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Identifier 101: How to Use Different Types of Identifiers in Recruitment Marketing

Data-Driven Intelligence, Programmatic| Views: 1334

Analytics have become increasingly important for running recruitment campaigns. Now recruitment marketers can better understand the behavior and actions of potential job seekers through data, which can be used to optimize processes and increase chances of hiring success.

Tracking candidate behavior through the use of identifiers is one key aspect of recruitment marketing analytics. Identifiers, in their different forms, allow for the collection of data that informs recruitment decision-making.

While the end goals of tracking vary between hiring organizations, the most common desired outcomes include a) pinpointing the most successful recruitment sources and their associated costs, and b) eliminating bottlenecks in the recruitment funnel.

Types of tracking identifiers and their use cases

In order to get the most out of applicant data, recruiters must understand the types of identifiers and how they are used. The data these identifiers return helps paint an overall picture of job seeker journeys, so that recruitment marketers can amplify campaign strengths and minimize weaknesses accordingly.

Here, we’ll summarize the most common identifiers that track actions of users (or job seekers). They differ not only in how they track actions, but in their level of granularity. It’s important for recruiters to select the level that works best for a given recruitment task. The amount of details in the gathered data may also vary according to a recruiter’s unique setup or the depth of integration with publishing partners.


The least exact identifier, a timestamp, provides a glimpse into a day’s worth of interactions (e.g. clicks, pageviews). It references the exact time an event or action – such as an application submission or an ad click – occurred on a website.

Use case: To determine how many clicks or actions occur on a given day. This could be valuable for assessing the cost-effectiveness of a recruitment campaign between multiple days.


This is the identifier that groups together any number of interactions that occur within a specific time frame, on a specific website and on the same device browser. The default session time frame in Google Analytics is 30 minutes, which means that a sessionID expires after this time. If another interaction occurs after the time is up, a new sessionID is formulated.

Use case: To determine how many unique sessions have been created. This identifier doesn’t give insight into the number of unique users, but rather the amount and frequency of activity from a particular source. Valuable in a similar way as a timestamp, the sessionID could be used for making volume-based assessments about a given time frame.


A clientID is an identifier string that is created and assigned based on a Universal Analytics Cookie. While a clientID represents a unique website user, it is not able to attribute activities from the same user across multiple devices. In other words, a clientID can only exist on the device or browser on which it was created. This poses an issue if many users – or job seekers – are accessing a particular aspect of the recruitment funnel across multiple devices. Tracking results using a clientID across multiple devices shows up as two users, when in reality it is only one.

Despite this shortcoming when it comes to determining unique users with a strong degree of certainty, the clientID allows for the measuring of device-specific job seeker data.

Use case: To see how many, or which types, of devices or browsers are used to perform specific actions. Job seekers may click on specific types of ads more frequently on mobile, for example. Recruitment marketers can then utilize this information to optimize ad placement.


Unlike a clientID, a userID is not generated by a cookie; it is created via a login system for a particular website. Because it works only when a user is logged in – and because this login can transfer across devices and browsers – it allows for the true measurement of unique users. Any job seeker who is logged into a website and performs multiple actions across devices is accurately represented as a single user in tracking data associated with a userID.

Use case:To see how many unique users have performed certain actions, and how specific users behave across the job seeker journey. Being able to track a unique user’s behavior across recruitment and application processes is extremely valuable for companies looking to get inside the head of their job seekers.

Bottomline: Utilizing various identifiers is a smart means for companies to get ahead in the talent wars.

Tools such as a Demand Side Platform (DSP) can help integrate this best practice into existing recruitment processes. Hiring decision-makers have plenty of flexibility to select the right identifiers that work for any particular task at hand; the secret lies in choosing the appropriate granularity of identifier.

When it comes to all aspects of a hiring campaign – from cost evaluation to candidate sourcing – identifiers serve as a game-changing mechanism to help companies get ahead of the competition.

About Spencer Parra

Spencer Parra is the VP of Product Management for advertising and data products at Radancy. In that capacity, Spencer and his team of product managers, program managers, data scientists, and data analysts work to develop products in a data driven mindset. As the leader of Advertising products, he works to bring a holistic full funnel approach to Radancy’s advertising technology stack with Programmatic Jobs at its foundation. Through data products, he tells the story of media performance via Radancy’s Metrics Gateway and helps ensure data is democratized through Radancy’s unified platform. Spencer came to Radancy from the Perengo acquisition in mid 2019 where he served as Lead Product Manager and a member of the founding team. With Perengo, he worked towards the vision of leveraging the same rigor and concepts from ecommerce advertising technology to the recruitment advertising space. Prior to Perengo, Spencer launched and supported in-app advertising products at Criteo as a solutions engineer. Spencer holds a B.S. in Aerospace Engineering from the Massachusetts Institute of Technology.

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