3 days) to make a decision.

That's the average length of time to get a job offer for one sector of the job market.

For others, offers were received within 24 - 48 hours of interviewing. In other cases, the hiring process can drag on for weeks.

Businesses often add too many steps, too many return interviews, and, too much time to the recruiting process. As the best people abandon the potential employer, less desirable hires remain in the applicant pool. Analytics, like Visier’s tool, help understand where the drop-off is occurring.

On a personal note, I was once being courted by a major analyst firm. It took a few weeks to get that first interview scheduled as their executive was on the road a lot. It went well and they wanted me back for another interview. It took weeks to get that one scheduled. This went on and on. I gave up after a while as I didn’t want to work with a firm whose processes are so screwed up and who put so little concern into the schedule/concerns of their job seekers. Plus, I knew the only people that would work for them were too willing to get treated like doormats. I needed to be with people who don’t suffer fools gladly.

A time-consuming recruiting process adversely affects a company’s employment brand and reduces hire quality. If Visier’s solution only helps one firm quit being a time-sucking recruiting mess, it’s a victory.

Time to fill

Ask operational leaders at many firms about how well they like Recruiting and you might get back some unpleasant remarks. In some firms, recruiters can’t find/source enough qualified applicants.

Sometimes the organization throws up lots of time-consuming roadblocks to prevent rapid hiring and on-boarding of new workers. Some managers bellyache about unfilled positions but won’t free up time on their schedules to interview candidates.

Products like SmashFly, which I discussed earlier can help build a great prospect pipeline. However, recruiters will still need to give operational leaders better guidance as to how long a position will remain unfilled so that they can do a better job of forecasting overtime, scheduling others to cover the work left vacant, etc.

Visier uses historical company data to help companies understand the likely timeframe it will take to fill an open position.  That data is crunched, real-time, against a machine learning tool to determine the likelihood of filling the position.  The tool is sensitive to: the role or group of roles selected; the quality of the data sample size; and, past time-to-fill data for the role(s).


Time is the enemy of HR – a theme I’ve repeatedly written about.  Operational executives must understand how long it takes to fill an open position and then re-engineer their processes to squeeze out every bit of non-value added time.

Quality of hire/diversity of hire

Some firms, lamentably, only want to ‘put butts in seats and don’t care too much about the quality of the people they hire for specific roles. That’s a shame. For all other firms, quality of hire is a key goal. The problem is how best to measure hire quality?

A great HR/Recruiting solution captures a number of metrics that are rarely stored within a recruiting solution. High quality hires should be top performers. They should earn great performance appraisals, above average pay raises, close more/bigger deals and are the go-to expert for their part of the company. These people are desired as mentors and should stay with the company an above-average length of time.

Great analytic tools track employees and determine the hiring sources and activities that increase the odds of finding and attracting a better share of high quality employees than competitors.

When I interviewed Visier executives on this matter, we discussed how a lot of unchallenged beliefs guide talent recruiting efforts. I mentioned how one Wall Street firm believed that people who rowed crew at an Ivy League college would be great investment bankers. That may have been one person’s limited experience but it is entirely wrong to generalize a single experience to an entire universe. Anecdotes, one-time successes and long-held and possibly out-of-date beliefs are not the same as data-driven facts.

The technology

The technology re: analytics in HR has changed significantly of late. Initially, there were a number of standalone of HR analytic providers. Visier is one of those early leaders. Subsequently, a number of HRMS, talent management and ERP vendors added analytic capabilities to their solution sets.

Are analytics that are part of a larger HR suite preferable to best-of-breed/standalone analytic applications? It depends.

If the standalone applications are superior from a functional perspective, then, of course, these would be preferable. The reverse is true, too.

Right now, it may be too early to provide a definitive answer as no vendor has developed a complete, all-encompassing suite of HR analytic tools.

To this point, a major HR vendor recently told me they have an 8-page listing of potential analytic applications they could build. However, since that vendor generally needs 18 months or more to develop, test and make market-ready a new application, it’s going to be a long time off before their suite trumps many standalone HR analytic suites.

Visier software provides a number of pre-built capabilities vs. a raw toolkit. Customers have to pay for a minimal standup cost to get the solution integrated with the various HR and other datasets a company possesses. Should HRMS data sources change, Visier does not charge a change fee to make these adjustments.

My take

Visier has put more thought and for longer, in HR analytics than most any other competitor. They started off years ago by doing some great primary research.

Specifically, they convened groups of top executives and not just HR executives, to ask them some fascinating questions. One of those was something like “So what sort of clues did you have that a particular employee was planning to leave?”  From these sorts of questions came lots of clues as to where pieces of an answer might lie.

Many of those data sources came from non-HR and some big data databases. As a side note, employers learned to watch which employees were suddenly using up all of their vacation time, had cashed in all of their stock options/sold all their company stock, had been out of the office on Fridays, had created a LinkedIn profile or started updating, etc. These factors go into an algorithm to determine which employees were most likely attire when set against a control group.

I like that Visier instead the time to do the research needed for them to build out a fact based solution. Some vendors just crunch convenient HR databases looking for anomalous data. That’s the laziest and flawed way to find insights. Some test out hunches. Some find interesting correlations but don’t really understand why these correlations actually exist. Visier looks for facts.

Other firms have mimicked many aspects of the Visier approach. This is making the HR analytics space more competitive and the analytic alternatives out there more numerous. All of this is good for HR analytic buyers. The questions for software buyers will be:

  • What business problems are critical to our firm?
  • Which analytic vendors provide insights into these issues?
  • Which solutions use the best data sources to drive insights?
  • How did the vendor chose these data sources?
  • How well will these data sources work with our workforce? Location?

We’re going to see a lot of analytic solutions from a lot of vendors but how anyone will be able to evaluate which solutions reign supreme isn’t totally clear. Experience will be our guide.

A grey colored placeholder image