Better Than a Magic 8 Ball
Predictive analytics turns insight into action
How many times have you heard the saying, “If I could predict the future, I’d be rich” and dismissed it as impossible? While most of us don’t feel bestowed with magical powers, the evolution of our world, especially over the last 10 years, has brought us into a new power position for prediction. Several important shifts make prediction possible for many companies. Our society is conducting more business online, like buying products and banking, and electronically initiating and recording more life experiences, like doctor visits and finding people to date. And computing power and storage solutions have become faster and more affordable. Together, these changes have produced a huge volume of new data and information with faster ways to access it. At the same time, advances in software technology make it easier than ever to turn business and customer information into new insights that will help you see what the future could be. The company that replaces its Magic 8 Ball with a predictive-analytics solution will hold a powerful competitive position.
In this article, you’ll learn how predictive analytics can solve critical business problems and how IBM SPSS* products provide essential predictive-analytics capabilities. Deploying production-ready environments is important for success, thus you’ll learn the basics on how predictive analytics drives IT requirements and how to prepare for deployments.
Predictive Analytics is Important to Businesses
Business analytics helps turn relevant information into insights for smarter business decisions that either solve specific business problems or achieve specific business goals. Predictive analytics provides a deeper level of analysis using past events, mathematical data, patterns and statistical models to illustrate trends and predict outcomes. Prediction can improve business decisions, profitability and competitive advantage. Predictive analytics can be applied to a variety of business situations and industries. It’s proven useful with industry-specific issues and those applicable to many businesses, such as customer management (acquiring new customers, and retaining and growing existing ones), and risk and fraud management.
Let’s examine a specific business problem in the retail industry to learn more about how predictive analytics works. ShopSports (a fictitious company) is an online sports retailer that wants to determine how to best sell more sports equipment to existing customers to increase revenue. It’s tried a variety of unsuccessful marketing programs, resulting in wasted time and money. The store is eager to find a way to identify which customers are willing to buy more sports equipment, what they are most likely to buy and what’s required to get them to make the next purchase. This insight is critical for the marketing team, which needs to design its next offer. The store has saved several years’ worth of sales and customer data online, which will be useful. It’s decided to implement an IBM SPSS predictive-analytics solution.
The predictive model now contains the insight that can be applied to real customers to determine the best marketing offer.
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