People Analytics Frontiers aka Why are We Asking the Same Questions Again?

by Nick Jesteadt & Erin Fleming

Apologies to our passionate audience for the delay; in full transparency, Nick & I have both recently started exciting new roles and we are full force in data cleaning and structuring, as discussed in our last article as the critical baseline for building trust and credibility in people analytics!

However, let’s assume you already have these basics down. Let’s say you have solid, useful turnover trends, an engaged employee base, and a clear organization structure and workforce planning process. What next? Presumably, you get to the really fun (tricky?) things now: how can you track and increase productivity? How can you create a skills database that can be used for hiring and career progression? Should you be treating HR like a product organization? And at the same time every great HR analytics team is building, and every vendor is promising, AI-driven solutions to all of the “murky”, tricky questions above.

For the last several years, these questions have generated articles, conferences, think pieces, and TedTalks, but remain (at least partially) opaque. That’s because they are part of the frontier of HR, and leaders are increasingly leveraging data & people analytics teams to help answer and give structure to these areas. Below, we will give a brief overview of why each remains such a challenging and exciting topic, and some of our thoughts on how to get started on them at your company.?

If you’ve already gotten started, we would love to hear some of the creative and unique ways other practitioners are pioneering in these important areas.

Productivity?

In some ways productivity is the fountain of youth for people analytics. Everyone wants it and despite some claiming to know the way forward, it still seems largely elusive. For some functions there are clear metrics: quotas for sales teams, actual production for manufacturing, etc. However, for most corporate roles, are we clear on what productivity even means, let alone how to accurately measure it??

We certainly have proxy metrics; for example, we can look at things like customer satisfaction or goals progress. In terms of actual productivity measurements, we can look at literal activity like meeting attendance, emails sent, or lines of code. However, is this really what we mean by productivity? We’ve all heard stories of people buying mouse moving machines to look busy when they really aren’t (of course, no one here would ever do something like this). Is this how we want People Analytics to be viewed - as the people checking if your mouse moves enough each hour or if you’ve sent enough emails for the day?

In some research, productivity tends to get blended together with performance (individual or company), or with engagement. We certainly think that these topics are interconnected: each area provides a different window into how an employee interacts with their employer: the value that they drive and the enjoyment they get out of their role.

The graphic below shows our spider-web of connectivity. There is an assumed correlation between engagement and productivity; and for many teams, proving this correlation has proved to be a fruitful endeavour for generating trust in people analytics teams! The logic is not hard to understand: if you like what you do and the company that you work for, you are more likely to work harder. But what does working harder mean and why is productivity across an organization so hard to define?

We alluded to it a bit in the introduction: metrics such as quota-attainment, billability, widgets produced, tickets resolved, calls logged, lines of code (increasingly and deservedly taboo) all provide concrete, quantitative windows into productive volume. However, trying to scale limitless and contextual options into a singular KPI for any organization can be frustrating and futile. Some ideas:

• Have each functional area at your organization define their specific productivity metrics. Each quarter, index their employee’s productivity KPI against the 75th percentile of that metric and generate a score from 0%-100% in terms of individual performance compared to the group.
• More abstract: ask employees (and supervisors) to assess productivity. Nudge them to be data-driven and start to collate feedback to such a question and use text analysis to blend together a picture of holistic productivity with nuance and narrative. This option may not yield a singular number per person, but it may allow for context and encourage productivity to be a topic of discussion at employee-manager 1:1s.

Whatever you choose to do, people analytics teams can show the relationship between productivity and performance (even with singular productivity KPIs such as quota-attainment or email volume).

If your company does annual ratings, or performance-based merit increases, are you able to correlate the singular (or holistic) productivity KPIs to those ratings or pay-for-performance proxies? This can be a valuable way to showcase to HR and Executive leadership how data-driven and consistent these ratings are. It can also show how strong any of these proxy productivity metrics are to organizational performance (or its perception). For example, if there is very little correlation between lines-of-code for your software engineers and their resultant annual increases, you may be able to advise and conclude a few things: 1) lines-of-code are not as important as we thought, and the recognition that employees receive are devoid of the metrics that we say should fuel them; 2) lines-of-code are not an effective KPI for assessing productivity and show up inconsistently in their relationship to performance; 3) we are not effectively connecting the metrics that we say matter to company performance to the expectations on individual recognition.

Doing this correlative analysis between your productivity metric(s) and your performance indicators (on an individual basis or even for company financial performance on the whole) should demonstrate what is resonating with your supervisors, with your employees, and what is embedded into your company’s expectations of value generation. It allows People Analytics teams to advise on what - and what not - to include to craft a better vision of organization specific productivity.

There is the final piece: how does productivity connect to engagement? There is typically a desire that driving engagement and employee satisfaction will generate greater contributions. That’s the logic behind offering gym memberships, office parties, and dozens of other employee-centric perks. Are they worth the investment? Similar to the correlation analysis done above with performance indicators, any singular or multifaceted productivity scores can be correlated with engagement metrics such as eNPS.

Groups that have “strong” productivity metrics and similarly high engagement results may be able to rest assured that the value they drive is built on a solid foundation: one that will result in employee retention and discretionary effort. What about where productivity is purportedly high but eNPS is low? This might be a “house of cards” where results can be lucky or fleeting and could reveal significant long-term risks. Being able to show this down to a team level is a critical area for People Analytics to prove their worth to any HR team.

Will increasing engagement then link to employee satisfaction? This is not a new or overly complex area. Several survey vendors will help unearth the correlated drivers from your engagement results so your leadership knows what switches to best flip to increase contentment.

As mentioned (and shown in the graphic), there is an expectation that these distinct areas are married to one other discretely or overtly. Correlation analysis will help reveal which metrics seem to be in lockstep. Even more compelling is when they do not: what assumptions within your company are broken in those instances, and how they can be repaired? Below, we have also provided a table that provides just a sample of KPIs for each of those four areas (you will see some overlap here as well). Feel free to comment and tell us your own:

Skills Based HR

Another hot topic in our world is using skills for hiring and promotions, rather than traditional roles. Some articles have even suggested getting rid of a job structure altogether. This idea makes perfect sense in a consulting type company structure; but we wonder about the success in a more traditional company. What does benchmarking for a skills based organization look like? How do you create succession plans or prepare for backfilling and workforce planning?

The first, and possibly largest, challenge with becoming a fully skills based organization is creating a skills inventory. Each employee, team, role, and project, would need a clearly defined set of skills. This is an area where AI has the potential to provide a lot of value. If we walk through a simple employee lifecycle, we can see where and how skills could come into play and if AI could help in each of these steps.?

Hiring?

We have all heard of using AI to scrape resumes and professional profiles online as well as using AI enabled tools to provide a ‘first look’ at applicants. In many ways, any organization doing this is already leaning towards skills based hiring.?

The big question is how to evaluate those soft skills that we need in many positions? Often hiring teams are using things like years of experience or project based interview questions as a way to get at whether candidates have the required soft skills (time management, communication, stakeholder management, etc). If we move to hiring more exclusively on skill sets, especially skills reviewed by AI, there are certainly advantages, but it is hard to imagine a world where almost every candidate wouldn’t simply list communication as a skill.?

In addition to finding candidates, if we create a list of skills rather than a traditional role, we need to think through candidates being able to easily find the role and understand it. This is another area where AI comes into play. There are already savvy job hunters using AI to aid their search (not to mention create resumes, write cover letters, and prep for interviews).?

Onboarding

Onboarding is not just for filling in paperwork and completing mandatory training. It should be an opportunity for a great first impression and to begin the process of your new hires finding their space in your organization.?

To steal an idea from the aforementioned consulting companies, we believe using skills to create communities within your overall company could be a great way to help people get connected quickly upon joining. Again, AI could be useful here in the creation of not just an inventory of skills, but grouping them to help create these communities, and suggesting good communities for new hires to join.?

Promotions & Rotational Programs

One of the biggest benefits of a skills based organization is the ability for employees to shape their careers beyond the traditional trajectories. Once a full inventory of skills is created, it can be utilized to map out a plethora of career opportunities, both in terms of the skills needed for promotions but also mapping what skills an employee may already have to transition into a different role within the organization.?

The big question here is about how we correlate a skill with performance. Much like in hiring, does an employee saying they have good communication qualify for the inventory? Does this skill need to be verified by a manager or some other performance metrics in order to be considered for promotion or a rotational program??

Terminations & Workforce Planning

Terminations (and planning for them) are a natural part of any employee lifecycle. More advanced organizations may be using predictive models to help foresee voluntary terminations and therefore complete succession planning and prepare for future hiring needs. In a skills based organization, this becomes much more difficult.?

If we set our organization up based on a combination of skills, rather than jobs, how can we adequately plan and budget to backfill those skills without simply considering the combination of them as a role or position? How would we benchmark skills, rather than jobs, for equitable compensation both within and outside the organization??

It is true that no two employees, even in exactly the same job, bring the exact same talents, experience, and skills to the role. The best organizations are able to bring out the best in each of these individuals so perhaps setting up a skills based HR organization is simply a more accurate reflection of the existing situation??

HR as a Product

The final area for exploration in this article is treating HR like a product. In this way, we can leverage many of the methodologies, practices, and AI innovations happening in Customer Experience organizations to better understand our own teams and how to improve our processes. The throughline is well trodden: employee retention mirrors customer retention, we onboard employees and customers, we measure NPS and eNPS – so what keeps some CX investment areas so separated from HR?

Below we summarize a few practices done well in CX teams (especially the data driven ones!) and juxtapose that with some similar areas that are typically not done well - historically and stereotypically - in HR teams.

Data-Driven Best Practices in High Functioning Customer Experience Teams:

1. Customer Journey Mapping: Analysing each step a customer goes through, from initial contact to the final service or product delivery, helps identify pain points and opportunities for improvement.
2. Touchpoint Analysis: Evaluating the effectiveness and satisfaction at every interaction point a customer has with the company.
3. Sentiment Analysis: Leveraging data from social media, surveys, and other feedback mechanisms to gauge customer feelings and satisfaction levels.
4. Segmentation Analysis: Dividing customers into groups based on demographics, behaviour, or other criteria to tailor services and communications effectively.
5. Churn Analysis: Identifying why customers discontinue service and what can be done to prevent it.
6. Net Promoter Score (NPS) Tracking: Measuring how likely customers are to recommend the company's products or services as a metric of loyalty and satisfaction.

Data-Lagging Practices in Immature HR Teams:

1. Personalization: Unlike CX, where services and communications can be highly personalized based on customer data, HR often applies more generalized policies and programs across all employees, which may not address individual needs or preferences effectively.
2. Proactive Issue Resolution: In CX, data analytics are often used to predict and mitigate potential issues before they affect a large number of customers. In HR, interventions are usually reactive, occurring after an issue has been reported or become evident through employee dissatisfaction or turnover.
3. Continuous Real-Time Feedback: CX organizations frequently utilize real-time feedback mechanisms to quickly adjust and improve the customer experience. In contrast, HR may rely on periodic surveys like annual engagement surveys, which may not capture timely feedback and can lead to delayed responses.
4. Comprehensive Onboarding Experience: While CX focuses on seamless onboarding to quickly enable customers to use a product or service, HR onboarding processes can sometimes be bureaucratic and less focused on ensuring a positive initial experience for new hires.
5. Advanced Analytics and Predictive Modelling: CX heavily uses advanced analytics to predict future buying behaviours and preferences. HR is typically less advanced in using predictive analytics to foresee employee behaviours or potential dissatisfaction points.
6. Experimentation and Rapid Iteration: CX often employs A/B testing and other experimental approaches to optimize the customer experience. Such experimental approaches are less common in HR practices, where changes are often slower and driven by long-term planning rather than immediate iterative testing.

If HR groups can begin to model and mirror their behaviours after CX - leveraging creative and credible People Analytics Teams along the way - they might find rich success. Establishing data-driven dashboards & analyses for the above can help accelerate the transformation of HR into a product type function.

As always, we welcome input from fellow practitioners! What are you doing at your own organizations in any of these areas? Is there something we missed in our analysis? What are some other enduring People Analytics topics that need examining?