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Imagine this: it’s the end of the fiscal year and you’re pulling ATS reports in effort to make smarter, more strategic budget decisions for the upcoming year. You look at the really big spreadsheet in front of you, sort the data, and begin to analyze where your hires came from. Armed with numbers and percentages, you allocate dollars to job boards, social media, employee referral bonuses, agencies, and maybe even radio and billboards.
But do all of these dollars add up and make sense?
“I don’t necessarily put a lot of weight on the source of hire,” shares Craig Fisher, head of employer branding at CA Technologies. “I want to know the source of employee, as well as the source of impact.” Susan LaMotte, CEO and founder of exaqueo, doesn’t hang her hat on a source of hire metric either, but instead looks at “source of influence.” And Andrew Gadomski, founder of Aspen Advisors, recommends that recruitment leaders analyze the source of “AEIOU: applicants, engaged, interview, offer, and unwanted” versus source of hire.
So, why no love for the source of hire metric?
In their 2015 Candidate Behavior Study, CareerBuilder determined that “on average, job seekers use 18 different sources when searching for a job—a steady increase from previous years and reinforcing the idea of the consumer candidate.” With so many touch points and potential points of influence, is it plausible to have one pure source of hire?
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If not, how can we begin to understand the many influencers?
“Look to employees who have been with the company six months or longer,” suggests Fisher. “At CA Technologies, we survey employees to see if we can spot trends in what they perceive as influencers on their decisions to apply and accept.” For exaqueo clients, “we call them sources of influence,” says LaMotte. “Instead of measuring the last port of call or click before a candidate applies, look at the entire employee lifecycle. Ask candidates and employees what are the biggest influences on their decisions to consider, apply, accept, stay, and leave.”
If not source of hire, then what?
“Source of hire is an old fashioned way of thinking that is not data driven … it’s summary driven,” contends Gadomski. Instead of looking at source of hires, “start looking at the unwanted applications. Look at the giant pool of applicants that did not move through the process to recruiter screen, hiring interview, or offer.”
This is a rather interesting way to look at hiring data! Recruiting teams spend so much time trying to understand WHO was hired that they often neglect understanding the 90%+ applicants that they did not.
Before your next budget meeting, ask yourself: How much time, money, and effort did you, and your team, spend on the unhired? Are there trends or a data story that you can put together to support NOT doing something? What efficiencies or changes could be had if more time was spent analyzing the unwanted? “By tracking the AEIOU, we see things much clearer in how we invest time and be productive,” affirms Gadomski.
How do you build a data story?
While you may not be ready to let go of the source of hire metric just yet, understand that it’s one data point within a larger talent analytics story. To begin to understand the bigger picture, you’ll want to look at all the numbers you have access and visibility into. How confident are you with this data?
To have a strong data program, you have to first have: a target (your hiring goals), segments (how you want to slice the data), frequency (how often you want or need to measure), historical trends, correlations (how things relate to one another), and time metrics (time to hire / time to start). Here are four steps to creating your talent acquisition data program.
Step 1: Get the data
- Know your targets for the year
- Start with operational data, including data from your ATS, candidate experience, hiring manager satisfaction surveys, and monthly expenses
- Gather related CRM data, if applicable
- Conduct focus groups and interviews to understand your employer brand and employee sentiment
Step 2: Create a data story with analytics
According to Gadomski, “Analytics is not reporting. It is reporting and several other things, including metrics and trend lines, a snapshot over time, and context around the information.” It also includes a discussion around understanding where you currently are and where the business needs you to go. Analytics is creating a data story and sharing it with stakeholders.
Step 3: Match your data to business outcomes
Data for the sake of data isn’t necessarily compelling to business leaders. Data for the sake of driving business outcomes or analyzing the number that matter most to your business—that’s compelling! Once you have your data story, align the presentation of your analysis to things that are important to the business, such as training or diversity.
Step 4: Have a portfolio of things that you measure
Nothing is easy when it comes to data; there are things that are easier to measure, then more difficult, and then the most difficult. Master the easiest measures first, before moving to the more difficult ones. It’s also important to look at the context and relationship of the data. “Within talent analytics, we have to pull together different categories of measures to see how they correlate,” states Gadomski. “For example, you can’t look at employer branding metrics in a vacuum. You have to look at them in relationship to others.”
“Data also drives change,” LaMotte points out. “When it comes to outcomes, you’re not just looking for successes, but areas of opportunity. When we measure ‘source of influence’ qualitatively for clients, we have real evidence that can change job board spend for example.”
Now, that’s a measurable impact from change.
So, what does all this mean?
The bottomline: looking at source of hire in isolation isn’t the best way to understand employer branding’s impact or measure the return on your recruiting investments. As an industry, we have to get beyond source of hire to truly understand our efforts. We also have to look at measures over time to understand which factors are influencing candidate behavior, application quantity and quality, and recruiter activity. “Look at changes in your data,” recommends Gadmoski, “evaluate whether it’s isolated, seasonal, or a pattern and then see if there is anything that you should be doing differently.”