What can data tell us about jobseeker behavior? We’re about to find out. Let’s look at our Employment Platform Portals (EP Portals), which include an Activity log feature that is used to record a jobseeker’s work-related activity, tracking their employment journey. 

The information logged in this feature is not restricted to typical metrics such as page views and click-through rates. Our behavior analytics allow us to track job search activity including time logged in, time spent searching for a relevant role, and also the time that elapsed between finding the role and starting and submitting an application. 

This data informs you of the user’s interest level in specific types of results they receive after performing a search or being matched by the system. It also lets us know if the user finds the result relevant and useful enough to pursue them further. 

We know that our customers need the security and flexibility to analyze their end-user data according to their own requirements. This is why you can fully customize any data recorded in our EP Portals’ Activity feature and set up regular data syncs that will export the information to other secure locations.  

WCC has vast experience in public employment, and we understand that user segmentation requires additional rules to accurately measure business-specific goals. Common metrics such as conversion and retention when used in commercial systems, have different significance when applied in the public employment domain. For example, if a jobseeker submits a job application, this is considered a conversion. If the same jobseeker, then returns to the system to submit two further job applications at a later date, this is a measure of retention.  

In terms of relative importance, retention metrics are more valuable when they signify a user’s motivation to gain employment. This is extremely relevant data for a PES to capture, to aid them as they attempt to understand the motivation levels among their user groups.  

WCC’s EP Portals use a rich taxonomy to structure data according to domain knowledge. This extends to the activities that users perform too, allowing the computation of meaningful performance indicators. Using a combination of relevant taxonomies, we are able to track each user’s journey from their activation, engagement, and retention metrics.   

This provides a bridge to follow the user journey through our EP Portals. A funnel representation of the user journey within a sample time frame shows how many users login versus how many proceed to start a job search. Subsequently, we then know how many of these users move forward to finally submit a job application. Other important metrics our EP Portals can supply, for instance, include the number of jobseekers who successfully secured a job during a defined timeframe. It is possible to confirm whether this was achieved by using the job search function, or if the candidate was matched to a job via our portal. This enables comparison between jobseekers marked as successfully hired in our system who used the job match function, rather than search. By slicing and dicing data, we can gain insight on how user actions relate to the all-encompassing goals of public employment services.   

Understanding user behavior gives us actionable insight into user thought processes that can inform UX design, plus the functionality that produces a positive result. As a committed user-centric product, we will guide the user towards whatever process works best for them individually. We also remain committed to further investment into UX research and cultivating an experimental culture behind our products.  

Behavioral analytics is not just about numbers, it’s also an emotional journey where user-centricity is front and center of everything we do. At WCC we aim to listen to users and respond by providing engaging content that captures their attention and supports their successful employment journey.