Web Analytics and how Recruiters can benefit

What are you doing?
If you want quality improvement in your online recruitment.

Of course this topic is about Recruitment, I will just be highlighting some marketing aspects of this profession, discussing Web Analytics and how Recruiters can benefit.

Web Analytics is the study of online behavior in order to improve it.
(More here http://en.wikipedia.org/wiki/Web_analytics)

Using Web Analytics we create awareness for ourselves on how candidates surf our site.

Web Analytics software is able to tell us where candidates come from, how they navigate through out site before applying for the job. Also Web Analytics software is telling us where candidates exit our site… that of course we don’t like.

How can we apply this Web Analytics knowledge to our Recruitment strategy?

Check this: Accountability and Create checkpoints
E.g. What money did I spend on traffic generation? And what is the ROI in: number of visitors, most valuable channel, conversion in applications.

If you already use an Applicant track and trace system, integrate your Web Analytics software with your ATS, this way you’ll get the best picture of your efforts out there. Note, be sure you use same Web Analytics software on your career site as you use on your company site.

Since we get a clearer picture of what we do and what they, the candidates, are doing with our online presence, we are able to move forward.

Now that we know what we spend, what quality and in what quantity each channel is able to bring us. We can build a roadmap for each and every job.

We are able to improve quality of our online recruitment.

Want to learn more on this topic?

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2 thoughts on “Web Analytics and how Recruiters can benefit

  1. Monitoring web analytics is vital for every online business, however often statistics are often overlooked.

    It was interesting to read your own post in relation to recruitment and how quality can be improved.

  2. Very interesting post. You are right that the implementation of web analytics is the same, but the key still remains on how you apply what you learn from the stats.

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