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Despite the benefits, many organizations still struggle with data driven talent acquisition. How do you put the right data at the core focus of your organization? And, what can you do to ensure your data-driven approach will be successful? These are questions we hear a lot. We hope to provide you with an answer in a sequence of three blogs. First, back to basics: “Why is data important?” Including tips to start immediately with recruitment analytics and grow in the data maturity of your organization!
Being proactive through measuring data
“Why is data important?” It seems like a simple question, but do you know the answer? To demonstrate the answer we like to use the example of Fitbit. You probably know the Fitbit, this smartwatch measures your movement activity. Fitbit determines that their users take 43% more steps than non-users. Why? Because determining a goal and continuous data revolving the progress are a driving factor behind a behavior change.
This process is the same for recruitment. Wouldn’t it be great to reduce your time to hire with 43%? Proposing SMART (Specific, Measurable, Acceptable, Realistic, Timely) goals and reporting on progression increases the success of achieving this goal. It sounds obvious, but still do many talent acquisition departments miss out on a data-driven approach.
Without a data-driven approach you operate on gut-feeling, a stage that we see as a phase at the bottom of the model of data-maturity; nothing is measured and people completely act upon gut-feeling. We need to step away from this approach as we want to move towards the proactive and predictive stages to grow on the model of data-maturity.
(image: model of data-maturity)
So, let’s start at the beginning:
How do you start with recruitment analytics?
To start effectively with recruitment analytics it is important to first think about the data you want to gather in each phase of the recruitment process. We like to divide this process in the talent journey, consisting of 4 manageable steps: Reach, Engagement, Apply & Hire.
In a phase of Reach you are looking for the visibility in Google, impressions from a campagne and for example the amount of website visitors. In a phase of Apply you are curious to know the amount of applicants, but also the quality of these applications, based on engagement actions they performed on-site. The quality is also determined by the following steps they perform in the application process, where you can connect to previous behavior of visitors on the website (which source, type of device, landing page? etc.). You can conclude quickly: we need to connect systems to gain insights to the answers we are looking for.
Connecting systems – we hear you think – is easier said than done. In reality connecting an ATS seems to be something really complicated for many organizations. This can cause organizations to be stuck in the reactive phase; website data and ATS data are evaluated individually in retroaction. Besides, we see that this evaluation often happens afterwards, when it is too late and changes are not possible anymore.
Climbing up in the model of data-maturity
To make a step towards proactive acquisition, a third step on the model of data-maturity, it is most important that the different phases in the talent journey are connected to each other. By connecting the website, ATS and HR system you provide insights throughout the whole talent journey. This provides opportunities to proactively use data.
For example: When the traffic to your site is low in the first phase of the journey (Reach), it is already in an early stage clear that the hiring manager cannot perform any presumptions in the last phase of the journey (Hire). Time to take action! But, preferably not by a boost of traffic from a job board: we know that this causes a high rejection-rate for specific types of vacancies.
Sounds good, right? In the next blog we share which KPI’s you need to get insights in to put the above example into practice.
Predicting the quality of a candidate
The moment you effectively connect all your systems and proactively start working with the available data, it is time to think about the next step: predictive analyses. How do you start with gathering a large data set to make sure you can make predictions for the future based on this data? The fourth step in the model, the predictive phase, helps you to predict the quality of an applicant before they enter your ATS.
In our last blog of this sequence we will tell you all about predictive analytics. Stay tuned!
Do you recognise yourself in the gut-feeling or reactive phase? Contact us and start with recruitment analytics today!