Leading Companies Strategize with Analytics

Harrah's Entertainment has established itself as the world's largest gaming company, ranking No. 1 in profits as a percentage of revenues. Harrah's did not achieve this status by following the path of many of its competitors—pouring major revenues into bigger, glitzier buildings. Instead, the company chose a different—some would say more modern—path to profitability by stepping into the Age of Analytics.

A new subcategory of the Information Age, the Age of Analytics represents a fresh, innovative line of attack to strategic activities. Organizations in virtually every industry, from retail to healthcare, are finding that their former competitive strategies no longer make the grade. Research has proven that companies that have embraced analytics as a new, creative strategy are consistently forging ahead of their competition.

With the objective of increasing customer loyalty, Harrah's embarked on a strategy that focused heavily on customer relationship management (CRM) technologies and innovative marketing techniques. Using information gathered through its loyalty program, Harrah's builds predictive models that allow the company to better understand its customers. This data is used to determine which customers to target for loyalty incentive programs in addition to predicting their worth into the future. According to Harrah's, its efforts have resulted in an increase in the company's share of customers' gaming budgets from 36% in 1998 to 45% today.

The importance of analytics
Analytics is a quantitative fact- and data-based approach to decision making with an emphasis on prediction and optimization. Tom Davenport, a distinguished professor of Information Technology Management at Babson College in Massachusetts and a noted expert in the field of analytics, has written "Competing on Analytics: The New Science of Winning" (Harvard Business School Press).

"Historically, when organizations have used business intelligence [BI], they have focused more on reporting, which is a backward-looking activity. Analytics lead you to look forward and make predictions," says Davenport. "Competing on analytics is building your strategy and the way you go to market around your analytical capabilities. It's not just using analytics, which has been going on for quite a long time, but instead giving them much more priority as a competitive resource than they have been given in the past."

Through the power of analytics, organizations can optimize their business processes to make them as effective as possible. Companies that take an analytical approach can differentiate themselves from competitors, at least for a while, but analytics also allows organizations with commodity products and services to differentiate based on customer interaction and marketing strategies. Dominant companies in every industry have seen their profits slide as "copycat" products, typically combined with lower price tags, flood the market. As a result, product specifications alone are no longer a sufficient differentiator. Now businesses need to understand customer needs and develop stronger relationships through tailored marketing campaigns.

Barclays banks on data

A key factor in becoming an analytical competitor is the ability to find new, unusual or distinctive types of data and then developing a strategy to effectively manage that data. The United Kingdom-based Barclays PLC, one of the world's largest global financial services providers, identifies significant behavioral changes through its Teradata Customer Management solution, which indicates alterations in customers' financial interests. Armed with this data, Barclays provides individual customers with relevant, timely information on how the bank's products and services can help satisfy that customer's changing needs across multiple channels, including the Web, ATMs, telephones, letters, e-mail and face-to-face contact.

Applebee's serves up accurate predictions

The business strategy at Applebee's International, the world's largest casual-dining chain, operating restaurants in 49 U.S. states and 16 countries, is to offer good food at great prices in a friendly, neighborhood atmosphere. Applebee's does so by understanding customer preferences and, at the same time, reducing costs by accurately predicting product demand. Applebee's pulls data from a variety of sources, including point-of-sale systems at each restaurant, customer satisfaction ratings, and food cost and labor management systems. Analysts use this data to determine customer food preferences and even how customers want their food presented when it is served. The company also uses this information to customize menus and plan staffing at the regional level. This strategy has improved the company's ability to accurately forecast product needs and reduce overall costs.

Cultural shift
Successful analytical competitors build a culture around the scientific method of analytics that instills company-wide respect for experimenting and testing quantitative evidence before implementing process or product changes. One large U.S. bank performs about 60,000 experiments a year, and another does not move forward in any area without testing first. "If you have an analytical culture, the rest is much easier to fall into place," Davenport says. "It's much more difficult to change the human being than it is to gather the data and build the algorithms and so on."

According to Davenport, the key to building an analytical culture lies in top management support and leadership. If an organization has support, it can take the "full steam ahead" path to hire the right people, create the data environment for the analytical work, build the applications, etc. "Companies that have embarked upon this path have shown value quite quickly, in less than a year in most cases," he adds.

If the senior management team is not committed, an organization can still create an analytical culture, although it involves a detour, which Davenport refers to as the "prove it" path. This approach involves using smaller demonstration projects and pointing out successful industry competitors to convince the management team. "Sometimes pointing out competitors can shorten that path," Davenport states. "Almost every industry has somebody who's emerging as an analytical competitor, and the good news is that they tend to be the high performers in the industry. Harrah's, for example, has become the world's largest gaming firm. My research shows pretty strong correlations between financial performance and how analytical a company is."

Technological advantage
To compete on analytics, organizations also need to compete on technology, and that means constantly monitoring cutting-edge IT developments. The key prerequisite is a data-oriented strategy that allows a business to rapidly pull information from every plausible source. These technologies could include enterprise resource planning, CRM, point-of-sale and other transaction technologies. Competing on that information, then, necessitates using an enterprise data warehouse that can store, integrate and present the data in formats that can be easily accessed and analyzed.

In addition to data-related technologies, organizations need BI software with a strong orientation to analytics and statistical capabilities as well as to reporting. As the popularity of analytics competition continues to grow, more systems that integrate these tools are available. The recently announced strategic partnership between Teradata and SAS, a leader in analytical software and services, combines the strengths of both companies and is a prime example of how technology is emerging in the analytics arena.

The next move
Companies that embrace analytical strategies continually face challenges from cultural, technological and strategic standpoints. These challenges must be dealt with head-on. Cultural and behavioral issues relate to the ongoing need to build an analytical orientation into the organization's culture, particularly pushing forward the "test and learn" approach. Davenport also points out the importance of building analytical groups that increasingly focus not only on analytical professionals but analytical amateurs as well. "The people working the call center, for example, may not have to generate algorithms for deciding the next best product to offer to a customer, but they do need some idea of why that product is being recommended so they can explain it to the customer," he says. "That's the challenge." From a technological standpoint, analytical competitors are going to see many new data types emerging that need to be managed and integrated. The life sciences sector, for instance, will be faced with novel types of genomic data; organizations with supply chains will need to deal with radio frequency identification (RFID) data; in customer management, companies will see new data from customer touchpoints.

The Warehouse finds value in supply chain analytics

Analytics competitors make expert use of data and modeling to improve a variety of functions, including customer relationships, financial performance and, as in the case of The Warehouse, supply chain management.

The Warehouse is the largest general merchandise discount retailer (non-food) in New Zealand. The company's rapid growth from a small retailer to an 85-store chain presented many challenges in supply chain management that were complicated by the large number of products (50,000-70,000 in any one store) and sourcing from several different suppliers, 50% of which are located outside New Zealand.

The company's processes initially involved manually determining replenishment levels that in many cases were set only once for the life of a product. As a result, The Warehouse was unresponsive to changes in demand, and in some cases overstock or out-of-stock situations would result.

To improve availability and more efficiently manage stock, The Warehouse implemented the Teradata Demand Chain Management (DCM) solution.

"Teradata DCM has provided us with a lot of value. We've seen significant availability improvements where we're using it from beginning to end," says Ray Renner, Stock Systems and Support manager, noting that the order/reordered product typically has 10% better availability than products manually ordered into the business. The Warehouse also uses tools such as those from MicroStrategy to provide valuable information that the company's buyers and analysts use to better manage the business.

The Warehouse also expects to use Teradata Demand Chain Management to generate replenishment orders through the calculation of suggested order quantities. "That will give us a lot of options with our suppliers in terms of providing them with order forecast information," Renner says. "We're definitely looking for collaborative information sharing with our suppliers and our distribution centers as well. One of the key benefits that we see of the time-phased forecasting nature of DCM is the ability to give our distribution centers volume forecasts to enable them to plan their staffing much more efficiently." Additionally, Renner indicates that providing suppliers with an accurate picture of what The Warehouse expects to order puts the retailer in a position to potentially bargain over the pricing structure.

Renner acknowledges that the company's focus has been primarily availability rather than forecast accuracy, which is something that will be considered as The Warehouse becomes more proactive with analytics. "There is just a plethora of opportunity for us," he says.

—J.Z.

Strategically speaking, it is critical for an analytical competitor to constantly review where analytics fits within the business model and take the necessary steps to refine the analytics as the business model and strategy change. In one case, a multi-national company has been successful in the U.S. and has established great relationships with customers, but the current growth market for the company's industry is in Asia. How can the organization use its analytical capabilities to establish a business in Macau? Do Asian customers want the same sorts of features and products that U.S. customers want? Using analytics enables the company to further its predictive capabilities and forge ahead in this market.

Increasing competition among analytical companies is also an issue. Many of the early analytical competitors have found that some companies that they were previously leading are catching up. Those early adopters are realizing the ongoing need to find new data sources and analytical approaches. "You've always got to be thinking about, 'Do I have the right strategy? Do I have the right analytical capabilities? Do I have the right analytical capabilities for my strategy?'" explains Davenport.

Another looming challenge is the issue of data privacy. European governments are currently more focused on data privacy in terms of regulation and policy than the U.S., but Davenport points out that more restrictions are likely in the future and the smart organization will be prepared for that.

Checkmate
New technologies and data sources that were once just brainstorming ideas are now becoming a reality, much to the advantage of analytical competitors. For example, information content in data warehouses has long been dominated by structured data. But significant progress is being made toward incorporating equal amounts of unstructured data, which includes natural-language data sources such as word processing files, e-mails, text fields from databases and applications, and even voice recognition.

The worth of such developments can be enormous for analytical competitors. Travelocity has launched a new project to mine loads of unstructured data contained in sources such as e-mails, customer satisfaction surveys and call center representative notes to better respond to customer service issues. The online travel agency is installing new text analytics software that will automatically identify facts, opinions, requests, trends and trouble spots from the unstructured data and then link that analysis with structured data from its data warehouse to help identify trends.

Innovative tools that help manage customer communication are also emerging. Checkout clerks can now scan cell phones holding bar-coded coupons; gas pumps can invite customers inside the store for a beverage; ATMs can display offers that are immediately customized to the customer; airlines or tour operators can create instant marketing campaigns to fill empty seats up to departure time. All of these technologies are dramatically shaping the way organizations can and will do business.

In today's increasingly aggressive business environment, analytics represents a valuable tool in the quest to outsmart the competition. With a consistent, enterprise-wide approach to data management and a flexible BI structure, any organization can become a successful analytics front-runner. T

Jackie Zack is a freelance business, marketing and technology writer based in Brighton, Mich.

The 5 components of the DELTA model

What does a business or organization need to do to become an analytical competitor? According to Tom Davenport, a distinguished professor of Information Technology Management at Babson College in Massachusetts, analytical competitors need to focus on the DELTA model, an acronym for five crucial analytical factors.

Data. Successful analytics requires large volumes of high-quality data that is integrated and accessible, typically in a data warehouse. "One of the reasons why a lot of Teradata's customers have been focused on analytics is because they can use the data in their Teradata systems to analyze," says Davenport.

Enterprise. An enterprise approach is crucial when dealing with analytics. In many organizations, analytics is fragmented and siloed within different parts of the business. Groups within the organization have little or no communication, don't share data and have different technological solutions for generating analytics. "The companies that I identified as analytical competitors are taking an enterprise approach with respect to all the key resources," explains Davenport. "They are combining people into more central organizations, or at least networked organizations, so they are talking to each other and sharing ideas about how to be successful with analytics. They need to have an integrated approach to data in an enterprise-level warehouse. In order to do that, they are typically standardizing on a few key technologies."

Leadership. A critical element for developing an analytical approach is leadership. "This is something that historically is not discussed much with regard to business intelligence [BI] and analytics, but I would argue it's the single most important factor," says Davenport. Senior managers and leaders need to support and have a passion about the subject of analytics in order to move an organization's culture in a more analytical and fact-based direction.

Targets. Successful organizations do not, at least initially, take an analytical approach to every aspect of the business but instead select a particular set of relationships to focus on, such as suppliers, employees or customers. An automobile insurance company might target pricing risk; professional sports teams frequently focus on employees; for Harrah's, the target is customer loyalty; and for a large North American bank it is understanding and optimizing customer relationships. "Over time, you do find that companies tend to take on more and more analytical applications, but at least initially they tend to have a target that is highly related to their predictive capabilities as a business," says Davenport.

Analysts/Action. Davenport says that an organization's analysts must be capable not only in terms of their analytical and quantitative sophistication but also in their ability to communicate results to the decision makers, persuading them to act on the basis of the analytics.

Action is also a component of successful analytics. A company that determines that a particular promotion is no longer effective but continues that promotion anyway, or an organization that identifies its best, most profitable customers but doesn't treat them differently, is not taking action, Davenport explains. "It's a very critical point because in some cases it's easier to do the analytical work than it is to take action on the results, but in reality, analytics without action are not terribly useful."

—J.Z.