How Different Types of Analytics Tell a Different Recognition Program Story

Analytics is the science of logical analysis. 

Analytics with employee recognition programs use recognition program output metrics, or usage data, and apply mathematical equations, statistical analysis, and computer software to paint a picture of what is going on.

However, different levels of analysis produce a different image and insights. The deeper you go with analytics, the more understanding you gain and the better action you can take. 

Consider the following observations. 

A Descriptive Analytics Approach

Recognition programs generate all kinds of program usage data. Descriptive Analytics simply asks, “What happened?” or “What is happening?”

They display data from recognition programs in different formats. Descriptive analytics is simply analyzing the bar charts, tables, and numbers, and then seeing what sense you can make from them.

For example, with recognition ecards, you would know the total number of people sending recognition e-cards and the total number of employees receiving them. It is black and white, with little backstory to glean from the data.

Recognition program data could show:

  • Number of registered users.
  • Percentage of participation.
  • Usage activity report:
    • Number of senders of recognition.
    • Number of receivers of recognition.
  • Dates of Activity.
  • Awards or points issued.
  • Values or targeted goals recognized.
  • Percentage of increase or decline.
  • Number of recognition ecards sent.
  • Number of nominations sent.
  • Number of social recognition posts sent.

And plenty of other program variables.

Looking at Diagnostic Analytics

Going down a level deeper, Diagnostic Analytics attempts to look at recognition data by answering the question “Why did it happen?”

Recognition program managers want to go beyond descriptive reports. They hope to know why the recognition data is where it is at, when it happened, who gave it, for what reason, and what they can change to improve recognition.

The benefit of a more diagnostic analytics approach is the ability to drill down by department and manager. You gain more answers over the static reports that descriptive analytics provide. As you explore the data, you might see some correlations with recognition program data and other outcomes, such as employee engagement measures.

For example, by drilling down on the recognition data, you know the number of managers in which departments sent recognition e-cards to the number of employees, and who they were, and which department they’re from. It only allows quick assessment to make general perceptions of why certain leaders in some departments sent more ecards than others, but that is about it.

Both Descriptive Analytics and Diagnostic Analytics operate from a hindsight perspective and examine lagging indicators of recognition metrics.

Moving to Predictive Analytics 

Things get more exciting when you move towards Predictive Analytics. Your motivation is to find out exactly “What is going to happen?” And you can even ask “What is likely to happen?” because of the program data generated.

Predictive Analytics uses advanced analytic modelling methods using autonomous or semi-autonomous methods of examining data using tools and algorithms to make predictions. From this analysis, they generate recommendations from the recognition data. Analytic scientists use methods like regression analysis to make correlations with other data, conduct pattern matching, do sentiment analysis, etc., and predictive modeling.

For example, predictive analytics, as the name implies, can help you predict certain outcomes based on the data analyzed. They can do a regression analysis using your recognition program data and other key performance indicators. This process helps predict that managers who give more recognition ecards have higher employee engagement scores and higher employee productivity measures than those managers who sent fewer or no ecards at all to their employees.

The Power Behind Prescriptive Analytics

Prescriptive Analytics is the most advanced of the previously described analytic methods. This method allows you to answer questions such as “What should be done?” as well as “What can we do to make ‘x’ happen?”

Recognition program owners can use prescriptive analytics with their algorithms, heuristics, simulation techniques, and business intelligence to get exact recommendations on what to improve to achieve an organization’s desired results.

For example, you can predict which managers are at risk of generating poor employee engagement, low sales results, or at risk of greater employee turnover. And they can prescribe the needed recognition behaviors and program utilization ideas to turn these numbers around.

Predictive and prescriptive analytics give a foresight perspective because they focus on leading indicators. By using this type of analysis, you can help your leaders gain a real return on investment from their recognition programs.

At Engage2Excel, we have paired analytics with learning. We know that managers who learn how to give better recognition improve recognition giving in person and with online programs. Managers who give more meaningful and motivational recognition have employees who achieve higher performance scores on specific targeted business measures.

As you might expect, enhanced analytics development along with your employee recognition programs will give you greater business impact from your recognition programs.

Recognition Reflection: Which type of analytics do your employee recognition programs use?

Roy is no longer writing new content for this site (he has retired!), but you can subscribe to Engage2Excel’s blog as Engage2Excel will be taking Roy’s place writing about similar topics on employee recognition and retention, leadership and strategy.

Please note: I reserve the right to delete comments that are offensive or off-topic.

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