Building smart business on the four pillars of predictive analytics
Building a successful business in the 21st century demands that your organization harness the power of predictive analytics to drive every campaign, operation, process and decision. The crux of the matter is whether you have confidence in the predictive insights you’ve derived from application of statistical models to your data sets. In the blog post "Decision confidence: Where the predictive chickens come home to roost" from a few years ago, I examined the role of predictive modeling in building confidence in business decisions.
Organizations of every shape and size are using predictive modeling to transform their cultures and disrupt their markets. The technology has been eagerly embraced as core power tools by a new breed of data scientists. They use these tools to sift through big data to find historical trends, chart alternate predictive scenarios, drive real-world experiments, flag potential risks and identify new opportunities. In scoping out key strategic priorities for deployment of predictive analytics, focusing on these four make-or-break considerations is vital:
- Strengthening customer loyalty and retention
- Boosting staff productivity and efficiency
- Minimizing and mitigating fraud
- Growing revenues and delivering superior financial results
Taken together, these strategic imperatives represent the four pillars of smart, proactive business. Beginning the week of January 25, 2016, the IBM Big Data & Analytics Hub is publishing a series of posts that explore each of these pillars in turn. Be sure to check out the first blog post in this series by Christine O’Connor, who discusses how predictive insights from customer data are improving customer retention by enabling highly effective sales through right-time, right-message engagement.
The week of February 1, 2016, Jane Hendricks discusses the pillar of human capital optimization. In that blog post, Hendricks describes how human resources professionals can use predictive analytics to generate insights in support for improvements in their recruitment, hiring and employee-retention initiatives.
Michael Sauceda turns to the third pillar of antifraud the week of February 8, 2016. The focus in this blog post is using predictive analytics tools such as IBM SPSS Modeler to detect, prevent, minimize and mitigate fraud. Sauceda’s discussion describes how forward-looking insurance firms are leveraging a multistage predictive analytics process that scrutinizes claims based on insight into fraudulent patterns and claims data.
And then the week of February 15, 2016, Luciane Ellis rounds out this four-part series with case studies highlighting how organizations in the healthcare and consumer packaged goods (CPG) industries have boosted bottom-line results from smart application of predictive analytics to key business initiatives.
Learn more about the power of IBM SPSS predictive analytics for optimizing the impact of every business decision. Also, be sure to check out this excellent Ventana Research benchmark on next-generation predictive analytics.