I am constantly researching information to better understand employee recognition and how I can better help you with these insights.
I recently stumbled upon some great information about analytics and how it applies to recognition and I want to summarize it for you. Nothing mathematical or statistics oriented – so don’t worry!
The information I found was from Gartner. They are an amazing information technology research and advisory company. Always be on the lookout for their reports, press releases, etc., for incredible findings and future trends.
I also found a great summary documents from Information Builders an analytics company in Spain. Don’t worry the information is in English!
If you’re like me I was never good at statistics and barely passed the course in my second year of university.
But when I found these descriptions of the different kinds of analytics it was so simple and understandable I just had to pass it along to you.
Quick Recognition Data Orientation
Recognition programs produce all kinds of interesting usage data.
This information can be turned into various forms of reports from basic Excel to fancy, graphically enhanced dashboards with bar charts and pie graphs, etc.
Information you can glean from almost all recognition programs today would be:
- 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
…and many other similar variables
The problem with these kinds of reports is that the numbers and format are static in nature with no major drill down capabilities or ability to dig deeper.
It is purely numbers for numbers’ sake.
For a long time these data reports were just called “reports”.
Now, many in the recognition industry are calling them “analytics”.
Gartner makes a nice distinction that will be helpful as we examine the full suite of analytic options.
They would call the above list of reports “Descriptive Analytics”.
1. Descriptive Analytics: The easiest way to remember exactly what Descriptive Analytics is to use the line Gartner has for Descriptive Analytics, in asking the questions “What happened?” or “What is happening”.
This is where you receive the data in various formats and you simply analyze the bar charts, tables and numbers manually and by observation to see what you can make sense of.
You know the total number of people who sent recognition e-cards and the total number of employees who received them. And that’s all.
2. Diagnostic Analytics: The next type of analytics progresses up a notch. Diagnostic Analytics attempts to look at recognition data and answer the question “Why did it happen?”
Most recognition managers and practitioners are wanting to go beyond reports and want to know why the recognition results happened as they did, when and where, and what can done about it.
Diagnostic Analytics permit you to drill down by department and manager and manipulate the data to get at a few more answers than the static reports provide. You may even be able to make some correlative comparisons as you work with the data.
Now you know the number of managers in which departments sent recognition e-cards to the number of employees from which department they’re from.
You can make general perceptions based on the leaders in each department as to why some sent more than others, but that is about it.
Both Descriptive Analytics and Diagnostic Analytics operate from the perspective of hindsight and look at lagging indicators of metrics.
3. Predictive Analytics: Now the recognition data starts to get a lot more exciting.
Predictive Analytics examines data or content with the desire to answer the question “What is going to happen?” or more precisely, “What is likely to happen?”
Predictive Analytics starts to draw upon advanced analytics methods using autonomous or semi-autonomous methods of examining data using tools and algorithms to make predictions and recommendations based on the recognition data – methods like regression analysis to make correlations with other data, pattern matching, sentiment analysis, etc., and of course, predictive modeling.
Analysts can provide you with insights from your recognition data. They can do regression analysis and show you those managers from the departments who sent more recognition e-cards generated higher employee engagement scores and higher productivity measures than manager who sent fewer e-cards.
4. Prescriptive Analytics: Is the pinnacle level of analytics and is an advanced analytics method that answers the questions “What should be done?” or “What can we do to make “x” happen?”
Prescriptive Analytics uses algorithms, heuristics, simulation techniques and business intelligence, etc. to provide recommendations on what to do to improve a company’s desired results.
We can not only predict which managers are at risk of poor engagement, low sales, retention and other business metrics through their use of recognition programs, but we can also prescribe how to improve their recognition behaviors.
Predictive and Prescriptive Analytics differ from Descriptive and Diagnostic in that they give foresight perspective and focus on leading indicators. This is what today’s leaders need to get real ROI from recognition programs.
At Rideau we have paired analytics with learning to the point that we know that those managers who improve their effectiveness with giving meaningful and motivational recognition have higher scores on the targeted business measures they are focusing on. We call it Vistance Analytics and Learning. You can take a peak here.
I had a hand in researching the recognition behaviors, creating the manager assessment tool, designing the bite-sized online learning content, all helping to prescribe the award winning learning in the right order needed for each respective learner to improve their recognition giving.
And it works!
Hope this has helped you make sense of analytics as it applies to employee recognition.
It is time to move up the analytics development curve and expect prescriptive analytics from your recognition programs.
Question: What type of analytics do your recognition programs mostly utilize?
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