As an HR or talent acquisition professional, you understand the import role that analytics play in online recruitment today. However, if you’re solely relying on source-level data as a key performance indicator for the jobs you're advertising, then you’re likely missing the bigger picture that your full recruitment marketing analytics are trying to paint.
When we talk about “full” or “deep” recruitment marketing analytics on the Recruitics Blog, what we’re referring to is a combination of source- and job-level analytics.
As a quick refresher, source-level data provides you with insight into how all of your jobs are performing on a given job board or aggregator. Job-level data, on the other hand, helps you understand how a specific job, or group of jobs, is performing either on a single source, or across multiple sources at once.
In order to run a successful recruitment marketing strategy based on data-driven insights and not simply intuition, you’ll need access to both--specifically because source-level data alone can often be misleading. And, as we’ve discussed in previous posts, misleading data can negatively affect your strategy by influencing future decisions based on incorrect or inaccurate insights.
Here, we’ll use case study data to show you two specific ways in which source-level data might be misleading and the impact this can have on your overall recruitment advertising strategy.
As a note: While all the data below is real, we haven’t included our clients’ name or details as a means of protecting their information.
In this sample of client data above, you’ll notice that the source producing the lowest CPC is Indeed. And, in this particular case, Indeed is also the source producing the lowest CPA. But, don’t mistake this correlation for causation. Simply put, it’s not always true that the source which produces the lowest CPC will also produce the lowest CPA, or vice versa.
For example, take a look at the second lowest CPC source: Jobs2Careers. What you’ll quickly notice is that that Jobs2Careers has the highest CPA! That’s because this source, for this particular client, is converting traffic into applicants at the lowest rate (meaning many more clicks are needed to produce applications on this source, compared to others).
On the flip side, in the client data above, the highest CPC source is Glassdoor, which also happens to have the second lowest CPA. Again, here we’re seeing the impact of a high conversion rate (but note that conversion rates on some sources are not always comparable, as we discussed last week).
Differences in conversion rates may be impacted by what jobs are being advertised on each source, and on what days/times those are jobs are showing. There are a variety of other factors that can play into the performance of a job advertising source on the whole, as well.
But in any case, the point is that you can’t judge a source solely on its CPC. For example, if you were making budget decisions based on CPC alone, you might consider cutting Glassdoor here, given that it has the highest CPC in our dataset, by far. However, if you made that decision based on CPC, you’d be making a decision without seeing the entire picture. In fact, if you were to cut Glassdoor based on CPC alone, you’d be losing your second lowest CPA source.
Let’s take a look at another sample of client data. (Again, we’ve anonymized our client to protect that information.)
In this dataset above, you can see that the source with the highest conversion rate is Glassdoor. However, upon further inspection, Glassdoor also happens to be the source producing the highest CPA.
On the the other hand you might notice that Monster, while producing the lowest conversion rate among our five sources, is at the same time producing the lowest CPA.
This, once again, represents a case study for why you can’t simply rely on one metric to determine the effectiveness of your sources. Here, if you were looking at conversion rates as a measure of performance and needed to eliminate a source, you’d most likely eliminate Monster. However, in doing so, you’d be losing one of your most cost-effective job advertisements--given the source’s CPA.
As described above, making budget decisions based off of singular, source-level metrics is risky, to say the least. That’s because when you’re looking at source-level data, you’re only seeing aggregate data based on the performance of all of your jobs on a given source. Source-level data doesn’t give you insight into how an individual job, or an individual group of jobs, performs on a given source versus an alternative source.
Rather, the most effective budget decisions are reached when you have the ability to look at job-level analytics, as well. By having access to the performance data and associated cost metrics for every job on every source, you’ll be able to answer important questions like “What’s the best source to advertise Job XYZ on?” And, “Which types of jobs get the best results on Source ABC?” Accordingly, answering questions like this will help you make more informed and smarter budget decisions.
Further, if you have a recruitment marketing analytics dashboard that allows you to track job- and source-level data from the click through to the hire, or End-to-End Analytics (E2E), you’ll have an even deeper understanding of quality - both the quality of your job advertising sources and the costs associated with acquiring quality applicants. Having access to E2E Analytics allows you to go beyond understanding which jobs or sources are producing the best CPC/CPA and conversion rates, and it also allows you to understand which of those jobs or sources are actually producing quality applicants and hires, and at what cost.
Don’t have access to source- AND job-level analytics? Get this data for FREE by signing up for Recruitics Analytics, our free recruitment marketing analytics dashboard.
Or, to learn more about Recruitics E2E Analytics, sign up for a demo today.
![The End-to-End Recruitment Marketing Analytics Funnel [INFOGRAPHIC]](https://info.recruitics.com/hubfs/THE_END-TO-END_RECRUITMENT_MARKETING_ANALYTICS_FUNNEL_INFOGRAPHIC.jpg)
