Too many organizations differentiate between talent acquisition operations data and candidate feedback data. The truth is that these are two sides of the same coin. And TA leaders that don’t view them as a whole, are missing huge opportunities for additional efficiencies and effectiveness.
Talent acquisition operations data would be loosely defined as just the facts. Who does what, and when they do it. Feedback data is basically how things happen and the quality level of results. Relatively recently, recruiting organizations have “operationalized” and started rigorously using operational data to manage the efficiency and effectiveness of their people, processes and technologies with some success.
Specifically I am talking about data like job, location, recruiter, hiring manager, hire date, application date, source, cost, etc. This data is mostly captured in core systems like the ATS. From this data, metrics are developed for managing operations. Metrics like Time to Fill, Cost per Candidate, Cost per Hire, Engagement Rate, Application Conversion Rate, Offer Acceptance Rate, etc. These are powerful metrics and they are the bedrock of managing any talent acquisition organization. They uncover inefficiencies, provide KPIs, and help drive changes that increase efficiency.
But they don’t tell you specifically what is working and what isn’t. A bottleneck report in an ATS, for example, can show you that something is wrong, but it doesn’t tell you what or why. You have to figure that out on your own. Similarly, if your offer acceptance rate is decreasing, you are left to guess why. You could cut your cost per applicant in half by doubling your application conversion rate, but how would you do this? Most organizations just make their best guess at answering these questions with limited insights and keep guessing until their talent acquisition operations data show evidence of improvement.
This is where feedback data comes in.
Enter Feedback Data Combined with Talent Acquisition Operations Data
To be fair, I can’t criticize the limitations of operational data without doing the same for feedback data. On its own, most feedback data that talent acquisition organizations gather is weak. Their surveys suffer from low response rates and when they do uncover problems, there is rarely anything actionable about the data. Why? Because the feedback doesn’t point to any operational data that would make it actionable.
For example, let’s say your survey data tells you that people aren’t getting appropriate communications during the hiring process. What do you do? Which stage of the process? Is this happening for all positions? WIth all recruiters? At all locations? You don’t have the operational data that would tell you where, when and with whom that issue is happening.
By combining operational data with feedback data, it becomes easy to diagnose a number of specific problems. Is the poor communication feedback tied to the application stage? Well, the problem is likely that your ATS is misconfigured or a link between your CRM application and your ATS is not working and auto-emails are not going out. If feedback comments are tied to this operational data, they will tell you exactly what the issue is.
Is the poor communication happening at final disposition? Well, mapping disposition feedback to individual recruiters can show you that you have ATS users who are not statusing rejected candidates properly resulting in auto-emails going out.
Real World Examples of TA Operations Data and Feedback Data Wins
Above are just some basic examples of how feedback and operational data can work together to pinpoint problems and turn questions about issues to answers. Here are a couple of real examples of how feedback and operational data result in insights that uncover specific problems:
- One Survale client saw that their internal candidate satisfaction was lower than external satisfaction. This is highly unusual. When they dug into the feedback data they quickly learned that the internal mobility technology they had recently acquired was hampering candidates rather than helping them in some very specific ways as they were exploring opportunities. The specifics were detailed in feedback comments. They were able to reconfigure their internal technology and fix the problem. This was diagnosed by combining feedback data (candidate satisfaction data) with operational data (internal applicants and hiring stage).
- Another client saw that their offer satisfaction rates for high volume hourly hires were very low compared to exempt hires and many were not showing up for work. Drilling into their hourly feedback data they were able to learn that their technology was often auto-hiring employees for positions at distant locations, many without even going through an interview process. This all was made possible by connecting feedback data (offer satisfaction) to operational data (hourly employees at specific locations).
- Another client had a territory that was vastly underperforming against the entire organization in terms of meeting hiring goals. Survale data showed them that, not only were this territory’s interview satisfaction numbers much lower, but it showed them that some very specific hiring managers were responsible for the low satisfaction. Once these managers were retrained, hiring results and satisfaction improved to baseline for the organization. This was solved by tying feedback data (satisfaction scores) to operational data (location, hiring stage and hiring managers).
Combining feedback data with operational data allows talent acquisition organizations to quickly and confidently optimize their people, processes and technologies in very specific ways. Ways that previously just weren’t possible a few years ago.
