The list of metrics that fall within the scope of employee engagement is staggering. What’s more, tomorrow’s list will likely differ from what you’re interested in today.
So—what should we be measuring? And how should we be measuring it? That depends on your organization’s growth strategy.
For example, maybe you want to increase cross-team collaboration as a result of your mentorship program roll-out. Maybe you want to see how successful your employee referral program is in reducing cost of acquisition. Or maybe you’re watching as your top performers leave for the competition at staggering rates and you’re desperate to uncover why.
The differences within the three aforementioned examples are wide and affirm the notion there is no one-size fits all consortium of metrics to measure employee engagement because all of these are indicators of engagement yet they also are measured in very different ways.
Rather than worrying about landing on the singular metric-above-all-metrics to measure engagement, I have found success adopting a more agile approach by selecting one or two metrics that you believe matter most to your ultimate objective for the business. Then, stay open-minded and expect that the metrics you measure might evolve as you see what transpires. Because in my experience, that’s the reality of data analysis. Very rarely do we analysts and researchers just stub our toe on organization shifting insights in the excel workbook. They take time—but, you can invite efficiency into that time by anchoring to a consistent framework.
Related: What can be measured, monitored and optimized in order to help you achieve your core business goals? As with all business challenges, visibility is half the battle. If you can see it, you can counter it. Learn more.
And that’s where the importance of a framework comes in to play. While metrics can be fluid, having a consistent methodology enables us to periodically evaluate our effectiveness and calibrate as needed. Having a prescribed process ensures that no matter what metrics we choose, we process data through a consistent series of stages that helps us foster positive change. At ITA Group, we use the following stages in our model: socialization, optimization and actualization.
Let’s walk through an example to help demonstrate best practices and what we learn from each stage of the framework.
Socialize the Data
Socializing the data looks a lot like giving the data back to those responsible for generating it in the first place. A socialized view of data is one of transparency that relies on one hard truth:
The worst place for data to rest is in a vault.
If you want to create natural accountabilities and groundswell growth, empower individual contributors and their immediate leaders with transparent data. Data positioned with even the slightest hint of disconnect to who or how the employee/manager views their position risks being discounted.
Imagine for a moment: 1,000 individual contributors report evenly to 100 supervisors, who report to 10 managers, who, in turn, report to one director. Natural Language Processing (NLP) techniques within the annual survey uncovered that employees believe they have too many meetings and it’s impacting their productivity. The director knows this and decides to share this information with their 10 managers with a call to be mindful of the number of meetings individual contributors are engaging in. That means each manager is responsible for decreasing waste within meetings for roughly 100 individual contributors—some of whom the manager likely has little direct interaction with. This creates an ambiguous execution of socializing data that also potentially risks individual contributor’s being made to feel like they were not heard and thus, a slow (if not zero) change in behavior.
Consider, instead, empowering your supervisors by giving them visibility over their individual team’s week over week meeting footprint in a one row per individual contributor format—10 is so much more manageable, not to mention personal! This gets the data in the hands of the people best positioned to move the needle.
But it doesn’t, in and of itself, move the needle—to do that, you need to optimize the data.
Optimize the Data
What would it look like to consider optimization, simply, as disaggregated ROI? Just like a salesperson relies on steps to the sale, data relies on optimization strategies to realize a true, authentic and understood path to ROI.
To demonstrate this concept, let’s keep our meeting/productivity example rolling. Simply put, stating where we are today and where we want to be in three months is not a function of optimization. Consider instead breaking down the steps required to reach that three-month goal and identifying who needs to be involved and how you can collectively work toward that goal.
Optimization is most successful when it’s an iterative and personalized process with ample opportunity to make incremental progress. Here are a few examples that illustrate common missteps within our current example and ways to avoid them:
1. Anchoring to a 3-month goal out of the gate
- Position a week one goal (e.g., turn one scheduled meeting into an email)
- Refine goals on a per individual contributor basis in subsequent weeks—this will take a bit more time and attention but personalization is critical to successfully sustaining a grass-roots effort.
2. Bringing all 1,000 individual contributors together just to be “together”
- Allow supervisors to express the empathy (type and level) they know their team prefers
- Have the supervisor transparently provide the data on where their team has performed historically—and where they as a team need to work
3. Tasking your leaders with a plan but not evaluating their progress
- Incorporate personalization in to your own strategy by allowing context to flow in to your follow up plans
- For example, if X is doing great, but Y is falling behind, reallocate X’s time to better understand the barriers Y’s team may be facing
Actualize the Data
Remember disaggregated ROI? This is where you get to roll it back up and share how the prior steps in the framework have helped you get here:
- Socializing the data: getting it in the hands of the people best positioned to move the needle
- Optimizing the data: inviting personalized context and making every step goal-oriented
If things have gone according to plan with those first two steps, you’ll find you’ve successfully decreased the average weekly meetings for individual contributors. What’s more, gradually, over that three month stretch, you could be celebrating drops in meeting attendance that correspond with lifts in eNPS ratings.
Understand that everyone’s data strategy is different. Yours may not fit into three-pre-defined checkpoints. That’s why this framework is iterative.
If you realize 90% of meetings are with the IT department, rather than your supervisor and individual contributor audience alone having data visibility, the next time you test set up accountabilities on the IT side as well and work at this underlying issue from “both sides”. While starting over is frustrating, a miss in actualizing results can result in a novel optimization discovery, which results in a resocialization of data that should ultimately drive more sustainable change.
The Ultimate Value of a Framework in Your Data Strategy
Data can be murky. And in my experience, confusion is driven less by advanced methods, approaches or metric gathering. It’s more connected to a lack of organizational alignment.
By having a consistent framework—and a culture that embraces agility—it will simplify the times when you decide to pivot between metrics, create tremendous efficiencies and generate universal alignment around the steps that matter most, driving real results.
In the year 2021, we’re predicting efforts to refine and enhance the employee experience will continue to be a top area of focus for leaders everywhere. Keep reading to learn more about the employee experience trends ITA Group team members predict will gain traction in the coming year.