At a recent event where I was talking about the online candidate conversion funnel, the audience was giving me a blank look. For many analysing the candidate journey on their websites was a new idea.
The candidate conversion funnel focuses on the page where candidates start on your site, and each subsequent page or step in the journey to finding a job and completing an application. The simple approach is to use your statistics package (e.g. Google Analytics) to recorded number of visitors to each page or step to highlight where candidates "drop off" or give up. It is possible with Google Analytics to set this up using Goals so you can accurately see the journey and be able to split it up by other factors eg mobile users.
Getting some basic (if albeit slightly crude) numbers to show your candidate conversion funnel is not difficult. But what does it tell you and why should you go to the trouble?
1. It gives you the ratio of how many candidates you are putting in to how many complete an application. This ratio is a valuable metric and even more so if you can access this figure by candidate source or job role. It provides insight to which sources of traffic (eg Twitter, Facebook, Indeed, LinkedIn, Google) delivers candidates that are most likely to apply. All these sources have a direct or indirect cost. It provides you a base line to forecast your marketing activities to fill the vacancies you are expecting (assuming you have a grasp on number of applications to hire).
2. Normally the volume of candidates to look on your site is far greater than the volume that apply for a job. There is nothing wrong with this. However what if your advertising message is getting people to click apply and they are then dropping off. This is more alarming. The candidate was interested in the job, and may be a great fit but the process drove them away. For some roles the process might be designed to filter candidates but for others this is a great concern. Knowing what is happening, at role level (or function level) gives you the information you need to know where to make improvements.
3. Where you have hard to fill roles analysing the funnel may be less about the application process and more about the advert content. If you are attracting (paying for) candidates to read those hard to fill roles but they are not clicking apply you need to know. With the right technology behind your website (and mobile site) you should be able to "test" different adverts or job title to learn what will attract that hard to get talent. When doing such tests you need a technology that support A/B testing or MVT. This means during the same time period some of your candidates see one advert while others see another version of the same advert. The platform then crunches the numbers and shows you which performed best. Ideally you should be able to take a look at the candidate applications to manually compare to ensure the quality matches the volume. If you do such a test without A/B testing or MVT technology your results wont be captured during the same time period. This means the results will be very unreliable as so many other factors may have influenced the candidates to click apply. I have analysed website performance and found the weather can impact the volume of conversion or how well a sports team performed.
When we start thinking about contextual content this level of analysis shows the value you get by providing relevant content.
Without the insight to the conversion funnel you have no idea where your candidate journey needs your support. The first step is to capture the data.