Our previous posts on data analytics stressed the importance of employee engagement with the compliance program. Now let’s give that subject our full attention: how, precisely, can a compliance officer use data analytics to drive improvements in this area?  

After all, strong employee engagement with your compliance messages and materials isn’t just crucial to the success of your compliance program — it’s also crucial to the success of the compliance officer! 

The ability to drive employee behavior toward certain standards of conduct (and also quantify and report on this), prepares you to build a strong case in your presentations to the board, requests for more budget, or briefings with regulators deciding how to settle a misconduct issue with your company. 

To do this, however, you first need a clear understanding of where employees are now — with hard data to back it up — and the ability to measure again periodically to show changes over time. 

In Rethink’s business, we enable clients to collect analytics using two different channels:

  1. Digital Code of Conduct
  2. Online courses

For most compliance teams, these are ideal channels to start with! Not only are they fully owned by the compliance program, but these two types of compliance materials tend to reach nearly every employee in the business. 

Further, they are initiatives you are doing anyway, not something special cooked up for the sole purpose of creating data to work with.

What kinds of data is it possible to collect?

In digital Codes, we can collect user behavior analytics, which is just what the name implies: analytics about how various users behave when they’re interacting with your Code website.

In courses, we can collect quantitative data on qualitative questions — topics related to employee perceptions, behaviors, attitudes, and even self-reported actions, gaps, or training needs.

Using Analytics for Better Understanding

Compliance officers can collect all manner of insights about employee engagement through data analytics. For example:

  • You can map out what a “typical” interaction with your compliance program looks like. How long did the average employee spend in the Code when you first launched it? In the following six months, how many returned after that initial introduction? Which topics held the most interest? Which policies did they click on?
  • You can compare different employee groups within the enterprise. For example, a global multi-national might find that 95 percent of its management-level employees believe the company has a strong speak up culture, but only 60 percent of line employees do. 
  • You can see how employee behavior evolves over time. For example, if you roll out new anti-corruption training, that typically leads to a spike in people visiting your anti-corruption policy — but does that new policy remain popular over time, or does interest plunge rapidly? That could mean the difference between employees who take anti-corruption seriously, or those who let your training go in one ear and out the other.

Our examples above touch on two important points. First, you want to define certain “personae” in your enterprise — profiles that represent certain types of users. That allows you to reach conclusions such as “employees in the Europe division tend to behave in this way” or “managers behave in this way, but rank-and-file employees behave in this other way.” Ideally, you want to understand the traits of highly engaged employees. 

Second, you want to understand what employees are doing and how they perceive your various compliance efforts. So you’ll want to identify events that might be triggering their behavior. For example, “We launched a new Code of Conduct in Q1, and saw a spike in visits to the Code well into Q3.” And you’ll want to collect employee answers to related questions like: “Did you visit the Code in 2023? If so, did you find it useful?” 

Simply put, you want data analytics to give you averages, correlations, and trends. That lets you understand the current state of your compliance program’s effectiveness, even if those analytics give you answers that need further investigation or that you don’t like to see. 

From Analytics to Engagement

Once you start generating insights about current employee engagement with the compliance program, you can start devising strategies to improve that engagement as necessary. 

Go back to the example we mentioned above, where new anti-corruption training led to a spike in calls to the hotline about anti-corruption. That might mean that employees didn’t understand your anti-corruption concerns until the new training; it might also be that employees didn’t take prior anti-corruption policies seriously, because they saw managers ignoring the policy and engaging in illicit payments. 

Either way, you have a question that needs answering. Then you can implement changes to the compliance program (more resources to investigate calls faster, or stronger disciplinary action against offenders) — and almost by definition, a better program leads to more engagement. When employees see that the compliance program responds to their needs and concerns, they engage with it more.

Let’s also remember that when your compliance program improvements are driven by the data, arguing for those improvements in front of senior management (or defending them in front of skeptical regulators) becomes much easier. You aren’t just making changes to the program based on a hunch, or anecdotal evidence, or on what you read about enforcement in the news. With hard data, leading to solid conclusions, the changes you want to make will be much more persuasive and likely to succeed.

That’s going to get better engagement with the board, the C-suite, and regulators too.