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Predictive Analytics

How to Jump-Start Your Predictive Analytics Initiatives

By Sriram Parthasarathy | August 7, 2018

In today’s fast-paced digital world, technologies change every two years or less. That’s why innovation and adaptation have become critical components of a successful software business.

Companies must adapt to stay in business—or get wiped out by those that do.

In a recent survey of 500 application teams, predictive analytics was the number one feature currently being added to product roadmaps. This is very good for companies already embracing the predictive wave, but it’s likely a rude wake-up call for those that have been slow to take action.

>> Related: What Is Predictive Analytics? <<

Is your company embracing predictive analytics? If not, it’s not too late to start. Here’s a framework to identify projects within your company that may benefit from predictive analytics:

PADS Framework for Identifying Predictive Initiatives

The key ingredient to identifying projects suited for predictive analytics is that there must be some measurable value added as well as a clear return on investment (ROI) in a defined timeframe. You can leverage the PADS framework (shown here) to easily identify predictive problems that solve critical business needs.

The four categories in the PADS framework are: 

1. Preventing problems before they happen by identifying outcomes. Common examples include: Will this customer churn? Will this customer pay their invoice late? Will this patient need a diabetes screening? By predicting the possible outcome, we can deploy preventive strategies to reduce any negative results and help the business grow.

2. Assisting humans with actionable intelligence. A problem in most workplaces is that experienced professionals retire and take valuable institutional knowledge with them. That’s why companies are looking to create predictive models that capture how decisions are made. By digitizing this collective knowledge over time, you can help future employees make better decisions. The common example is loan approval: Financial institutions are creating models that capture how loans are approved and provide recommendations for loan officers.

3. Detecting problems that are currently happening. Not everything can be predicted and prevented. In cases where negative outcomes are common—or even intrinsic to the nature of the case—it’s important to identify the case and take action quickly. An example is a fraudulent transaction or insurance claim that must be identified and acted upon in real time. Such transactions must be able to be caught at different stages of processing.

4. Streamlining services. This is very helpful in realigning human and non-human resources to improve customer services or processes. An example is the ability to predict the number of sales orders in a given week so that you can align resources and inventory to meet demand. Another example is predicting the number of customer service calls on a given day to align call center resources. Or, in a healthcare setting, you might predict how many patients will cancel their appointments in order to avoid wasting valuable staff resources.

As you can see, there is a clear ROI attached to each of the above categories. In each case, we are detecting and/or preventing a problem and acting upon information to create the best outcome. It’s also easy to articulate the value of each problem and solution, which opens up a clear path to drive further investment, alignment, and momentum.

These are just a few examples—there are many other interesting problems predictive analytics can solve. But this should give you an idea of how you can enhance your application by jump-starting your predictive analytics initiatives.

See how Logi can help with your predictive analytics needs. Sign up for a free demo of Logi Predict today >

About the Author

Sriram Parthasarathy is the Senior Director of Predictive Analytics at Logi Analytics. Prior to working at Logi, Sriram was a practicing data scientist, implementing and advising companies in healthcare and financial services for their use of Predictive Analytics. Prior to that, Sriram was with MicroStrategy for over a decade, where he led and launched several product modules/offerings to the market.

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