Founded in 1873, the Cincinnati Zoo & Botanical Garden is one of the world’s top-rated zoological institutions, and the second oldest zoo in the United States. It is also one of the nation’s most popular attractions, a Top 10 Zagat-rated Zoo, and a Parents Magazine Top Zoo for Children. The Zoo’s 71-acre site is home to more than 500 animal and 3,000 plant species. To increase revenue and improve performance, the Zoo’s senior management team embarked on a comprehensive review of its operations. The review found that management had limited knowledge and understanding of what was actually happening in the Zoo on a day to-day basis, other than how many people visited every day and the Zoo’s total revenue. Who is coming to the Zoo? How often do they come? What do they do and what do they buy? Management had no idea. Each of the Zoo’s four income streams—admissions, membership, retail, and food service—had different point-of-sale platforms, and the food service business, which brings in $4 million a year, still relied on manual cash registers. Management had to sift through paper receipts just to understand daily sales totals. The Zoo needed to change its information systems to focus more on analytics and data management. On rainy days, attendance falls off sharply, often leaving the Zoo overstaffed and overstocked. If the weather is unusually hot, sales of certain items such as ice cream and bottled water are likely to rise, and the Zoo may run out of these items. The Zoo now feeds weather forecast data from the U.S. National Oceanic and Atmospheric Administration (NOAA) Web site into its business intelligence system. By comparing current forecasts to historic attendance and sales data during similar weather conditions, the Zoo is able to make more accurate decisions about labor scheduling and inventory planning. As visitors scan their membership cards at the Zoo’s entrance, exit, attractions, restaurants, and stores, or use the Zoo’s Loyalty Rewards card, the Zoo’s system captures these data and analyzes them to determine usage and spending patterns down to the individual customer level. This information helps the Zoo segment visitors based on their spending and visitation behaviors and use this information to target marketing and promotions specifically for each customer segment. The Zoo used its customer information to devise a direct mail marketing campaign in which this type of visitor would be offered a discount for some of the Zoo’s restaurants and gift shops. Loyal customers are also rewarded with targeted marketing and recognition programs. Instead of sending a special offer to its entire mailing list, the Zoo is able to tailor campaigns more precisely to smaller groups of people, increasing its chances of identifying the people who were most likely to respond to its mailings. More targeted marketing helped the Zoo cut $40,000 from its annual marketing budget. With IBM analytics, management can now instantly identify which beer is selling best, on what day, and at what time to make sure inventory meets demand. Previously, it took 7 to 14 days to get this information, which required hiring part-time staff to sift through register tapes. The Zoo’s ability to make better decisions about operations has led to dramatic improvements in sales. Six months after deploying its business intelligence solution, the Zoo achieved a 30.7 percent increase in food sales and a 5.9 percent increase in retail sales compared to the same period a year earlier. Other zoos across the country have taken note of the Cincinnati Zoo’s success, including the Point Defiance Zoo in Tacoma, Washington. Point Defiance’s online ticket sales increased by 700 percent in 2013, but management had no idea how or why the increase had occurred. Questions: What management, organization, and technology factors were behind the Cincinnati Zoo losing opportunities to increase revenue? How did the Cincinnati Zoo benefit from business intelligence? How did it enhance operational performance and decision making? What role was played by predictive analytics?