Imagine you’ve just got the bill for your meal and realize you’ve been charged the same amount for your water as you have for your lobster dinner. You might scratch your head and wonder why a restaurant would charge the same for vastly different things, especially when the water is normally free, or when lobster is much more difficult and costly to prepare. Now imagine you got charged every single time your water was refilled—and most of your money was spent on water! This is what it feels like when you have no control over your job advertising spend.
Whether it’s the cost-per-click (CPC) model of job aggregate sites like Indeed, or if you’re handing over a lump sum for job slots on one site like LinkedIn, you’re treating all your open jobs, from the V.P. to the sales rep, with the same strategy. But different positions attract an array of candidates who search for jobs differently. The best recruitment marketing approach to maximizing job advertising spend is to diversify that strategy and target the sites that will reach the best candidates for each specific job.
Why your money is not spent efficiently
Consider the hard-to-fill index. A job with truly specialized skills has a smaller candidate pool. Those “hard-to-fill” positions will get fewer clicks on a site like Indeed, simply because of the nature of the position. When you have a “hot job” that is popular, it will be easier to fill no matter what. But you’ll also get way more clicks and an excess of applicants; in a CPC model you’ll be paying more for the job ad that is easier to fill. Kind of like paying extra to fill that glass of water? That’s hard to swallow. And it’s just one of the ways ad spend is currently inefficient.
Another way dollars are lost is by not measuring the window of effectiveness for a job ad. If 95% of job seekers see your ad within the first week, then your budget is wasting money each week as the viewership of the job ad decreases. While you may be using important metrics like cost per hire or using historical data from your organization to find the sites that have worked best for your job ad campaigns in the past, the metrics to measure job ad effectiveness go beyond this. It would be better to know the historical data from job ad boards and have data from thousands of companies, not just one; it would be vital to understand the changing trends on how each job seeker searches, and where the skilled employees are for specific jobs. You would need to do something not humanly possible.
How AI can streamline your process and your costs
So do HR teams need to transform into data scientists? Job advertising is generally about one-third of your recruitment marketing budget. Right now companies are not utilizing the vast amounts of data available to determine the best-targeted campaign, wasting up to half of those ad spend dollars.
Just as the process has changed from placing single ads manually in newspapers or on a single site like Monster.com to a more automated process with Indeed, the technology now is changing for the entire ad campaign—from manually-monitored to fully-automated. AI-enabled technology like PandoIQ, which collects data from thousands of companies and job sites, can effectively utilize this data to your advantage – all automatically. The strategy isn’t determined by a person, it is determined by more data than any one team of HR professionals could manage.
The complex set of algorithms use machine learning to create the strategy—the times, the sites, and allocating job advertising spend to optimize the process across every job in a given period, able to both predict and monitor the effectiveness of every dollar spent across the board. In essence, it makes the process effortless—and spends the budget in a smarter way to maximize the job advertising spend—so that all job ads, from the VP to the sales rep, are performing and getting you the best cost per hire across the board. It’s all about the numbers. If you’re not monitoring them and using them to your advantage, you’re wasting those ad spend dollars. None of this is possible without leaning on AI, which takes the guesswork and manpower out of researching and trial and error.