Imagine you’re pulling ATS reports at the end of the fiscal year to help you decide on a wiser, more strategic budget for the next year. You arrange the data on the enormous spreadsheet in front of you as you take a look at it and start to consider where your hires come from. You allocate money for job boards, social media, employee referral bonuses, agencies, and possibly even radio and billboards using numbers and percentages.
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?
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?
Gadomski claims that the source of hire is an antiquated style of thinking that is summary-driven rather than data-driven. Start looking at the rejected applicants rather than the source of hires. Take a look at the vast number of applications who were turned away before the employment interview or offer stage.
This perspective on hiring statistics is rather intriguing! Recruiting teams frequently overlook to comprehend the 90%+ of applicants they did not hire because they spend so much time attempting to understand WHO was hired.
Ask yourself, “How much time, money, and effort did you, and your team, spend on the unhired,” before your upcoming budget meeting. Can you compile trends or a data story to support NOT taking a certain action? What improvements or modifications might result from spending more time evaluating the unwanted? According to Gadomski, “by measuring the AEIOU, we see things lot clearer in how we invest time and be productive.”
How do you build a data story?
Understand that the source of hire statistic is merely one data point in a wider talent analytics story even if you might not be ready to let go of it just yet. You should examine all the data you have access to in order to start grasping the wider picture. How certain are you about this information?
You need a target (your hiring objectives), segments (how you want to slice the data), frequency (how frequently you want or need to measure), historical trends, correlations (how things relate to one another), and time metrics (time to hire / time to start) before you can have a strong data program. The four phases listed below will help you create 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
Gadomski asserts that analytics is not reporting. It is reporting as well as a number of other things, such as measurements and trend lines, an overall picture across time, and information context. It also covers the topic of realizing where you are now and where the company needs you to go. Analytics is the process of telling a data story and involving 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
When it comes to data, nothing is simple; some things are easier to measure than others, which are followed by others that are the most challenging. Prior to tackling the more challenging steps, master the simpler ones. Additionally, it’s critical to consider the data’s relationships and context. According to Gadomski, “with talent analytics, we have to combine many measure categories to determine how they correspond.” You can’t, for instance, examine employer branding measures in a vacuum. You must consider them in light of other people.
“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.”