The most important data analytics for HR professionals
A guide to incorporating analytics into HR practices to benefit your business
Human resources, like the name suggests, is often seen as soft-skill and people-oriented. However, drawing on data and analytics enables HR professionals to most effectively carry out the department’s functions. Using software and automation tools allows staff to focus more attention on people and their experiences, rather than administrative tasks such as paperwork and record maintenance. Implementing analytics into ongoing HR practices allows your team to make more informed decisions without losing the people-centric essence of HR.
Currently, widespread HR practices tend to track what I call “symptom” metrics such as turnover rate, acceptance rates, absence rates, revenue per employee, and others which provide objective accounts of the workforce. However, further analysis looks towards “cause” metrics to address the root cause of the symptoms. Since people themselves are the biggest part of a team, optimizing HR functions creates a substantial impact on a company’s ability to achieve its strategic goals. I've broken down this guide into 3 areas to incorporate data analytics: Recruitment, employee experience, and turnover.
Before getting started, however, it’s important to be aware of the pitfalls of the technology, and carry out due diligence on suppliers and products. Even though integrating AI and automation in recruitment can help facilitate data analytics, HR software has the potential to be biased and discriminatory. Amazon eventually abandoned its machine learning based recruitment software that secretly showed bias against women by penalizing the word “women’s” in resumes and devaluing women’s colleges. HR leaders considering using AI based software need to ensure that the software is not simply choosing candidates based on prior hiring biases.
Recruitment
“If you, as a recruiter or as a Talent Acquisition professional, are not incorporating AI into your process - you are behind the game.” - DK Bartley, Chief Diversity Officer, Moody’s Corporation
Recruitment is one of the most time consuming activities of an HR department but luckily using data analytics can allow recruitment to be more fair, inclusive, and effective. Using an applicant tracking system (ATS) allows you to collect information and organize candidates for hiring based on your selected criteria. The ATS automates tedious recruitment tasks like job postings, careers pages, interview scheduling, managing workflow, and tracking candidates, giving you more time to spend on strategic decisions.
Beyond an ATS, one key piece of data your HR team should be tracking is the application to hire ratio. If the ratio is high, there is an overflow of applications but limited hires, while a low ratio indicates a lack of applicants. While application:hire ratio is a “symptom” metric, data analytics can help explain the results of your company’s ratio as a “cause” metric.
Jeremy Stanley, former VP of Data Science at Instacart, explains the recruitment cycle as a funnel to determine how many applicants are needed for a single great hire. Taking a wider view of data and metadata by examining the number and type of candidates entering and filtering out of every stage of the recruitment funnel allows the company to identify top performing recruitment channels or ones that require specific marketing changes. For example, if there is an overwhelming number of initial applicants but many of those are exiting from the application on a particular website, the HR and marketing teams should spend time to align messaging and develop better job descriptions to encourage more qualified applicants. Using data analytics will improve the recruitment process to equip hiring managers with better insights and information to make high-quality hires.
Other metrics to monitor include: headcounts, demographic data, time-to-hire, time-to-fill, and quick quits.
2. Employee Experience
The HR department’s most important responsibility is to improve employees’ productivity. One of the key drivers of productivity is engagement.
Engagement is “people want[ing] to come to work, understand[ing] their jobs, and know[ing] how their work contributes to the success of the organization” according to John Baldoni, author and leadership educator.
While periodic surveys asking employees about their engagement levels have become a common practice, 58% of companies are not taking action to improve employee experience based on their surveys. Using surveys to measure engagement as a pulse check is low hanging fruit that is not doing enough to create quantifiable and qualitative gains on an organizational level.
Google, as a leader in People Analytics, has shown how to use analytics to improve employee experience in various projects. We can condense the learnings from its Project Oxygen and Aristotle into two main points. Firstly, combining performance metrics with qualitative survey comments provides HR managers with a vast expanse of data to draw on. Secondly, HR teams need to ask the right questions aimed at improving employee experience. Specific metrics to track include the employee Net Promoter Score (eNPS) and employee mobility.
Data analytics can also be used to improve Diversity, Equity, and Inclusion (DEI) initiatives by tracking diversity data, evaluating the leadership pipeline, and benchmarking goals related to DEI. DEI goals can be integrated into every process and stage of HR activities such as flagging job codes for underrepresented minorities to take action to diversify the applicant pool. Retaining diverse employees is equally important to hiring, and evaluating mobility through the leadership pipeline will provide your company with relevant insights. [For further reading on retaining diversity, check out our previous blog post: A Second Look at DEI].
Products like Coach bring together data analytics and employee experience by sending out pre-scheduled interactive posts to your team on Slack and then providing real-time HR data analytics. With specifically designed message packs like the DEI-focused “Belonging Boosters”, Coach fosters empathy and dismantles prejudice and bias through starting the conversation on belonging in the workplace. Coach conducts sentiment analysis so your HR team can monitor employee engagement to test and iterate different HR initiatives. Book a demo for Coach here.
3. Turnover
Using data analytics as a guiding method for HR decisions at all levels of activities will improve employee retention, reducing turnover.
Implementing an HR Information System (HRIS) will allow you to collect data like name, age, address, salary benefits, attendance, absences, and performance reviews. Tracking employees using an HRIS allows the HR team to analyze symptom metrics of an employee’s tenure at the company in the event of expected or unexpected turnover. Check out this comparative analysis of different HRIS to get started.
Before diving into prescriptive analytics (asking “what should we do?”), HR departments can diagnose their current state of turnover by looking at retention rate to determine the level of involuntary vs voluntary turnover. Based on your results, use data from surveys, check-ins, focus groups, company reviews, and exit interviews to determine resignation drivers, correlations, and segments. Using a data-driven approach provides HR teams with the ability to make informed decisions about how to manage and potentially avoid turnover, along with other solutions such as training or relocating.
Other metrics to monitor include: revenue per employee, performance and potential data, and turnover rate.
Conclusion
With leaders facing an unprecedented dynamic in the transition to and from remote work, data analytics can improve your existing HR programs. Begin your journey into data-driven HR by identifying the segment of HR with high costs and uncertain effectiveness, be it hiring, employee experience, and turnover, and starting with some of the recommended practices in this guide. No matter which analytics you choose to begin tracking, be certain to establish key performance indicators before beginning. Test your metric-tracking methods, consistently ask for feedback, and implement changes to improve performance.
If you’re looking for more specific training on data analytics in HR, the following courses are a great place to get started:
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