7 reasons why you need predictive analytics today
How did you learn to keep your hands away from hot stoves? If you burned yourself on a stovetop when you were younger, then you now use the knowledge gained from that experience to predict what will happen if you accidentally touch a burner: You know to stay away. On a more complex level, predictive analytics does the same thing for your business. It uses your data to make connections based on past experience, and it applies that information to make predictions about what will affect your company in the future. The effects of employing predictive analytics can be astounding—so much so that some people are calling predictive analytics a necessary capability for keeping a competitive edge. The following are seven reasons why your business needs predictive analytics today.
1. Secure a competitive stronghold
Predictive analytics help you play to your company’s strengths and your competitors’ weaknesses. By tapping into the data surrounding your company’s experience, you generate insights unique to how you perform. Such insights are not outside common knowledge; rather, they provide a deeper awareness of how you are successful and where your organization’s individual advantage lies.
Alternatively, predictive analytics allows you to evaluate the actions of consumers who have been exposed to not only your but also your competitors’ marketing and sales. The modeling process learns to distinguish between customers who choose you versus those who select a competitor, identifying what factors played into their decision. It helps you play to your own strengths, pinpointing the areas where your competitors are failing.
2. Increase sales and retain customers competitively
Predictive analytics gives your company the knowledge needed to target customers with the proper message at the right moment. By predictively scoring customers based on their next likely action—whether purchase or churn—you can more effectively spend your messaging dollars. Slash costs on your marketing messages and increase response rate by suppressing those who are deemed unlikely to respond based on their predictive score. Similarly, offer discount codes only to those who are about to leave, saving the cost of distributing to a wider audience.
3. Maintain business integrity by managing fraud
Fraud investigators can look into only a set number of cases each week. With predictive analytics, you can use your company’s past experience with fraud cases to score transactions according to their level of risk. More precisely, narrowing the number of potential fraud cases more effectively uses investigators’ time while leading to fewer false positives and more fraud cases detected.
4. Advance your core business capability competitively
The next step to growth beyond increased sales is to improve your company’s core offering and how it is delivered. This could mean different things across different industries, but at its core, it focuses on using predictive analytics to optimize your approach to market. In the field of insurance, this could mean more effectively driving selection and pricing decisions by accurately identifying customers who pose a greater risk of submitting higher aggregate claims. Additional examples in other industries include predicting inventory demand, predicting health risk for proactive healthcare and predicting likely or swing voters for optimization of political marketing campaigns.
5. Meet today’s escalating consumer expectations
If you have ever received an email from a company familiar to you and been frustrated at its lack of relevance to you, then you already understand the benefit that predictive analytics brings to consumers. Unsolicited and untargeted communications are less and less tolerated by consumers, and personalized recommendations are expected—even relied on. Predictive analytics also addresses consumer security expectations, heightening security while triggering fewer false alarms. A positive customer experience enabled by predictive analytics paves the way for a growing and loyal customer base.
6. Do more than evaluate the past—learn from it
When employing analytics to evaluate your company’s performance, most standard methods will summarize past success or failure. But predictive analytics can create learning from past experiences, recognizing mathematical trends and patterns among your data and using them to anticipate unforeseen and forthcoming eventualities. The ability to integrate social data and unstructured text provides a deeper understanding of how consumers engage with and influence others in their social circles, creating the opportunity to predict churn or potential customers based on historical data.
7. Render business intelligence and analytics truly actionable
Standard metrics and reporting provide valuable insight to decision makers. Predictive analytics takes things a step farther by employing scores specifically designed to suggest an action.
"Value comes only when the insights gained from analysis are put to action to drive improved decisions. Best practice is to use decision management to embed predictive analytic models in operational systems."—James Taylor, coauthor of Smart (Enough) Systems
To see the capability of predictive analytics in action, watch a demo of IBM Cognos TM1 integrated with business intelligence and predictive analytics capabilities.
Adapted from a report by Eric Siegel, PhD, founder of Predictive Analytics World and author of the bestselling, award-winning Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die.