Start Treating your Employees like Customers
Three concepts from marketing analytics that could help unlock the full potential of people analytics
The opportunity for people analytics to enjoy a late-mover advantage compared to other corners of the data & analytics space has been well documented. Unfortunately, it isn’t clear that the field is maximizing its opportunity to leverage methods native to other analytics disciplines, despite the large potential for overlap.
At the same time, HR practitioners are struggling to balance reaping efficiency gains from scalable solutions with attempting to serve the (often) contradictory needs of different employee groups. One such group is the digital natives, who, accustomed to personalized experiences in their consumer lives may be disappointed to find that companies are struggling to offer them much more than an internal job postings board.
To alleviate these challenges, I would like to highlight a few marketing analytics concepts – each tailored to answer a different type of question. Despite being better known in the marketing context, they can serve as unique and valuable tools in the people analytics practitioner’s toolbelt. This post will provide a quick introduction to each concept and will briefly cover some techniques that can serve as a starting point.
I. Segmentation
II. Attribution
III. Customer journey analytics
Segmentation
Most are familiar with the concept of dividing a customer base into distinct groups with unique needs and wants. The key insight: your workforce is not a homogeneous group. Different types of employees care about different aspects of your employee value proposition depending on their job, career trajectory, family situation, etc. Ignoring this fact and developing HR solutions for “employees” generally is liable to result in mediocre solutions that take care of everyone but don’t really please anyone.
To better target employees, people analytics can help HR and business leaders by identifying the key variables that differentiate employee groups. Understanding the key “market segments” that make up your workforce serves as a more informed starting point by which you can design more personalized solutions to drive tangible strategic outcomes for the business.
Techniques:
A. Latent profile analysis is an exploratory technique that assumes there are hidden, underlying pattens in your population and can identify or segment groups accordingly. Once segmented, you can create fascinating insights by correlating group membership to demographic variables to further describe the groups, and to key talent outcomes like engagement and turnover.
B. Cluster analysis is a simpler technique using machine learning that can be used to cluster, or group employees together into a given number of segments based on similarity. This can be useful if you already have an idea for which key variables you want to use to segment.
Source: https://willhipson.netlify.app/post/latent-profile/latent-profile/
Attribution
In marketing, attribution broadly refers to the practice of using data to understand whether customer actions can be attributed to specific marketing pushes, or touch points. If you’ve ever wondered how companies try to figure out whether advertisements actually work – this is it!
McKinsey estimated that 15-20% of marketing budgets are wasted due to lack of attribution. This is purely speculative, but I would guess that number is even higher in HR because we’re so behind the curve. Figuring out which HR programs, processes, and offerings actually have the desired impact (or how to improve them so they do) could be a really exciting opportunity to help HR make smarter investment decisions.
Techniques:
A. The Kirkpatrick model is a classic framework that evaluates training effectiveness at four levels – reaction, learning, behavior, and impact. In my experience, this model generalizes well to assessing broader HR programs and offerings, not just training. It is useful because it prompts the analyst to examine each step in the value chain, from how an offering was received by the employee, to whether they behaved differently, to whether their behavior change made the desired impact.
B. Multi-touch attribution models are the standard in marketing analytics and refer to an umbrella of algorithmic techniques that model customer touch points. The power of these models lies in their ability to infer causality – that is, they can help you make judgements about which touch points led to a “conversion”. Depending on your question about employees, these techniques might be overkill, but check out Markov chain and shapely value models if you’re interested.
Customer Journey Analytics
One thing that HR business partners already have a great understanding of is that the real employee experience does not map cleanly to our functional silos (Centers Of “Eh”?). For example, the onboarding process may encompass touch points across recruiting, HR operations, IT, learning & development, the employee’s manager, and teammates. Without examining this process holistically, we are left with disconnected facts, and the odds are high that we’ll miss relationships between touch points, or worse, miss glaring gaps in the experience.
This is the key insight of customer journey analytics, and while organizations large enough to have employee experience leaders are already on top of this, in smaller organizations this conversation could start in people analytics, who has an (ideally) 360-degree view of the employee. Either way, people analytics can help identify pain-points along the customer journey, connect them to overall HR/business goals, and provide insights to decision makers on how to address.
Techniques:
A. Employee listening: The great part about the employee journey concept is that it doesn’t necessarily require advanced analytics. Instead, we need to figure out what data we need to collect. Working with your partners to map the employee journey can be the first step to figuring out what surveys could garner valuable insights. Alternatively, pulse surveys and open-text field questions can be a great source to uncover problems you didn’t know existed.
B. Regression analysis: I couldn’t write a post on methods without paying homage to the classics. Regression modelling can be a flexible and powerful tool to help build understanding of how each aspect of the employee journey is related by finding relationships across disparate data points over time.
Wrapping up
It’s important to keep in mind that there are fundamental differences between HR and marketing, and that we shouldn’t go too far with this analogy when deploying people analytics techniques. Treating employees differently based on their age could get you into legal trouble, as an example.
Caveats aside, too many HR offerings treat employees as a single homogenous group. As we continue to navigate the tightest labor market in history, thinking about your employees as customers could give you an upper hand in the talent wars. It could give you the mindset to start answering questions like: which segments of our workforce have different needs, are our offerings actually working for them, and which touch points should we focus on to improve their experience? In the people analytics revolution, these questions could differentiate next-generation people functions from traditional HR departments.
