Predictive Analytics Business Game Changer
Forrester Research evaluated 13 big data predictive analytics solution vendors and IBM emerged as one of the three leaders.
Predictive analytics has never been more relevant, and easier, than it is now. Big data, gobs of compute power, and modern tools are making predictive models more efficient, accurate, and accessible to enterprises.
Analytics: why do it?
Because enterprises that predict will win, retain, and serve customers better than those that don’t. That’s the bottom line of every business — serve customers better than your competitors.
Enterprises must gain predictive powers in three areas:
1. Provide direct insights about customers and business processes
Dashboards and reporting are the most common use for predictive analytics within organizations today. Exposing information on causative trends and projections into the future, many traditional business intelligence vendor tools contain simple predictive models. These tools surface valuable information to managers and executives, but often lack the link to business decisions, process optimization, customer experience, or any other action based on the predictive insights.
2. Intelligent, adaptable customer interactions and business processes
If organizations don’t use predictions to change the future, then they’re making their data scientists as helpless as Troy’s Cassandra. Today’s top predictive analytics tools can deploy their models or scoring engines into the applications where there is a need for insights. Today, organizations are using predictive to enhance business processes by detecting fraud at the moment of swiping at point-of-service, automatically adjusting digital content based on user context, or initiating proactive customer service for at-risk revenue sources.
3. Re-imagine customer engagement and inspire new digital products
The potential utility of predictive analytics goes far beyond the mainstream uses most companies focus on today. Model building and deployment continue to accelerate, enabling application developers to use predictive analytics quickly and with increasing ubiquity in deployed applications. Compounded with the use of app data, developers are able to focus features and bugs that predict the greatest customer value and anticipate the impact of new app functionality or aesthetics.
Read the complete report to learn
- Why predictive analytics is a game changer
- The six steps of predictive analytics
- Which vendors are Leaders, which are Strong Performers and which are Contenders
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