Google processes 500,000 questions every 6 seconds…
That’s about how long it took you to read that sentence. In the recruitment industry, that equated to over 16.5 billion global searches in the Job Listings category last year, which resulted in 3.2 billion global clicks on ads. That means about 1-in-5 job-related searches generated a click on a Google ad, and that was before Google For Jobs had even launched. Google is getting smarter by the day, and today I want to talk a little about Google’s automation and machine learning, and how that affects our strategies in Recruitment Marketing.
Google has been in the business of automation and machine learning for years, so this is nothing new for them. They have recently been rolling out new features and products to their Google Ads users that enables them to utilize this functionality on a much more accessible level.
Take for example Responsive Search Ads, which released in beta earlier this year. These are a type of ad that takes a large number of headlines and descriptions and utilizes machine learning to create best-performing combinations of the ad. Now, when used in conjunction with regular Expanded Text Ads, it has been forecasted that companies could expect to see up to a 5%-15% lift in CTR for campaigns. Of course, creating compelling ad copy is a huge part of the success of any search campaign, especially when attracting active job seekers. It is important to note here, that these ads are still in beta and may not be available to every advertiser, but the days of manual A/B testing may be drawing to a close.
It used to be that display was considered a more passive way to attract candidates, and to some extent, it still is. When we talk about someone actively typing “jobs near me” into a search bar, that’s as active as you can get. But searching on Google only takes about 4% of a person’s time on the internet, and the rest is browsing content, on apps or social media, YouTube, email, etc. That’s why it’s important to get in front of job seekers on the other various sites they visit around the web.
Another example of machine learning and automation for Google Ads would be their Smart Display campaigns. In these types of campaigns, you provide variations of headlines, descriptions, logos, and images, and Google will optimize combinations of said assets across all properties (Google Display Network, Gmail, and YouTube). When using this strategy for recruitment marketing, we’ve seen a lift in our display strategies that have significantly lowered our cost-per-apply in display campaigns (which typically had a much higher CPA than our typical search campaigns).
There is definitely nuance in the different ways to employ machine learning and automation within recruitment campaigns, especially when running in tandem with established campaigns. You have to know how to pull the levers and when. At Bayard, we are highly trained, certified, Google Premier Partners, and have experience with running machine learning strategies in addition to more traditional recruitment marketing campaigns.