By Jason Kuo, Group Solutions Marketing Manager, SAP
The biggest change is the proliferation of big data. Organizations are finding competitive advantage in extracting value from data explosion – whether typical enterprise transactional data or unstructured data from external sources like Hadoop, sensor, or social. At the same time, in-memory technology has dramatically reduced the time and cost of data processing and makes it possible to perform predictive analytics against vast volumes of data in real-time. Finally, the spectacular growth of R – an open source statistical and data mining language – has empowered a fresh breed of data scientists, placing a vast array of analytical possibilities at their fingertips.
In the white paper Seven Reasons You Need Predictive Analytics Today, Eric Siegel (president, Prediction Impact, Inc. and chair, Predictive Analytics World) discusses seven strategic objectives that companies can attain by employing predictive analytics.
1. Compete – Secure the Most Powerful and Unique Competitive Stronghold
Predictive analytics gives your enterprise a powerful, unique, and proprietary source of business intelligence to use in sales and for customer retention. It also helps you identify where competitors are falling short (their weaknesses and constraints), helping you attack those weak points and gain better market share.
2. Grow –Increase Sales and Retaining Customers
Using predictive analysis in marketing, sales, and customer retention applications is the core value proposition of predictive analytics, aiding all the industry verticals. Which customer segments would be interested in which product, and how should you structure the offer are all determined and proposed via predictive analytics. This knowledge helps organizations decide where to focus and grow their business faster than competitors.
3. Enforce – Maintain Business Integrity by Managing Fraud
Fraudulent transactions involving invoices, credit card purchases, tax returns, insurance claims, mobile phone calls, online ad clicks, and consumer banking checks incur great cost – across all industries. Predictive analytics is very effective at detecting these risks, enabling you to proactively mitigate them.
4. Improve – Advance Your Core Business Capacity Competitively
Predictive analytics improves product manufacturing, testing, and repair in many ways, such as detecting faulty items on the assembly line during production.
5. Satisfy – Meet Today’s Escalating Consumer Expectations
Predictive analytics provides greater relevancy through more precisely targeted marketing; thus it appeals and satisfies the customer expectations. Improvements delivered by analytical quality control, reliability modeling, streamlined services, and expedited application processing all lead to improved customer satisfaction.
6. Learn – Employ Today’s Most Advanced Analytics
The ability to learn from experience distinguishes predictive analytics from other business intelligence and analytics techniques. Predictive modeling delivers a robust predictive model for critical goals such as customer churn, based on your organization’s data.
7. Act – Render Business Intelligence and Analytics Truly Actionable
Standard reporting doesn’t always deliver readily actionable insights – human judgment is needed. Predictive analytics is specifically designed to generate “conclusive action imperatives.” Predictive score can drive the action to be taken with each customer, making predictive analytics an incredibly actionable form of business intelligence.
Learn more about the predictive analytics capabilities from SAP, including those in SAP HANA and the new SAP Predictive Analysis, by checking out these video, demo, and product resources.