It is a problem no one wants to talk about: The struggle to realize the business value of people analytics. It’s not because companies are not willing to invest in it. It is because the adoption of people analytics technology is difficult and complex. A one-size-fits-all approach does not work. To address this, Crunchr has developed the very first model for people analytics adoption. It provides a workable solution to scale adoption.
People analytics has been driven in the last few years by its huge potential. Companies have invested money, hired experts, and started to explore the possibilities, riding a wave of momentum and high expectations.
Yet, despite the promise, the results are still very anecdotal and experimental. Many HR teams feel that the impact is not as large as they expected at the beginning. Some companies have tried to revive their people analytics programs through training, and projects to improve data quality and fix authorizations.
Clearly, it’s time to take a step back and evaluate the state of people analytics adoption -- and what to do next.
Many organizations experimented with people analytics projects on a small scale. And even though the results are promising, these organizations are not yet making the big companywide impact that was promised. At Crunchr, we believe that three conditions must be met in order to achieve this:
The New Crunchr Model: Driving Adoption of People Analytics, 2021 all rights reserved.
Prioritization: the focus on million-dollar questions, where you can make an impact.
Productization: Being able to scale the solution to the entire company.
Adoption: Ensure that everyone embraces the solution and makes it part of their work culture.
Focusing on the third condition, we started by questioning why companies would try to use a one-size-fits-all approach to people analytics adoption. Some groups of employees will readily embrace the technology, while others must be convinced to use it. If technology adoption differs among employees, then the way to drive this adoption needs to differ for each group.
Building the model
Crunchr People Analytics Model Architecture. All rights reserved, 2021.
We started researching other industries, like healthcare and law, where professionals have embraced technology and completely changed the way they work. We studied the constructs and drivers behind technology adoption and created the first-ever model for people analytics adoption.
The model shows the five stages of a person’s perception of technology as it influences their decision to adopt the technology (aka “constructs”):
Intention to use
Opportunities to use
Each construct is fed by drivers or behavior influencers. Let’s take the first stage – Seems useful -- as an example. For someone to believe that people analytics is useful, the perceived Image of this technology as “cool” or “insightful” is influential. Job Relevance, or the perception that people analytics will make their job better or their role more important, also plays a role. Furthermore, a person has to be able to trust the Output Quality, or data quality, of the technology. And finally, users need to clearly see the benefits of using people analytics (Useful Results). When all four of these drivers are in place, a person perceives the technology as useful.
Another construct, Intention to use, is formed through five drivers. One is Subjective Norm (example: your HR director says having a gut feeling about a hiring decision is fine, but you need to back it up with data). Another driver is Voluntariness. Research shows that if managers mandate employees to use technology, adoption rates are low. But if technology use is presented as a voluntary but attractive option (example: using this tool will help you advance your HR career), the influence on behavior is positive.
Developing Personalized Interventions
After developing our model, we turned the focus to providing a framework to promote adoption. First, we did a survey of 125 Crunchr users based on the constructs and drivers. Then, we correlated the answers with our model. Next, we clustered the users into buckets, which allowed us to personalize the interventions.
For instance, users with a high score on Seems useful and Seems easy but a low score on Intention to use are grouped into one bucket. They should receive training that addresses the drivers feeding Intention to use.
For longer-term Crunchr users, this model has also been useful at improving adoption. We can measure how often they log in, for how long, and how they use our products. We can correlate the data with these drivers in order to understand the missing links between the current state of use and full-scale adoption. That allows us to develop interventions like product tours and data quality seminars.
For new customers, we now personalize deployment with a survey that lets us know right away which tools will best support them with adoption.
Insights so far
Along the way, we have discovered some interesting things. We learned that survey responses did not differ by role but rather by maturity in the model. In other words, the readiness to adopt technology depends not on a user’s job or level but rather on which stage he or she happens to be at. We have also seen at least one case of an incredible increase in adoption using this framework.
At Crunchr, we are just in the early phase of this project, but the results have been exciting. We will continue to experiment and refine the best tools for the next era of people analytics adoption.
Reach out to us if you would like more information about our People Analytics Adoption Model and how Crunchr can help your organization succeed with people analytics at scale.
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