4 talent acquisition analytics mistakes to avoid

Talent acquisition teams generate a lot of data, and it is often an underused resource. But once the HR department embraces their data and establishes metrics and a system of collection, there are several next steps that require you to fully optimize the data: understanding those metrics, letting the data guide you towards strategic insights, then taking action. Whatever your analytics entails, from basic spreadsheets to a comprehensive AI-driven system, there are a few mistakes to avoid.

Are you making any of these acquisition errors?

1. Being slow to act

Usually, if something works fine, you shouldn’t fix it. But that’s not what analytics is about. Using data to improve your hiring practices on multiple fronts is one of the driving forces behind the movement towards analytics in the HR industry. Action may be the last step, but it’s mentioned here first because it’s the most important.

While a programmatic recruitment platform learns and adjusts to data in real-time using AI, not every talent acquisition team engages such a comprehensive analytics system. Thus, we engage in decision-making that still may not optimize hiring strategies based on the data. So, what is your data telling you? If a job ad is under-performing, you’re not getting enough clicks, or have a poor conversion rate, it’s time to understand why and also adjust in a timely manner; acting on the data in a quick time-frame will ensure you are getting a better ROI in your hiring campaign. You can’t simply sit back and watch—you have to take the plunge to let those strategic insights guide your actions.

2. Not seeing the forest for the trees

Looking too closely at any one data point and not taking in the bigger picture has problematic effects—because that data works in a larger ecosystem. For example, what’s happening beyond your organization? What’s the competition doing? Looking into hiring trends across your industry when you are competing for candidates can help you explain your own data—whether you are looking at a blip or a trend.

So when it comes to the data within your organization, you want to consider the relationship between data points and not take too narrow a focus. A simple example: if you are only looking at time-to-hire and improving that metric, then you run the risk of upping your cost-per-hire or devaluing your quality of hire at the expense of hiring someone quickly. Because the data is a complex ecosystem, your analytics need to consider how these elements work in concert—this is precisely one of the benefits of a robust analytics program. You can use data to make smarter decisions by considering multiple factors at once.

3. Making faulty conclusions

With a lot of data comes a myriad of interpretations for the hows and whys. That means there are multiple points that could look off or could be misread. For example, in a particular job ad campaign, there are multiple factors that can lead to its under-performance. Are you using the best source for job type? Since different candidates search for jobs differently this is often a factor. Is your application not user-friendly? If more candidates are applying on their mobile devices and your application doesn’t suit that platform you will get less applies. Is the job ad description at fault? Finding the correlations between metrics and understanding how to adjust to get a better performance is key.

4. Using faulty logic

One thing HR teams know above all to avoid is discriminatory hiring practices. Yet, we’ve seen recently how some AI-assisted hiring programs that use machine learning can also “learn” how to discriminate. While one of the main purposes of using data is to avoid decision-making based on hunches or assumptions, analytics on occasion needs some human intervention with machine logic to ensure your hiring practices are not just efficient—but also fair.

On the other hand, the benefit of your analytics is that it can tell you if you are engaging in preferential hiring without intending to; realizing how to alter these practices, rather than replicate this pattern is essential. If you have a programmatic recruitment platform working for you, there needs to be some human intervention now and then.

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