This is an MIT Sloan Management Review article. Historically, neither the creators nor the distributors of cultural products such as books or movies have used analytics -- data, statistics, predictive modeling -- to determine the likely success of their offerings. Instead, companies relied on the brilliance of tastemakers to predict and shape what people would buy. Creative judgment and expertise will always play a vital role in the creation, shaping and marketing of cultural products. But the balance between art and science is shifting. Today companies have unprecedented access to data and sophisticated technology that allows even the best-known experts to weigh factors and consider evidence that was unobtainable just a few years ago. And with increased cost and risk associated with the creation of cultural products, it has never been more important to get these decisions right. In this article, the authors describe the results of a study of prediction and recommendation efforts for a variety of cultural products. They discuss different approaches used to make predictions, the contexts in which these predictions are applied and the barriers to more extensive use, including the problem of decision making pre-creation. They then discuss two aspects of the prediction market. First, the need for better prediction for distributors of cultural products, and second, the potential for business models around prediction techniques.

This is an MIT Sloan Management Review article. When picking assessment tools to inform better decisions about future paths, executives are faced with a wide variety of options--some of which are well established, while others are in early stages of development. The authors provide an insider's guide to prediction and recommendation techniques and technologies. They cover prediction tools including attributized Bayesian analysis, biological responses analysis, cluster analysis, collaborative filtering, content-based filtering/decision trees, neural network analysis, prediction (or opinion) markets, regression analysis, social network-based recommendations and textual analytics. With each potential tool, they briefly describe the technique, who uses it and for what purpose, its strengths and weaknesses, and its future prospects as a prediction tool. Finally, the authors offer up an indication of the best time in the decision process to begin using the tool.

Health insurer IFA and grocery chain ShopSense have formed an intriguing partnership, but it threatens to test customers' tolerance for sharing personal information. For years, IFA's regional manager for West Coast operations, Laura Brickman, had been championing the use of customer analytics--drawing conclusions about consumer behaviors based on patterns found in collected data. She came away from a meeting with the grocer's analytics chief, Steve Worthington, convinced that ShopSense's customer loyalty card data could be meaningful. In a pilot test, Laura bought ten years' worth of data from the grocer and found some compelling correlations between purchases of unhealthy products and medical claims. Now she has to sell her company's senior team on buying more information. Her bosses have some concerns, however. If IFA came up with proprietary health findings, would the company have to share what it learned? Meanwhile, Steve is busy trying to work out details of the sale with executives at ShopSense. Many have expressed support, but COO Alan Atkins isn't so sure: If customers found out that the store was selling their data, they might stop using their cards, and the company would lose access to vital information. Though CEO Donna Greer agrees, she knows that if things go well, it could mean easy money. How can the two companies use the customer data responsibly? Commenting on this fictional case study in R0705A and R0705Z are George L. Jones, the CEO of Borders Group; Katherine N. Lemon, an associate professor of marketing at Boston College; David Norton, the senior vice president of relationship marketing for Harrah's Entertainment; and Michael B. McCallister, the president and CEO of Humana.

Health insurer IFA and grocery chain ShopSense have formed an intriguing partnership, but it threatens to test customers' tolerance for sharing personal information. For years, IFA's regional manager for West Coast operations, Laura Brickman, had been championing the use of customer analytics--drawing conclusions about consumer behaviors based on patterns found in collected data. She came away from a meeting with the grocer's analytics chief, Steve Worthington, convinced that ShopSense's customer loyalty card data could be meaningful. In a pilot test, Laura bought ten years' worth of data from the grocer and found some compelling correlations between purchases of unhealthy products and medical claims. Now she has to sell her company's senior team on buying more information. Her bosses have some concerns, however. If IFA came up with proprietary health findings, would the company have to share what it learned? Meanwhile, Steve is busy trying to work out details of the sale with executives at ShopSense. Many have expressed support, but COO Alan Atkins isn't so sure: If customers found out that the store was selling their data, they might stop using their cards, and the company would lose access to vital information. Though CEO Donna Greer agrees, she knows that if things go well, it could mean easy money. How can the two companies use the customer data responsibly? Commenting on this fictional case study in R0705A and R0705Z are George L. Jones, the CEO of Borders Group; Katherine N. Lemon, an associate professor of marketing at Boston College; David Norton, the senior vice president of relationship marketing for Harrah's Entertainment; and Michael B. McCallister, the president and CEO of Humana.

Health insurer IFA and grocery chain ShopSense have formed an intriguing partnership, but it threatens to test customers' tolerance for sharing personal information. For years, IFA's regional manager for West Coast operations, Laura Brickman, had been championing the use of customer analytics--drawing conclusions about consumer behaviors based on patterns found in collected data. She came away from a meeting with the grocer's analytics chief, Steve Worthington, convinced that ShopSense's customer loyalty card data could be meaningful. In a pilot test, Laura bought ten years' worth of data from the grocer and found some compelling correlations between purchases of unhealthy products and medical claims. Now she has to sell her company's senior team on buying more information. Her bosses have some concerns, however. If IFA came up with proprietary health findings, would the company have to share what it learned? Meanwhile, Steve is busy trying to work out details of the sale with executives at ShopSense. Many have expressed support, but COO Alan Atkins isn't so sure: If customers found out that the store was selling their data, they might stop using their cards, and the company would lose access to vital information. Though CEO Donna Greer agrees, she knows that if things go well, it could mean easy money. How can the two companies use the customer data responsibly? Commenting on this fictional case study in R0705A and R0705Z are George L. Jones, the CEO of Borders Group; Katherine N. Lemon, an associate professor of marketing at Boston College; David Norton, the senior vice president of relationship marketing for Harrah's Entertainment; and Michael B. McCallister, the president and CEO of Humana.

This is an MIT Sloan Management Review article. Futurists have long anticipated the day when computers would relieve managers and professionals of the need to make certain types of decisions. But automated decision making has been slow to materialize. Argues that automated decision making is finally coming of age and the new generation of applications differs substantially from prior decision-support systems. Today's applications are easier to create and manage than earlier systems. Rather than require people to identify the problems or to initiate the analysis, companies typically embed decision-making capabilities in the normal flow of work. Those systems then sense online data, apply codified knowledge or logic, and make decisions--all with minimal amounts of human intervention. They can help businesses generate decisions that are more consistent than those made by people, and they can help managers move quickly from insight to decision to action--helping companies reduce labor costs, leverage scarce expertise, improve quality, enforce policies, and respond to customers. As automating decisions becomes more feasible, organizations need to think about which decisions people must make and which can be computerized.

This is an MIT Sloan Management Review article. Many firms know about their customers, but few know the customers themselves or how to get new ones. Leaders in customer knowledge management go beyond transaction data, using a mix of techniques, and they aren't afraid to tackle difficult problems. The authors report results from interviews with 24 leading firms and describe seven practices that the leaders share. The companies interviewed--including Harley-Davidson, Procter & Gamble, and Wachovia Bank--have undertaken specific and successful initiatives centered around the management of customer knowledge. Within the practices, two results stand out: First, firms are beginning to rely more on data from actual interactions, such as sales and service. They are seeking creative ways to turn data from these interactions into knowledge. Second, even the most ambitious firms are keeping data from different approaches separate. They are not accepting the notion of an integrated data repository. The authors go on to present the practices of the leaders in customer knowledge management.

Data remains one of our most abundant yet underutilized resources. This article provides a holistic framework that will help companies maximize this resource. Outlines the elements necessary to transform data into knowledge and then into business results. Managers must understand that human elements--strategy, skills, culture--need to be attended to along with technology. Examines the experiences of over 20 companies that were successful in their data-to-knowledge efforts. Identifies the critical success factors that must be present in any data-to-knowledge initiative and offers advice for companies seeking to build a robust analytic capability.