The quality of candidate data is central to the experience that the recruiting team can deliver.
The issue is, most organizations aren’t always aware of how poor the quality of their data is, be it for candidates, prospects, employees, products…
Ta leaders may not always have data analysts on their teams, but they can help their teams set up processes and good practices to help maintain the quality of their candidate data.
Why does candidate data quality matter?
We live in a fluid talent market. Not only are jobs open for longer–the mean vacancy duration in the US in June 2017 was over 30 days–but information about roles and employers is also more available, and candidates themselves are less constrained by geography or language.
All candidates have more options than ever before, and can afford to be selective, especially high performers and candidates with scarce skills. In such a competitive market, every interaction between the talent team and their target candidates matters, from the first time they land on your Linkedin page or see a job ad from your company to the day they send back an offer acceptance.
This is why recruiters have been upping their talent engagement game, and getting closer and closer to the levels of personalization and delight that consumer brands offer their customers.
Recruiters are picking up consumer marketing personalization practices...
Consumer marketers have found a lot of different ways to collect and use publicly available data. They use social media profiles, sign-up forms, newsletters, website activity, free wifi sign-ups in retail locations, and a myriad of other behavioral data points.
The data is collected, aggregated, analyzed, and then used in a few different ways:
- To predict what customers are likely to buy a certain product
- To tailor marketing campaigns to specific segments of the population
- To improve customer experience without going through surveys
- To develop new services and products that customers might be interested in
A great example of impressive data use is Amazon. They collect massive amounts of behavioral information, like how much time you spend on a page, or what shows you watch on prime video, as well as account information, like your name or your address.
That data is then used to show you recommendations of products, or to predict sales and shorten shipping times. You can imagine how much value that creates for the company: reducing shipping costs, increasing sales, improving the customer experience… even adjusting prices!
Another old-but-gold example of amazing marketing personalization is Target. Their infamous “pregnancy score” uses buying habits of customers to figure out when they are expecting a baby, so they can target them with marketing campaigns around baby products.
...and they need high candidate data quality for that.
Amazon and Target are only two among hundreds of companies that deliver highly personalized consumer experiences. In FMCG and entertainment especially, the most successful companies understand that the foundation of competitive, omnichannel, hyper-targeted consumer experiences is quality data.
Good candidate data quality is not just about specific demographics, such a first name, date of birth, or last known address. It’s about behavioral information: when was the last time the candidate visited your career site? What job descriptions did they click on? Did they recently update their job title on Linkedin? Did they add a portfolio on Dribbble or an answer on StackOverflow?
How talent leaders can improve candidate data quality
This kind of experience is not possible with bad candidate records and inaccurate data, of course, which is why it’s important to have candidate data that is well-maintained and reasonably error-free.
Most data quality issues are caused by human error, so it makes sense to start with eliminating manual processes as much as possible, and replacing them with automation. A lot of data-related tasks, such as entering candidate data or updating compliance records, for example, can be done with automated workflows using your CRM.
Automation is not the only answer, however, or at least, not a sufficient one. Candidates sometimes enter inaccurate information about themselves, for example, and that is a part of the process that cannot be totally automated. It can be valuable to perform sanity checks at different points of the recruiting workflow.
For instance, set up a time to clean email addresses that have registered a hard bounce every month, and take a quick look down registration lists after every event to eliminate obviously inaccurate records that jump out at a glance.
There are also more sophisticated methodologies of assessing data quality that should be performed at least once or twice a year in every talent team. This assessment method takes only 2 hours from a small team and can help put a metric on data quality. Simply collect the 100 most recently created candidate records from your system, and go over them one by one with your team to identify the ones that are obviously wrong.
For example, you can check names, job titles and locations against linkedin profiles, or manually check that consent information is up to date. The number of records that show no mistakes can be converted into a percentage metric, and you can use it to track the evolution of candidate data quality over time.
Quality is not just about absence of errors, however. Candidate data quality can be judged on three criteria:
Connected: look for indicators that most of the datapoints on each candidate lives in a single system, a single source of truth. This can be achieved by having a central talent operating system that connects to the rest of the talent tool stack with powerful APIs and integrations. Both your IT team and your technology providers can help you identify where you can build such integrations if they don’t already exist.
Searchable: if all your candidate records sit in one place but are not easily searched, filtered and exploited, then they don’t really do your team much good. For instance, if you have a record of your candidates’ last visit to your careers site, but you can’t easily identify those candidates and apply specific actions to them, then that information isn’t very useful.
Your recruiters should be able to set up an automated workflow based on most fields in your candidate database. The search and filter functionalities should be sophisticated and easy to use–Boolean search is a good start.
Compliant: with big data comes big responsibility. The standards for keeping data compliant and private are only going to get higher, so any candidate data that cannot easily be maintained and kept compliant with data protection and privacy laws is essentially a ticking bomb.
The amount of data that candidates share with the world-and with prospective employees- should be used to provide them a better experience. However, it is a staggering amount of information that can quickly become overwhelming and cause more harm than good if the talent team does not manage it properly.
Candidate data quality is central to the talent team’s ability to attract, engage and retain the best talent. Without it, truly personalized recruitment experiences that create delight and engagement are impossible.
A webinar with Amazon: Selecting the Right Recruitment Marketing Technology
According to Aptitude Research, over 70% of enterprise organizations are investing in recruitment marketing capabilities this year. In order to help with this process, Kelly Cartwright, head of Talent Acquisition Technology Strategy at Amazon Web Services, and Madeline Laurano, founder of Aptitude Research, joined us to discuss some of the latest trends in recruitment marketing and key recommendations for evaluating providers.