The Candidate Data Infrastructure – Part 4 of Transforming Recruitment Marketing

This is the fourth in a five-part blog series on how to transform recruitment marketing to ensure it creates impact and value for your organisation.

Technology is both the why and how for transforming recruitment marketing. Seven years ago, when I first dipped my toe into the talent acquisition space there was a sense that change was on the horizon. Recruitment marketing was emerging as a discipline and the practice of building proactive talent pipelines was moving off of excel spreadsheets housed on recruiter and sourcer laptops to enterprise CRMs that allowed talent teams to collect and advertise to both passive and active candidates through digital channels like automated emails, job alerts and social posts. In those early days of the talent acquisition technology stack evolution the focus was on how to reach and market to candidates. We were all seeking the elusive answer that would allow us to close the ATS black hole, and make candidates feel like they were not forgotten forever. Progress was made. Recruitment marketing became a recognized discipline in the TA space and while we were all working on becoming better at how to engage our talent pipelines, recruiting technology continued to advance. Today we face a technology stack that not only allows us to market to candidates but also allows us to apply various forms of artificial intelligence to identify, match, assess and even select candidates. It is this transition to talent technology that applies intelligence to decisions made in the recruiting process, that has pushed open the door to the next revolution in recruitment marketing. Today it is not enough to just capture and market to a talent pipeline, recruitment marketing must serve as the vehicle to ensure talent intelligence solutions have the candidate data needed support recruiting decisions. Good news is that by applying what we have learned about how to attract, engage and manage talent pipelines recruitment marketing is in the perfect position to meet the challenge leveraging the vary technology that we now need to serve. For more on how I see recruitment marketing meeting this challenge see my first article in this five-part series, Transforming Recruitment Marketing. So far I have discussed how to create and leverage a candidate data map, and how to ensure you know your audience so that you can provide them value in exchange for asking them for more data. In this, the fourth article in this five-part series, I will be talking more about what technology capabilities you should be adoption as a modern recruitment marketer.

Today’s recruitment marketer needs to start thinking about how they will do four things to support their recruiting partners and the TA organization. They need to collect, organize, connect and analyze candidate data. To do these things they will need to rely on their expertise in candidate marketing tactics, audience insights and strong partnerships across the talent acquisition team.

The first paradigm shift I am proposing is in how we think about recruitment marketing and redefining the recruitment marketing why and thus the output of a good recruitment marketing. Today’s recruitment marketing needs to be less about pushing information to candidates and more about collecting information from candidates. Let that sink in for a moment. What do you measure your current recruitment marketing campaign success on? If you are relying on success measures that tell you how many eyeballs you had on a campaign or how many clicks you received, you are missing a key opportunity to understand how effective your recruitment marketing was in eliciting candidates to share more information about themselves. Here is why this is critical. In the past when the goal was to get as many candidates a possible to complete and application, making them visible to recruiters so they can assess them and determine which ones to move forward in the process, recruitment marketing was focused on creating applicant volume. As more technologies enter the talent acquisition ecosystem that include AI algorithms for matching applicants to opportunities, the recruiter is less impressed by how much volume you brought to them but instead be how many relevant and “matching” candidates you wooed into the process. This has become a quality play not a volume play. Recruitment marketing needs to be able to collect as much information as possible about candidates in a way that increases a candidate’s engagement and generates a sense of relationship and trust. So, just increasing the number of questions you put on a talent form or number of talent forms you insert into your process will not provide the results you are looking for. You need to leverage technology solutions that allow you to collect information form candidates in a way that allows them to both feel like they are getting something out of the transaction and that leaves them with a positive impression of your brand. This is the ideal place to insert the use of SMS / text, conversational AI and other technology tools that allow you to mimic a conversation with candidates. These technologies are your interaction layer, and their purpose is to ease the way into candidate data collection. 

In addition, you need a robust parsing layer to apply to your candidate data. The challenge is that not all information that is collected from candidates is easily digestible by your technology systems. Therefore, you need to make sure your parsing engines and capabilities are keeping up with the way candidates are providing you with information. Parsing resumes has been commonplace for years. This now needs to evolve to parsing chat transcripts, assessment outputs, video submissions, etc. 

As I noted, the introduction of robust CRMs to the talent acquisition technology stack was a boom over the last six or so years because they allowed talent acquisition teams to not only have visibility to active candidates, but also passive ones. These CRM tools all allowed for some capability to sort or organize candidates, usually so that you could most effectively engage and market to them based on audience differentiators. When we start to think about the value of a candidate or contact in our CRM being directly related to how much we know about them or how easily we can make them leverageable by recruiters to match them to opportunities, it becomes clear we need to rethink how we set up our CRM fields, statuses, lists and contact records. The new infrastructure needs to ensure the candidate data is in a format that can be accessed by the matching systems as well as ensure you as a recruitment marketer can collect the right data for the algorithm to make the best matching recommendations. 

The truth is the CRM is most likely not going to be your only candidate database. Many companies separate out their passive candidates in the CRM and active candidates are in their ATS. Sometimes there are even more databases that house internal candidates or referrals or early career talent. When the goal was around how to market to these specific populations, then having them all in different systems with different data fields that may or may not be the same as what you collect from other candidates was less important. In today’s ecosystem however it is essential the matching AI systems can not only capture all of those various candidate types in their integrations, but how that data is collected and organized will directly impact the systems ability to compare and leverage information from various locations. Solving for this data quandary requires you have a comprehensive data management plan. What data on candidates do you collect where, how will you organize, label and keep it accurate? The exercise in my article on candidate data mapping, can help you start this organization process by providing clarity on what candidate data you need, who needs to access it and where you are or want to house it. This in the end allows you to create a candidate data organization and architecture. You need this to be able to know when and where you need to create candidate data connections across your talent acquisition tech stack.

Starting about three or four years ago talent acquisition teams started to see an increased need to connect the data they had in their CRM systems with their ATS, their assessment technologies, candidate survey tools, and various marketing engines that supported everything from social media engagement to events. There was a need to connect systems to better have visibility into who had been captured in the talent acquisition process and how they had been engaged with. The introduction of candidate matching systems, that need candidate data to apply their algorithms against and output the best available talent in the overall pipeline, has changed the need for integrations so that they are about more than just tracking status or engagement and instead now serve the need of ensuring system integrations are focused on what we know about a candidate.  In addition integrations need to support teams in knowing that candidate data housed in one system is reflected accurately in another where a candidate action may need to be taken. There is no getting around it, your technology stack needs to have robust integrations that are thought through to ensure they allow you to create seamless experiences not just for your candidates, but also for your recruiters.   Nothing impacts a recruiter’s trust in your recruitment marketing efforts faster than seeing the information coming out of your CRM is different than that they have in the ATS system. Ensuring these and your other systems talk to each other is critical.

Knowing what information to integrate across which system however is not as simple as just connecting every data field. You need to know how the information will be used. You are, remember, talking about candidate data. Candidates have data privacy rights you need to account for. In addition, overloading a system with data that is not needed to support the actions it is meant to take will only slow down its operating speed and create confusion around what to do with all that data. You need to leverage your candidate data map, data architecture and clearly plan to house the right candidate data in the right systems and then give the right talent acquisition team members access to it. The goal here is purposeful data management.

Finally you need to start investing in effective and reliable artificial intelligence engines for your technology stack. Once you have all of this candidate data, various usage modes for strong artificial intelligence capabilities will allow you to use the data to make decisions around everything from who to connect with, how to connect with them, who to match to what opportunities and how to get the most value out of your pool of talent. For that to work you need your data architecture to understand what data the algorithm needs and make sure you are creating the right data integrations to make that happen.

The promise of this new age of talent technology is a future where it will be possible to leverage these artificial intelligence tools to help talent team make talent-based predictions and have the capability to act on them. This new era of predictive modeling is starting to emerge, and to be ready for it, talent acquisition teams must get their technology candidate architecture stabilized and ensure they have the right technology tools and capabilities in their tech stack. Not only do they need to clearly understand what data they will need, but how they will use the art of recruitment marketing to build relationships with candidates in order to collect that data over time.

Stay tuned for part 5:  Replace Nurturing with Purposeful Engagement Plans

Cover image: Source

This article first appeared on LinkedIn on 25 January 2021.

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