It has been helpful in answering questions about the past and to a certain extent in explaining current events, but traditional BI doesn’t provide any insight into the future. Historically, BI has helped businesses answer questions such as what happened, why did it happen, what was the problem and what actions are needed. Predictive analytics goes beyond this and helps the business with what is happening in real-time and what will happen next.
See Related Graphic 1: Predictive Analytics for What's Happening and What's Next
Predictive analytics is forward-looking and it provides actionable predictions based on trends, patterns, relations and correlations in data. It enriches the decision-making process in a big way by making the intelligent prediction available as the basis for the decision-making process. At a high level, predictive analytics applies a mathematical modeling and statistical analysis approach to the data to develop knowledge that can be used to predict future events. A very common example of predictive analytics is the one used by credit bureaus to develop credit scores for consumers by predicting the customer behavior.
Role in Advancing Business: Actionable Insights and Predictions
“In business, as in baseball, the question isn’t whether or not you’ll jump into analytics. The question is when. Do you want to ride the analytics horse to profitability … or follow it with a shovel?" – Rob Neyer, author and senior writer, ESPN.com
Predictive analytics has great commercial, scientific and social value. The purpose could be to decide the product marketing strategy, customer retention, risk management or product pricing. This exercise can’t be completed in isolation with the use of data, technology and statistical models alone. Business acumen plays a significant role in formulating insightful predictions that make sense and deliver to meet the business requirements. The ultimate purpose of the analytics is to help deliver on strategy and achieve defined goals.
It’s essential to define what to predict and how to use the predictions. There are a wide variety of trends, patterns and behaviors that can be identified, but the focus should be on getting the actionable insight according to the defined objective. Start with defining the objective of the data mining and predictive analytics, which could be risk management, fraud detection or antiterrorism. The following are some examples where predictive analytics is widely being used.
Strategic business planning: Predictive analytics enhances strategic business planning by eliminating the reliance on mere averages or guesswork and setting the focus on actionable intelligence. It enables businesses to better manage risk and uncertainty and helps with decision-making to manage the likely future events. Smart companies are integrating predictive analytics with decision-making processes to improve strategic-planning processes. Embedding the ability to intelligently predict the uncertain future and the ability to measure the impact of this uncertainty in strategic planning goes a long way in helping companies achieve their business objectives.
Risk: Adoption of predictive analytics lets companies effectively respond to changes in risk drivers and actually manage all material risks related to competition, capital markets and regulatory bodies. Predictive modeling makes risk data more relevant and actionable. It is widely used to predict risk for credit loan applications and plays a significant role in managing risk and deciding insurance policy premiums based on customer demography, financial information and claims history. In rapidly changing markets, the reward for being able to manage risk quickly and effectively is greater than ever.
Audit and fraud detection: In cases of audit and fraud detection, the goal could be to catch the incidence of fraud before it occurs or apply the predictive models to the large number of transactions to identify the ones that are more likely to be fraudulent. This is helping companies change their approach from being corrective to preventive in fraud management.
Medicine: Role of predictive analytics in patient, physician and facility management has been growing as the cost of medical care has come under scrutiny. In the field of medicine, it is being used in disease management to improve provider partnership and strengthen customer relationships. Patient profiles are used by physicians to predict hospital admission likelihood. Data mining is being used by hospitals to predict the length of hospital stay, which helps them better manage the patients, physicians and the facilities.
Miscellaneous: Predictive models are also being used in a wide range of scientific and social initiatives. Scientists use it to refine their predictions of global climate change. Knowledge discovery from spatial data is leading to a new era of climate change predictions. Predictive models are being used to help manage resources for student, faculty and curriculum planning in educational institutions. It is being used to help manage the natural resources and minerals. And it is assisting in predicting food contamination and disease outbreaks.
Customer Relationship Value and the Path to Profitability
“The pillars of effective cross-selling are strengthened and enhanced by the effective application of predictive modeling and analytics.” – “The Intelligent Contact Center: Using Predictive Analytics to Generate Growth,” Christopher Checco and David Rook, Customer Chemistry
By capitalizing on the rigor of predictive analytics, companies can align business strategy with customer acquisition, development and retention programs to manage one of their most precious assets: customers. Individual business goals could be to sign up new customers, cross sell, up sell, reduce customer churn, etc. Retailers frequently use data mining and predictive modeling to identify customer behavior patterns and use this information discovery to target customers for direct marketing offers. This helps in building a competitive position, developing plans to maximize sales and increasing revenue.