When to Apply Predictive Analytics

We love predictive analytics and if we are not careful, it is easy to start reducing everything into predictive models. I have even caught Tadd standing at the window collecting primary data on the smoking habits of the people in our building. To make sure that CAN and predictive analytics experience continued success, we have developed a guide for when to apply predictive analytics.

First, predictive analytics should not be applied if:

The cost of being wrong is low.

You should not apply predictive analytics if reducing uncertainty does not provide enough value. Predictive models should only be applied in situations with a high cost and/or probability of being wrong and where predictive analytics can provide information to reduce uncertainty. To determine if predictive analytics is worth applying to a decision you need to calculate the expected value of information. In the book How to Measure Anything, Hubbard provides the following formula, expected value of information is equal to the difference between the expected opportunity loss before and after information. The expected opportunity loss is equal to the chance of being wrong multiplied by the cost of being wrong.

The relationships are obvious.

A predictive model is basically a story about why something happens and what will most likely happen in the future. With this in mind, you do not need to develop models if people are able to accurately describe the relationships between variables well enough that they can tell stories about why certain things are or are not happening.

The model can not be Reproduced.

Even if you develop a model, but it can’t be reliably reproduced then you should admit that predictive analytics can not be applied.
Predictive models find and tell stories that are difficult for people to discover because there are either too many variables or the events being studied are too rare. The value in knowing the story of why something happens is that you can reduce uncertainty, make better decisions and work smart.

The following are situations when predictive analytics should be applied:

Too Much Information.

Over the last 30 years there has been an explosion of data, and business intelligence has been focused on collecting and presenting that data. Predictive analytics is one of the BI tools that is capable of statistically filtering out what data is most important.
Customer management is an example of when predictive analytics can be used to find valuable patterns in an overwhelming amount of data. Predictive analytics can be used to determine how to group customers, and develop campaigns to improve specific behavior for each client segment. For example, CAN sorted through over 500 different variables and millions of observations to determine the 5 variables that have a significant impact on responses to specific market campaigns.

Rare but Important Events.

When an event is rare and results in either major gains or losses, it can be very beneficial to gain a better understanding of why the event happens. Predictive analytics can help you develop plans to encourage or discourage specific rare events.
Predicting the failure of essential business processes is an example of when predictive analytics can be used to find valuable patterns in rare but important events. Using predictive analytics CAN’s clients are able replace equipment and parts before they breakdown so that essential business operations continue to move forward. Imagine being able to know and replace parts at risk of failing before you spend the time and money to install a piece of heavy construction, mining or logging equipment on site.
In conclusion, the next step in the evolution of business intelligence is to understand what is likely to happen. Predictive analytics allows executives to learn from the cumulative knowledge of their organization. This systematized learning has the potential to help businesses and executives to make decisions that are less wrong, so that they can work smart. However, it is important that predictive analytics is applied in the right applications, so that is produces the most value to the end users.
 
Contact us to learn if predictive analytics are right for your organization.