Predictive Analytics: Answering the Age-Old Question, What Should We Do Next? Current Challenges As organizations strive to meet today s most pressing challenges, they are increasingly shifting to data-driven decision making to help them develop a competitive edge. Forward-thinking companies look to predictive and real-time analytics to gain insights about what will likely happen next so they can take the right actions to drive optimal outcomes. Table of Contents: Current Challenges Leveraging Datas Applying Analytics Success Stories Taking Action Organizations of all types across all industries face the same imperatives: to provide an optimal customer experience, to develop tailored services and products and, ultimately, to achieve competitive advantage. But meeting those needs is far from easy. Over the past 10 years, the relationship between the customer and the organization has suffered, says Erick Brethenoux, executive program director of worldwide predictive analytics and decision management strategy at IBM. You must listen to what customers say. You must understand their problems, their attitudes, their opinions and their interactions in a much deeper and complex way. At the same time, company operations must run at peak efficiency while the volatile world economic climate, increasing incidence of fraud and rapidly changing business models combine to make doing business riskier than ever before. Organizations operations have to be very agile and very lean in order to provide the service that customers demand at a cost the company can afford, Brethenoux explains, [and] it s critical to be as prepared as possible to respond to varying situations to minimize both risk and cost.
Against this challenging business backdrop, every decision matters. Within the largest enterprises millions of decisions are made each day, notes Hervé Dhelin, EMEA predictive analytics marketing manager at IBM. It has become essential that these decisions are backed by information that provides critical insight into the decision-making process, Dhelin explains. Organizational complexity is a barrier to achieving that sort of information flow at many organizations. In particular, simply handling the exploding amount of data flowing in from different sources has become almost overwhelming. Organizing the data is one daunting challenge; normalizing the data so that everyone in an organization can use it is another. Figuring out how that data can be used for the company s benefit is yet another. While organizations continue to wrestle with these substantial and pervasive internal and external challenges, many have recognized that the key to working through these problems lies in the data itself. These companies have shifted to datadriven decision making instead of basing their important decisions on guesswork. And forward-thinking companies are increasingly looking to predictive and real-time analytics to understand what will likely happen next so that they can take action to drive optimal outcomes. The Case for Predictive Analytics Although its influx can be overwhelming, data has become a mighty asset for companies that can harness its power. Predictive analytics gives organizations a way to turn mountains of structured and unstructured data into actionable insights, enabling them to predict what event is likely to happen next and take actions that will help them optimize the outcome. Analytics works by applying quantitative methods to gain insights from data. Predictive analytics forecasts the future by looking at past and real-time data to determine what is likely to happen; it s a data-driven crystal ball that helps employees make the best business decisions. Predictive analytics has emerged as an essential tool for organizations looking to achieve competitive advantage. Organizations that use predictive analytics can get a better handle on their customers preferences so they can offer targeted products and services; leveraging existing data helps determine customers future behavior, giving the company the needed insight for maximizing cross-sell and up-sell success. Organizations that use predictive analytics can also better retain existing customers and attract prospects by
identifying their future needs and improving the overall customer experience. Other uses include: Data driven risk management. Predictive analytics enables organizations to look at past customer behavior and rank customers by level of risk, as well as letting them examine negative outcomes to decrease future risk. Predictive maintenance. In some industries, analytics can enable predictive equipment maintenance. When companies can predict the likely failure point of large machinery, aircraft or fleets of vehicles, they can avoid being caught off guard. Inventory management. In retail applications, integrated analytics can predict what customers are likely to buy next, so firms can ensure that inventory matches product recommendations and marketing efforts. Crime prevention. Predictive analytics can help municipalities detect crime patterns and enable police departments to thwart crimes before they happen. Health care. Predictive analytics can help create better patient outcomes. Top-performing companies use analytics five times more often than lower performers, according to the recent IBM Institute for Business Value and MIT Sloan Management Review study, Analytics: The New Path to Value. What s more, half of survey respondents said that better information and analytics were top priorities in their organizations. Analytics both enables IT and helps line of business. Predictive analytics is a powerful ally for IT, giving it the means to enable every department to work smarter and perform better. When IT puts analytic capabilities into the hands of business users, all areas within an organization become predictive.
Applying Predictive Analytics The wealth of data that organizations gather every day provides the foundation for predictive analytics and data mining. To be truly successful, predictive analytics implementations require three constituents: those who work with the analytics, line of business and IT. IT plays a key role by leveraging analytics across an organization, getting the data, ensuring the computing power and facilitating deployment. IT plays an absolutely critical role in enabling a business to run in the most efficient way possible, IBM s Brethenoux points out, and predictive analytics goes a long way in enabling efficiency. Most of the time, organizations continue to use legacy systems to provide decision management. Each time that an organization adds a decision management system to its ecosystem, it needs to update some programs, which adds substantial work for IT and significantly slows the process. An effective predictive analytics solution enables all line-ofbusiness personnel to adapt the system to satisfy their particular needs, notes Dhelin. Each department can make changes on its own, instead of relying on IT to tweak the system because of changing market conditions, he explains. This frees up considerable time for IT employees. As IT staff already know, implementing a solution capable of delivering a prediction in almost real time within a business process can be a nightmare. However, a solid predictive analytics solution can swiftly accomplish this task. While implementing predictive analytics requires an initial cash outlay, the payback is, in most cases, less than one year. In fact, according to an independent assessment conducted by Nucleus Research, 94 percent of IBM Predictive Analytics customers achieved a positive return on investment (ROI) with an impressive average payback period of just 10.7 months. The report attributes these returns to reduced costs, accelerated productivity, and increased employee and customer satisfaction. Implementing a predictive analytics solution need not be arduous or complicated. The IBM SPSS portfolio of predictive and advanced analytics software, for instance, is based on open standards, which ensures that it can work with any operational system an organization already has in place. The deployment is completely open and requires no special skills or additional investments, enabling organizations to maximize their previous IT investments.
We don t want to disrupt systems already in place, says Brethenoux. Predictive analytics simply augments the IT environment, adding value to investments that organizations made years ago. Many organizations that participated in the Nucleus Research study started with a few seats and expanded as both knowledge and use of the solution grew. In other words, when an organization deploys predictive analytics on a small scale, other departments and lines of business begin to demand the solution once they see what the solution can do. Success Stories Generating Accurate and Actionable Business Intelligence Organizations of all types are implementing predictive analytics to improve the customer experience, increase customer revenues, optimize operations and proactively detect fraud. Memphis-based First Tennessee Bank had been spending thousands of dollars annually on its product-centric direct marketing campaigns. The bank suspected it was wasting money on inefficient marketing campaigns. First Tennessee Bank opted to transition to a customer-centric marketing model, which used knowledge of customers and their behavior. This model enabled the bank to use the wealth of data its direct marketing staff was already collecting, adding data mining methodology to its direct marketing strategy. The bank purchased IBM SPSS Statistics and IBM SPSS Modeler, data mining software that enables organizations to create and apply predictive models that generate accurate and actionable insights. The software s predictive modeling capability enables the bank to spark demand by specifically targeting customers that are likely to buy new products.
IBM SPSS predictive analytics is enabling First Tennessee to gain an unprecedented level of insight from our data, making our marketing campaigns more efficient and profitable, says Tanner Mueller, First Tennessee Bank s direct marketing database manager. IBM SPSS predictive analytics solutions empowered the bank to truly understand its customers and optimize conversations in ways that were relevant to customers and profitable to the bank. By better targeting customers, the bank s marketing staff was able to decrease the quantity of pieces the bank mailed out, while increasing the frequency of the mailings. It also extended the planning cycle for campaigns and established a policy of targeting all products every month. IBM SPSS Modeler helped the bank reduce its direct mailing costs by 20 percent while increasing the customer response rate by 3.1 percent. Once you re customer-centric, you can choose the right channel, says Mueller. You can take steps forward because you can make adjustments using modeling and switch your marketing strategies when you need to. (See Banking on Knowledge for more information.) Data Mining Helps Optimize Vehicle Quality Luxury vehicle manufacturer BMW Group strives to attract customers with its innovative designs and quality construction. This goal requires the firm to continually evaluate and assess its products and services while considering its customers opinions. BMW needed to manage and efficiently analyze vast quantities of data from sources such as vehicle error memories, dealer feedback and repair reports, in order to obtain meaningful data to fuel the improvement process. The firm opted for IBM SPSS data and text mining software to develop an easy-to-use solution that can quickly and efficiently analyze data and combine the results. These results are then available to a wide circle of users. The solution can handle several thousand queries within a short period of time, allowing specific analyses to be run on large volumes of data. Pattern recognition and statistical and mathematical processes identify new correlations and trends. BMW runs many different analyses on the platform, including analyses of repeat data information on the types of repairs that bring customers to the service department most frequently. These analyses provide the auto maker with new insights that can help its research and production processes. The firm can also use data mining processes to analyze fuel consumption data. In general, it s about making various processes transparent, says Michael Unger, key account manager, Predictive Analytics, at IBM in Germany. Success can then be measured wherever data is generated. The longer term goal is, of course,
to improve BMW s performance in all areas and thus further consolidate its success. (See BMW Group Uses IBM SPSS to Consolidate Its Competitive Position for more details.) Identifying Fraudulent Claims With the incidence of claims fraud on the rise, insurers have become increasingly reliant on their ability to identify fraudulent claims to remain profitable. And, says Bill Dibble, senior vice president in Claims Operations at Infinity P&C, using predictive analytics to serve the insurer s legitimate claimants has been of equal importance. A key benefit of the IBM SPSS system is its ability to continually analyze and score these claims, which helps ensure that we get the claim to the right adjuster at the right time, he says. (See Smarter Insurance for more information). Infinity Property & Casualty Corporation, a provider of nonstandard personal automobile insurance, wanted to improve its ability to identify fraudulent claims to make better use of its investigative staff and to pay legitimate claims more quickly. The firm also aimed to reduce its high monthly costs for outsourced subrogation. Infinity P&C immediately realized benefits when it implemented IBM SPSS predictive analytics solutions. Using IBM SPSS Modeler, Collaboration and Deployment Services, and Decision Management, the insurer saw a reduction in claims payments and an improvement in customer service. The IBM SPSS predictive analytics solutions have doubled the accuracy of the insurer s fraud identification, contributing to a return on investment of 403 percent. In addition, the referral time to send claims to Infinity s Special Investigative Unit has dropped from 45 to 60 days to a mere one to three days.
Taking Action Organizations of every type and size have the opportunity to use predictive analytics to empower employees to make decisions based on actionable insights on what will likely happen in the future. The first step in choosing a predictive analytics solution is to ask the right questions, according to IBM s Brethenoux. First, organizations must identify the problem that they are trying to solve, he says. Next, they must get executive-level sponsorship. Organizations should also ensure that a new predictive analytics solution: Is easy to use. All employees should be able to leverage predictive analytics across the enterprise, enabling marketers and others to access information and create predictive models without additional IT support. Integrates smoothly and seamlessly into their existing information system. The impact of the implementation should be as minimal as possible, says Dhelin. And the integration should be as transparent as possible. Incorporates an intuitive user interface. Users should not need to learn any new language. Offers a complete platform. Easily integrates with other applications. IBM Business Analytics software delivers actionable insights that decision makers need to achieve better business performance. IBM offers a comprehensive, unified portfolio of business intelligence, predictive and advanced analytics, financial performance and strategy management, governance, risk and compliance, and analytic applications. With IBM software, companies can spot trends, patterns and anomalies, compare what if scenarios, predict potential threats and opportunities, identify and manage key business risks and plan, budget and forecast resources. With these deep analytic capabilities, our customers around the world can better understand, anticipate and shape business outcomes. For further information or to reach a representative, please visit ibm.com/analytics.
Resources IBM Business Analytics Banking on Knowledge BMW Group Uses IBM SPSS to Consolidate Its Competitive Position Smarter Insurance