Predictive Analytics

Predictive Analytics for Business Applications

By Sriram Parthasarathy
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The world’s favorite applications use predictive analytics to guide users—even when they don’t realize it. If you’ve ever used a flight cost predictor like Google Flights or browsed through movie recommendations on Netflix, you’ve benefited from predictive analytics.

Predictive analytics has also made its way into business applications. In fact, it’s the #1 feature on product roadmaps, according to Logi’s 2018 State of Embedded Analytics Report. So many application teams are including predictive analytics capabilities in their software because of the enormous value it offers to end users and application teams alike.

What Is Predictive Analytics?

At its core, predictive analytics answers the question, “What is most likely to happen based on my current data, and what can I do to change that outcome?” A mathematical model uses historical data to identify key trends and patterns to predict what will happen in the future.

predictive analytics

What’s the Value of Predictive?

For end users, predictive analytics can give them insights and suggest actions that directly impact operations, revenue, and risk assessment. It applies to business applications for a wide range of use cases across various industries. Some scenarios include:

  • Reducing customer churn: A sales application with predictive analytics could analyze regular customer behaviors and alert the sales professional when a customer is likely to churn out. Targeted promotions can then be deployed effectively.
  • Detecting fraud: In finance, predictive analytics helps identify potentially fraudulent behavior before it happens. Banks can predict loan defaults, approve credit, or detect suspicious activities.
  • Reducing machine downtime: Predictive analytics can improve production capacity and reduce downtime by analyzing historical and online data from production lines.
  • Flagging high-risk healthcare patients: Hospitals and physicians can identify high-risk patients to prioritize for screening and recommend preventative treatments. This also consequently reduces hospital readmissions.

For businesses, adding machine learning and artificial intelligence into your application sets you apart from the competition and allows end users to make better decisions, which in turn, gives you the opportunity to increase revenue. Over 90 percent of business leaders expect to see new business value from artificial intelligence implementations in the coming five years, according to a recent survey from the MIT Sloan Management Review, in partnership with BCG Henderson Institute.

How Do You Get Started in Predictive?

Predictive analytics is a complex capability, and therefore implementing it is also complicated and comes with challenges. When companies take a traditional approach to predictive analytics (meaning they treat it like any other type of analytics), they often hit roadblocks, such as:

  • The need for a data scientist with statistical modeling expertise
  • A multi-step process every time you do an update or release
  • A failure to let users take immediate action from inside the predictive application
  • A steep learning curve, leading to low user adoption

Just like any other new feature or capability that you introduce through your software, if you want your end users to use it, you need to meet them where they are—in the applications where they already spend their time. If predictive analytics lives as a standalone or separate tool, it will simply never get adopted.

To address challenges around user adoption, distribution of predictive analytics, and closing the insight-to-action gap, you need to embed predictive analytics directly into your application. This will allow end users to quickly and efficiently see what is going to happen in the future and subsequently act on it without leaving your application.

Even better, some emerging embedded predictive analytics tools are designed specifically for a range of users and do not require expertise in statistical modeling. Moreover, they help reduce the burden on application teams by streamlining a lengthy development.

Originally published August 20, 2019; updated on July 31st, 2020

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.