Let’s cover the most important stuff first. I finished off part 1 of my Google for Jobs in 2019 post saying that I was going to take a little time off to be a dad again, which I successfully did! Our daughter Corrine was born on June 13th, at 8lbs 15oz. She and Mom are doing great, and we’re all figuring out our new routine with her big sisters. Ok, now onto the second most important thing, updated Google for Jobs data.
If you recall the webinar that I put together back in May (Google for Jobs in 2019 – here’s link if haven’t seen it yet), part of it looked at some of the initial trends in Google for Jobs data that we had available to us across our network of TalentBrew career websites. By automatically appending UTM Parameters in early March of 2018, Google gave people like myself a gift in measuring the true impact of these search results. Before that time, an organic visit from Google for Jobs looked the same as a visit from Google’s main organic index, even though the technologies powering these search results are completely different.
By automatically appending UTM Parameters in early March of 2018, Google gave people like myself a gift in measuring the true impact of these search results.
Since job description pages can be indexed and returned within Google’s main index like any other piece of web content, this sort of muddied up the trends that we had been able to measure. Yes, it can be assumed that a large percentage of this traffic was being delivered via Google for Jobs, but it just wasn’t possible to say with a certain degree of confidence what that percentage exactly was. That changed around March 1st of 2018, and since that date, we’ve been able to segment out these search results from one another – very much appreciated by digital marketers and analytics professionals.
Since the last webinar was back in May, we had a rather limited year over year data set to compare against. I always like looking year over year whenever possible, since it removes any normal job search seasonality from the equation (always highest in January, dipping into the summer, return for a bump in the fall before falling off in November), but also is a better general comparison for company-specific seasonal patterns.
Things do tend to change a bit year to year, but many companies have similar hiring pushes at certain times. Think the springtime for a company like Lowes or Home Depot, or holiday needs for retailers like Macy’s or Bath & Body Works. Comparing November and December against September and October just doesn’t provide analytics value for the latter, given the large shifts in overall hiring needs. Now, expand these subtleties across a wide variety of geographies and industries, and you can imagine that a previous time period comparison doesn’t provide the clearest patterns.
Once I had the raw data, I made sure to remove any sites that didn’t have google_jobs_apply source data associated with them. I wanted to really ensure that I focused on sites that received Google for Jobs traffic during both years, to make it a clear comparison that ensured:
- The site was actually live during both years, as there were some cases in which we have built out and launched career sites between 2018 and 2019, or alternatively taken some down.
- Google for Jobs was live in that country during both years. Since Google has rolled out this functionality globally over time, it’s naturally been exposed to more job seekers around the world. I didn’t want to include sites tailored to countries that are receiving these search results in 2019, but didn’t have them in 2018.
Using that criteria, I ended up with 238 different career sites to measure performance across (up from 211 back in the May webinar). How did these patterns change with a few more months of data? Let’s break them down.
First, overall users. During the first two months, we saw close to a 90% gain year over year. Through July, users did continue to grow, but at a slower pace of 69.6%. I do think it’s impressive that there were just slightly over 1 million more users than the previous year, and reinforces the transformative nature of these search results. I wonder how many of these million plus job seekers would’ve found that exact same job via Google a few years back? I also think about all of the “real world” results that have improved the lives of so many people, as a result of finding a job or career advancement opportunity.
It’s impressive that there were just slightly over 1 million more users than the previous year, and reinforces the transformative nature of these search results.
Beyond the overall number of users, I wanted to take a look at the patterns of New Users. This is the number of first-time users that have initiated a session in these time periods. I found that both data sets outperformed overall user growth, and the longer time period grew around 3 percentage points more than the overall number.
Again, new users grew by over 1 million compared to the previous year. I think it’s interesting to see this group outperform overall users, which gets me thinking that more and more people are using these search results for the first time globally. Or, can there be correlations with job seekers selecting to go directly to the source of the job more frequently? Definitely food for thought.
Next, I looked at Overall Sessions from this year to last. Google defines a session as ”The period of time a user is active on your site.” So, a single user could have multiple sessions during the timeframe measured. Sessions have continued to grow, again at a slower rate than the first two months, but also almost exactly the same as user growth. I think that this will be an important metric to measure over time, as it will speak towards repeat user behavior from Google for Jobs.
I view Pageviews a little bit differently, as they speak more towards overall user behavior once they arrive at a job page on the career site. Are they finding the right job? Are they looking at other job content, or other available jobs? Both of these timeframes have grown larger than the other metrics we’ve looked at so far, so it does speak to job seekers taking more of an “inside out” navigation approach over time.
It’s interesting to dive into case-by-case situations, and look at the pageview behavior over time, segmenting out Google for Jobs traffic on its own. I think there are some great takeaways in there.
The pattern in overall number of Apply Clicks is obviously one of great interest, since it gets to the root of the question “Are job seekers finding the right job, and then taking that next step?” Within eCommerce, it’s the same measurement of taking that next step to purchase an item. Sure, it may sit in the shopping cart, but it’s compelled that consumer to take that step.
In the longer timeframe measured, we still see a flattening out of growth rate, similar to the other metrics. I do think it’s interesting though that this growth is greater than the overall number of users and sessions during the five month period. This trend supports the idea that job seekers are finding the jobs that they want to apply to more frequently, whether as a result of better overall search results, or an easier navigation of the system over time. If you ask me, it’s probably a combination of the two.
And lastly, the Apply Click Ratio to sessions. This helps standardize the conversion rate between the two time periods, considering there were different levels of sessions and apply clicks in these timeframes. It piggybacks a bit off the overall growth that we saw in the last metric. It’s impressive that over 62% of people that land on a job from Google for Jobs then go ahead to click the apply button there, and this number has steadily grown over time.
Google has yet to specifically monetize these search results, so we’re looking purely at the organic search performance directly to a company’s career site. These metrics above make it abundantly clear that if you haven’t already, it’s time to ensure your own company’s individual job listings are optimized to perform within Google for Jobs. The traffic volume globally in every metric measured has grown by more than 69% over 2018, and the conversion rate is now up over 62%.
These metrics above make it abundantly clear that if you haven’t already, it’s time to ensure your own company’s individual job listings are optimized to perform within Google for Jobs.
Google for Jobs in the EU
Here’s one last little update since my May webinar, and one that I think helps explain a thing or two why the Google for Jobs rollout across Europe has been slower than the rest of the world.
I’d speculated on previous webinars that perhaps the various European languages were holding things up, and that Google hasn’t had the greatest relationship with the EU in recent history. Last week, a colleague of mine from London sent over the following screenshot of search results he was seeing there. I’ve never seen those within the US, but after reading this Reuters article regarding antitrust complaints from 23 job website rivals, it makes a whole lot more sense.
It’s obvious that Google is treading lightly in Europe, and has plans to help ensure that they aren’t dominating the jobs search results. To quote the article, ‘Some rivals allege that positioning is illegal because Google is using its dominance to attract users to its specialized search offering without the traditional marketing investments they have to make.’
Here’s an example of what that search result looked like:
As you can see, Google uses the 3rd part site’s favicon, and links out to it in a bubble type of search result vs. the standard search result format. My colleague confirmed that if you click the links to external job listings, it populates the search that you performed on Google. It will be interesting to see if Google restricts this to European sites, based on EU standards and regulations, or if it’s additional functionality that could be rolled out to other countries throughout the globe. My gut tells me that we’ll only see this in Europe, and that Google prefers to show the Google for Jobs structured data that their machine learning algorithms are geared towards.
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