How to use data to achieve better outcomes
The right data → the right decisions → good outcomes
How do you know you are using the right data to drive the outcomes your company needs?
I was in a meeting recently where we were discussing the completion of a task to track a significant amount of user behavioral data within our application. We started throwing out all sorts of insights we could gain about our customers, and how we could transform these insights into meaningful product improvements. Within minutes, it became clear that we had a new problem on our hands — It would take years to accomplish the ideas generated in our short discussion, even if we were to 10x our resources.
I’ve spoken with many HR leaders who are experiencing the same problem. Investments in digital transformation and new technologies have resulted in an overwhelming amount of data. But simply having access to more data doesn’t result in improved company outcomes. An undisciplined “bottom-up” approach to sifting through data to find what can be useful in driving better decisions and better outcomes is a costly guessing game.
The most effective HR leaders use outcomes to choose which data to leverage.
The right data to make the right decision depends entirely on the desired outcome. In working with many HR leaders at successful companies, I’ve mapped three common HR-related outcomes to the types of data needed to be successful in making the right decisions to improve these outcomes.
Hiring the right candidate
Hiring the right candidate is one of the most common, yet most important outcomes contributing to the success of any business. The hiring process is simply a data-collection process to aid decision makers in making the right hire. One of the most common mistakes in this decision-making process is using data points that don’t actually correlate with job performance. In order to isolate which data points actually correlate with better job performance, take your highest performers and lowest performers and create two data sets, looking at their job applications, resumes, and pre-hire assessment results. Analyze the data sets to determine which data points actually differentiate between the two groups. This will illuminate the data points that you can use in future hires to bring in more candidates that model your highest-performing employees.
Reducing recruiting costs
Once you’ve optimized your hiring process to increase your chances of each hire leading to a rock-star performer, you can focus on improving your ability to recognize these candidates sooner. This will result in spending more time and resources on candidates that you will eventually hire and less on those you won’t, reducing your waste and overall recruiting costs. Similar to the step above, create two simple data sets consisting of the candidates that were hired and those that weren’t. Finding the common characteristics and backgrounds that are distinct to those that were hired will help you make decisions about where to spend your recruiting efforts and how to create more effective messaging for future opportunities. For example, in hiring for software engineering positions, we found that the majority of those that were hired vs those that weren’t regularly participated in local or online developer communities. This helped us develop a strategy to focus on sponsoring and speaking at these developer communities, while reducing spend on other areas that haven’t been as fruitful. This has significantly reduced our overall recruiting costs in finding rockstar developer talent.
Improving customer satisfaction
A fantastic customer experience and high customer satisfaction has a direct, positive impact on a company’s overall success and growth. This is why customer satisfaction scores are often one of the top performance metrics for customer-facing roles. When discussing hiring the right candidate above, I emphasized isolating the skills that are predictive of high performers and leaning heavily on those in the hiring process. Taking this a step further, many companies have already identified a short list of skills that lead to improved customer satisfaction. These include polished speaking ability (especially important for non-native candidates), empathy, and clear communication skills. Some of these skills are easier to obtain reliable data than others. But focusing on measuring these skills in the hiring process (through developing in-house tools or leveraging third party assessments) will lead to improved overall customer satisfaction.
These are just a few of the common outcomes that HR leaders are working towards. Whichever outcomes are most pressing for you, you will be more successful in your decision making by starting with the outcomes you need to achieve and working backward to identify the most relevant data.