Chapter 11 The Data Asset: Databases, Business Intelligence, and Competitive Advantage

Understanding How Data Is Organized: Key Terms and Technologies

A databaseA single table or a collection of related tables. is simply a list (or more likely, several related lists) of data. Most organizations have several databases—perhaps even hundreds or thousands. And these various databases might be focused on any combination of functional areas (sales, product returns, inventory, payroll), geographical regions, or business units. Firms often create specialized databases for recording transactions, as well as databases that aggregate data from multiple sources in order to support reporting and analysis.

Databases are created, maintained, and manipulated using programs called database management systems (DBMS)Sometimes called “databade software”; software for creating, maintaining, and manipulating data., sometimes referred to as database software. DBMS products vary widely in scale and capabilities. They include the single-user, desktop versions of Microsoft Access or Filemaker Pro, Web-based offerings like Intuit QuickBase, and industrial strength products from Oracle, IBM (DB2), Sybase, Microsoft (SQL Server), and others. Oracle is the world’s largest database software vendor, and database software has meant big bucks for Oracle cofounder and CEO Larry Ellison. Ellison perennially ranks in the Top 10 of the Forbes 400 list of wealthiest Americans.

The acronym SQL (often pronounced sequel) also shows up a lot when talking about databases. Structured query language (SQL)A language used to create and manipulate databases. is by far the most common language for creating and manipulating databases. You’ll find variants of SQL inhabiting everything from lowly desktop software, to high-powered enterprise products. Microsoft’s high-end database is even called SQL Server. And of course there’s also the open source MySQL (whose stewardship now sits with Oracle as part of the firm’s purchase of Sun Microsystems). Given this popularity, if you’re going to learn one language for database use, SQL’s a pretty good choice. And for a little inspiration, visit Monster.com or another job site and search for jobs mentioning SQL. You’ll find page after page of listings, suggesting that while database systems have been good for Ellison, learning more about them might be pretty good for you, too.

Even if you don’t become a database programmer or database administrator (DBA)Job title focused on directing, performing, or overseeing activities associated with a database or set of databases. These may include (but not necessarily be limited to): database design, creation, implementation, maintenance, backup and recovery, policy setting and enforcement, and security., you’re almost surely going to be called upon to dive in and use a database. You may even be asked to help identify your firm’s data requirements. It’s quite common for nontech employees to work on development teams with technical staff, defining business problems, outlining processes, setting requirements, and determining the kinds of data the firm will need to leverage. Database systems are powerful stuff, and can’t be avoided, so a bit of understanding will serve you well.

A complete discourse on technical concepts associated with database systems is beyond the scope of our managerial introduction, but here are some key concepts to help get you oriented, and that all managers should know.

• A table or fileA list of data, arranged in columns (fields) and rows (records). refers to a list of data.
• A database is either a single table or a collection of related tables. The course registration database above depicts five tables.
• A column or fieldA column in a database table. Columns represent each category of data contained in a record (e.g., first name, last name, ID number, date of birth). defines the data that a table can hold. The “Students” table above shows columns for STUDENT_ID, FIRST_NAME, LAST_NAME, CAMPU.S._ADDR (the “…” symbols above are meant to indicate that in practice there may be more columns or rows than are shown in this simplified diagram).
• A row or recordA row in a database table. Records represent a single instance of whatever the table keeps track of (e.g., student, faculty, course title). represents a single instance of whatever the table keeps track of. In the example above, each row of the “Students” table represents a student, each row of the “Enrollment” table represents the enrollment of a student in a particular course, and each row of the “Course List” represents a given section of each course offered by the University.
• A keyCode that unlocks encryption. is the field used to relate tables in a database. Look at how the STUDENT_ID key is used above. There is one unique STUDENT_ID for each student, but the STUDENT_ID may appear many times in the “Enrollment” table, indicating that each student may be enrolled in many classes. The “1” and “M” in the diagram above indicate the one to many relationships among the keys in these tables.

Databases organized like the one above, where multiple tables are related based on common keys, are referred to as relational databasesThe most common standard for expressing databases, whereby tables (files) are related based on common keys.. There are many other database formats (sporting names like hierarchical, and object-oriented), but relational databases are far and away the most popular. And all SQL databases are relational databases.

Even though SQL and the relational model are hugely popular and dominate many corporate environments, other systems exist. An increasingly popular set of technologies known as NoSQL avoid SQL and the rigid structure of relational databases. NoSQL technologies are especially popular with Internet firms that rely on massive, unwieldy, and disparately structured data.

We’ve just scratched the surface for a very basic introduction. Expect that a formal class in database systems will offer you far more detail and better design principles than are conveyed in the elementary example above. But you’re already well on your way!

Transaction Processing Systems

For most organizations that sell directly to their customers, transaction processing systems (TPS)Systems that record a transaction (some form of business-related exchange), such as a cash register sale, ATM withdrawal, or product return. represent a fountain of potentially insightful data. Every time a consumer uses a point-of-sale system, an ATM, or a service desk, there’s a transactionSome kind of business exchange. (some kind of business exchange) occurring, representing an event that’s likely worth tracking.

The cash register is the data generation workhorse of most physical retailers, and the primary source that feeds data to the TPS. But while TPS can generate a lot of bits, it’s sometimes tough to match this data with a specific customer. For example, if you pay a retailer in cash, you’re likely to remain a mystery to your merchant because your name isn’t attached to your money. Grocers and retailers can tie you to cash transactions if they can convince you to use a loyalty cardSystems that provide rewards and usage incentives, typically in exchange for a method that provides a more detailed tracking and recording of customer activity. In addition to enhancing data collection, loyalty cards can represent a significant switching cost.. Use one of these cards and you’re in effect giving up information about yourself in exchange for some kind of financial incentive. The explosion in retailer cards is directly related to each firm’s desire to learn more about you and to turn you into a more loyal and satisfied customer.

Some cards provide an instant discount (e.g., the CVS Pharmacy ExtraCare card), while others allow you to build up points over time (Best Buy’s Reward Zone). The latter has the additional benefit of acting as a switching cost. A customer may think “I could get the same thing at Target, but at Best Buy, it’ll increase my existing points balance and soon I’ll get a cash back coupon.”

Enterprise Software (CRM, SCM, and ERP)

Firms increasingly set up systems to gather additional data beyond conventional purchase transactions or Web site monitoring. CRM or customer relationship management systems are often used to empower employees to track and record data at nearly every point of customer contact. Someone calls for a quote? Brings a return back to a store? Writes a complaint e-mail? A well-designed CRM system can capture all these events for subsequent analysis or for triggering follow-up events.

Enterprise software includes not just CRM systems but also categories that touch every aspect of the value chain, including supply chain management (SCM) and enterprise resource planning (ERP) systems. More importantly, enterprise software tends to be more integrated and standardized than the prior era of proprietary systems that many firms developed themselves. This integration helps in combining data across business units and functions, and in getting that data into a form where it can be turned into information (for more on enterprise systems, see Chapter 9 "Understanding Software: A Primer for Managers").

Surveys

Sometimes firms supplement operational data with additional input from surveys and focus groups. Oftentimes, direct surveys can tell you what your cash register can’t. Zara store managers informally survey customers in order to help shape designs and product mix. Online grocer FreshDirect (see Chapter 2 "Strategy and Technology: Concepts and Frameworks for Understanding What Separates Winners from Losers") surveys customers weekly and has used this feedback to drive initiatives from reducing packaging size to including star ratings on produce.R. Braddock, “Lessons of Internet Marketing from FreshDirect,” Wall Street Journal, May 11, 2009. Many CRM products also have survey capabilities that allow for additional data gathering at all points of customer contact.

External Sources

Sometimes it makes sense to combine a firm’s data with bits brought in from the outside. Many firms, for example, don’t sell directly to consumers (this includes most drug companies and packaged goods firms). If your firm has partners that sell products for you, then you’ll likely rely heavily on data collected by others.

Data bought from sources available to all might not yield competitive advantage on its own, but it can provide key operational insight for increased efficiency and cost savings. And when combined with a firm’s unique data assets, it may give firms a high-impact edge.

Consider restaurant chain Brinker, a firm that runs seventeen hundred eateries in twenty-seven countries under the Chili’s, On The Border, and Maggiano’s brands. Brinker (whose ticker symbol is EAT), supplements their own data with external feeds on weather, employment statistics, gas prices, and other factors, and uses this in predictive models that help the firm in everything from determining staffing levels to switching around menu items.R. King, “Intelligence Software for Business,” BusinessWeek podcast, February 27, 2009.

In another example, Carnival Cruise Lines combines its own customer data with third-party information tracking household income and other key measures. This data plays a key role in a recession, since it helps the firm target limited marketing dollars on those past customers that are more likely to be able to afford to go on a cruise. So far it’s been a winning approach. For three years in a row, the firm has experienced double-digit increases in bookings by repeat customers.R. King, “Intelligence Software for Business,” BusinessWeek podcast, February 27, 2009.

Incompatible Systems

Just because data is collected doesn’t mean it can be used. This limit is a big problem for large firms that have legacy systemsOlder information systems that are often incompatible with other systems, technologies, and ways of conducting business. Incompatible legacy systems can be a major roadblock to turning data into information, and they can inhibit firm agility, holding back operational and strategic initiatives., outdated information systems that were not designed to share data, aren’t compatible with newer technologies, and aren’t aligned with the firm’s current business needs. The problem can be made worse by mergers and acquisitions, especially if a firm depends on operational systems that are incompatible with its partner. And the elimination of incompatible systems isn’t just a technical issue. Firms might be under extended agreement with different vendors or outsourcers, and breaking a contract or invoking an escape clause may be costly. Folks working in M&A (the area of investment banking focused on valuing and facilitating mergers and acquisitions) beware—it’s critical to uncover these hidden costs of technology integration before deciding if a deal makes financial sense.

Operational Data Can’t Always Be Queried

Another problem when turning data into information is that most transactional databases aren’t set up to be simultaneously accessed for reporting and analysis. When a customer buys something from a cash register, that action may post a sales record and deduct an item from the firm’s inventory. In most TPS systems, requests made to the database can usually be performed pretty quickly—the system adds or modifies the few records involved and it’s done—in and out in a flash.

But if a manager asks a database to analyze historic sales trends showing the most and least profitable products over time, they may be asking a computer to look at thousands of transaction records, comparing results, and neatly ordering findings. That’s not a quick in-and-out task, and it may very well require significant processing to come up with the request. Do this against the very databases you’re using to record your transactions, and you might grind your computers to a halt.

Getting data into systems that can support analytics is where data warehouses and data marts come in, the topic of our next section.

Hadoop: Big Insights from Unstructured “Big Data”

Having neatly structured data warehouses and data-marts are great—the tools are reliable and can often be turned over to end-users or specialists who can rapidly produce reports and other analyses. But roughly 80 percent of corporate data is messy and unstructured, and it is not stored in conventional, relational formats—think of data stored in office productivity documents, e-mail, and social media.R. King, “Getting a Handle on Big Data with Hadoop,” BusinessWeek, September 7, 2011. Conventional tools often choke when trying to sift through the massive amounts of data collected by many of today’s firms. The open-source project known as Hadoop was created to analyze massive amounts of raw information better than traditional, highly-structured databases.

Hadoop is made up of some half-dozen separate software pieces and requires the integration of these pieces to work. Hadoop-related projects have names such as Hive, Pig, and Zookeeper. Their use is catching on like wildfire, with some expecting that within five years, more than half of the world’s data will be stored in Hadoop environments.R. King, “Getting a Handle on Big Data with Hadoop,” BusinessWeek, September 7, 2011. Expertise is in short supply, with Hadoop-savvy technologists having lots of career opportunities.

There are four primary advantages to Hadoop:IBM Big Data, “What is Hadoop?” YouTube video, 3:12 May 22, 2012, http://www.youtube.com/watch?v=RQr0qd8gxW8.

• Flexibility: Hadoop can absorb any type of data, structured or not, from any type of source (geeks would say such a system is schema-less). But this disparate data can still be aggregated and analyzed.
• Scalability: Hadoop systems can start on a single PC, but thousands of machines can eventually be combined to work together for storage and analysis.
• Cost effectiveness: Since the system is open source and can be started with low-end hardware, the technology is cheap by data-warehousing standards. Many vendors also offer Hadoop as a cloud service, allowing firms to avoid hardware costs altogether.
• Fault tolerance: One of the servers running your Hadoop cluster just crashed? No big deal. Hadoop is designed in such a way so that there will be no single point of failure. The system will continue to work, relying on the remaining hardware.

Financial giant Morgan Stanley is a big believer in Hadoop. One senior technology manager at the firm contrasts Hadoop with highly structured systems, saying that in the past, “IT asked the business what they want, creates a data structure and writes structured query language, sources the data, conforms it to the table and writes a structured query. Then you give it to them and they often say that is not what they wanted.” But with Hadoop overseeing a big pile of unstructured (or less structured) data, technical staff can now work with users to carve up and combine data in lots of different ways, or even set systems loose in the data to hunt for unexpected patterns (see the discussion of data mining later in this chapter). Morgan Stanley’s initial Hadoop experiments started with a handful of old servers that were about to be retired, but the company has steadily ramped up its efforts. Now by using Hadoop, the firm sees that it is able to analyze data on a far larger scale (“petabytes of data, which is unheard of in the traditional database world”) with potentially higher-impact results. The bank is looking at customers’ financial objectives and trying to come up with investment insights to help them invest appropriately, and it is seeking “Big Data” insights to help the firm more effectively manage risk.T. Groenfeldt, “Morgan Stanley takes on Big Data with Hadoop,” Forbes, May 30, 2012.

Other big name-firms using Hadoop for “Big Data” insights include Bank of America, Disney, GE, LinkedIn, Nokia, Twitter, and Wal-Mart. Hadoop is an open source project overseen by the Apache Software Foundation. It has an Internet pedigree and is based on ideas by Google and lots of software contributed by Yahoo! (two firms that regularly need to dive into massive and growing amounts of unstructured data—web pages, videos, images, social media, user account information, and more). IBM used Hadoop as the engine that helped power Watson to defeat human opponents on Jeopardy, further demonstrating the technology’s ability to analyze wildly different data for accurate insight. Other tech firms embracing Hadoop and offering some degree of support for the technology include HP, EMC, and Microsoft.

Query and Reporting Tools

The idea behind query and reporting tools is to present users with a subset of requested data, selected, sorted, ordered, calculated, and compared, as needed. Managers use these tools to see and explore what’s happening inside their organizations.

Canned reportsReports that provide regular summaries of information in a predetermined format. provide regular summaries of information in a predetermined format. They’re often developed by information systems staff and formats can be difficult to alter. By contrast, ad hoc reporting toolsTools that put users in control so that they can create custom reports on an as-needed basis by selecting fields, ranges, summary conditions, and other parameters. allow users to dive in and create their own reports, selecting fields, ranges, and other parameters to build their own reports on the fly. DashboardsA heads-up display of critical indicators that allow managers to get a graphical glance at key performance metrics. provide a sort of heads-up display of critical indicators, letting managers get a graphical glance at key performance metrics. Some tools may allow data to be exported into spreadsheets. Yes, even the lowly spreadsheet can be a powerful tool for modeling “what if” scenarios and creating additional reports (of course be careful: if data can be easily exported, then it can potentially leave the firm dangerously exposed, raising privacy, security, legal, and competitive concerns).

A subcategory of reporting tools is referred to as online analytical processing (OLAP)A method of querying and reporting that takes data from standard relational databases, calculates and summarizes the data, and then stores the data in a special database called a data cube. (pronounced “oh-lap”). Data used in OLAP reporting is usually sourced from standard relational databases, but it’s calculated and summarized in advance, across multiple dimensions, with the data stored in a special database called a data cubeA special database used to store data in OLAP reporting.. This extra setup step makes OLAP fast (sometimes one thousand times faster than performing comparable queries against conventional relational databases). Given this kind of speed boost, it’s not surprising that data cubes for OLAP access are often part of a firm’s data mart and data warehouse efforts.

A manager using an OLAP tool can quickly explore and compare data across multiple factors such as time, geography, product lines, and so on. In fact, OLAP users often talk about how they can “slice and dice” their data, “drilling down” inside the data to uncover new insights. And while conventional reports are usually presented as a summarized list of information, OLAP results look more like a spreadsheet, with the various dimensions of analysis in rows and columns, with summary values at the intersection.

Data Mining

While reporting tools can help users explore data, modern data sets can be so large that it might be impossible for humans to spot underlying trends. That’s where data mining can help. Data miningThe process of using computers to identify hidden patterns in, and to build models from, large data sets. is the process of using computers to identify hidden patterns and to build models from large data sets.

Some of the key areas where businesses are leveraging data mining include the following:

• Customer segmentation—figuring out which customers are likely to be the most valuable to a firm.
• Marketing and promotion targeting—identifying which customers will respond to which offers at which price at what time.
• Market basket analysis—determining which products customers buy together, and how an organization can use this information to cross-sell more products or services.
• Collaborative filtering—personalizing an individual customer’s experience based on the trends and preferences identified across similar customers.
• Customer churn—determining which customers are likely to leave, and what tactics can help the firm avoid unwanted defections.
• Fraud detection—uncovering patterns consistent with criminal activity.
• Financial modeling—building trading systems to capitalize on historical trends.
• Hiring and promotion—identifying characteristics consistent with employee success in the firm’s various roles.

For data mining to work, two critical conditions need to be present: (1) the organization must have clean, consistent data, and (2) the events in that data should reflect current and future trends. The recent financial crisis provides lessons on what can happen when either of these conditions isn’t met.

First lets look at problems with using bad data. A report in the New York Times has suggested that in the period leading up to the 2008 financial crisis, some banking executives deliberately deceived risk management systems in order to skew capital-on-hand requirements. This deception let firms load up on risky debt, while carrying less cash for covering losses.S. Hansell, “How Wall Street Lied to Its Computers,” New York Times, September 18, 2008. Deceive your systems with bad data and your models are worthless. In this case, wrong estimates from bad data left firms grossly overexposed to risk. When debt defaults occurred; several banks failed, and we entered the worst financial crisis since the Great Depression.

Now consider the problem of historical consistency: Computer-driven investment models can be very effective when the market behaves as it has in the past. But models are blind when faced with the equivalent of the “hundred-year flood” (sometimes called black swans); events so extreme and unusual that they never showed up in the data used to build the model.

We saw this in the late 1990s with the collapse of the investment firm Long Term Capital Management. LTCM was started by Nobel Prize–winning economists, but when an unexpected Russian debt crisis caused the markets to move in ways not anticipated by its models, the firm lost 90 percent of its value in less than two months. The problem was so bad that the Fed had to step in to supervise the firm’s multibillion-dollar bailout. Fast forward a decade to the banking collapse of 2008, and we again see computer-driven trading funds plummet in the face of another unexpected event—the burst of the housing bubble.P. Wahba, “Buffeted ‘Quants’ Are Still in Demand,” Reuters, December 22, 2008.

Data mining presents a host of other perils, as well. It’s possible to over-engineerBuild a model with so many variables that the solution arrived at might only work on the subset of data you’ve used to create it. a model, building it with so many variables that the solution arrived at might only work on the subset of data you’ve used to create it. You might also be looking at a random but meaningless statistical fluke. In demonstrating how flukes occur, one quantitative investment manager uncovered a correlation that at first glance appeared statistically to be a particularly strong predictor for historical prices in the S&P 500 stock index. That predictor? Butter production in Bangladesh.P. Coy, “He Who Mines Data May Strike Fool’s Gold,” BusinessWeek, June 16, 1997. Sometimes durable and useful patterns just aren’t in your data.

One way to test to see if you’re looking at a random occurrence in the numbers is to divide your data, building your model with one portion of the data, and using another portion to verify your results. This is the approach Netflix has used to test results achieved by teams in the Netflix Prize, the firm’s million-dollar contest for improving the predictive accuracy of its movie recommendation engine (see Chapter 4 "Netflix in Two Acts: The Making of an E-commerce Giant and the Uncertain Future of Atoms to Bits").

Finally, sometimes a pattern is uncovered but determining the best choice for a response is less clear. As an example, let’s return to the data-mining wizards at Tesco. An analysis of product sales data showed several money-losing products, including a type of bread known as “milk loaf.” Drop those products, right? Not so fast. Further analysis showed milk loaf was a “destination product” for a loyal group of high-value customers, and that these customers would shop elsewhere if milk loaf disappeared from Tesco shelves. The firm kept the bread as a loss-leader and retained those valuable milk loaf fans.B. Helm, “Getting Inside the Customer’s Mind,” BusinessWeek, September 11, 2008. Data miner, beware—first findings don’t always reveal an optimal course of action.

This last example underscores the importance of recruiting a data mining and business analytics team that possesses three critical skills: information technology (for understanding how to pull together data, and for selecting analysis tools), statistics (for building models and interpreting the strength and validity of results), and business knowledge (for helping set system goals, requirements, and offering deeper insight into what the data really says about the firm’s operating environment). Miss one of these key functions and your team could make some major mistakes.

While we’ve focused on tools in our discussion above, many experts suggest that business intelligence is really an organizational process as much as it is a set of technologies. Having the right team is critical in moving the firm from goal setting through execution and results.

A Data-Driven Value Chain

The Wal-Mart efficiency dance starts with a proprietary system called Retail Link, a system originally developed in 1991 and continually refined ever since. Each time an item is scanned by a Wal-Mart cash register, Retail Link not only records the sale, it also automatically triggers inventory reordering, scheduling, and delivery. This process keeps shelves stocked, while keeping inventories at a minimum. An AMR report ranked Wal-Mart as having the seventh best supply chain in the country (the only other retailer in the top twenty was Tesco, at number fifteen).T. Friscia, K. O’Marah, D. Hofman, and J. Souza, “The AMR Research Supply Chain Top 25 for 2009,” AMR Research, May 28, 2009, http://www.amrresearch.com/Content/View.aspx?compURI=tcm:7-43469. The firm’s annual inventory turnover ratioThe ratio of a company’s annual sales to its inventory. of 8.5 means that Wal-Mart sells the equivalent of its entire inventory roughly every six weeks (by comparison, Target’s turnover ratio is 6.4, Sears’ is 3.4, and the average for U.S. retail is less than 2).Twelve-month figures from midyear 2009, via Forbes and Reuters.

Back-office scanners keep track of inventory as supplier shipments come in. Suppliers are rated based on timeliness of deliveries, and you’ve got to be quick to work with Wal-Mart. In order to avoid a tractor-trailer traffic jam in store parking lots, deliveries are choreographed to arrive at intervals less than ten minutes apart. When Levi’s joined Wal-Mart, the firm had to guarantee it could replenish shelves every two days—no prior retailer had required a shorter than five day window from Levi’s.C. Fishman, “The Wal-Mart You Don’t Know,” Fast Company, December 19, 2007.

Wal-Mart has been a catalyst for technology adoption among its suppliers. The firm is currently leading an adoption effort that requires partners to leverage RFID technology to track and coordinate inventories. While the rollout has been slow, a recent P&G trial showed RFID boosted sales nearly 20 percent by ensuring that inventory was on shelves and located where it should be.D. Joseph, “Supermarket Strategies: What’s New at the Grocer,” BusinessWeek, June 8, 2009.

Data Mining Prowess

Wal-Mart also mines its mother lode of data to get its product mix right under all sorts of varying environmental conditions, protecting the firm from “a retailer’s twin nightmares: too much inventory, or not enough.”C. Hays, “What Wal-Mart Knows about Customer Habits,” New York Times, November 14, 2004. For example, the firm’s data mining efforts informed buyers that customers stock up on certain products in the days leading up to predicted hurricanes. Bumping up prestorm supplies of batteries and bottled water was a no brainer, but the firm also learned that Pop-Tarts sales spike sevenfold before storms hit, and that beer is the top prestorm seller. This insight has lead to truckloads full of six packs and toaster pastries streaming into gulf states whenever word of a big storm surfaces.C. Hays, “What Wal-Mart Knows about Customer Habits,” New York Times, November 14, 2004.

Data mining also helps the firm tighten operational forecasts, helping to predict things like how many cashiers are needed at a given store at various times of day throughout the year. Data drives the organization, with mined reports forming the basis of weekly sales meetings, as well as executive strategy sessions.

Wal-Mart leverages its huge Hadoop-based data trove to support some of its data mining efforts, sifting through massive amounts of social media—Twitter posts, Facebook updates, and other so-called unstructured data—to gain insights on product offerings, sales leads, pricing, and more. The firm purchased social startup Kosmix for $300 million to deepen social and Big Data expertise in the company’s @WalmartLabs.R. King, “Getting a Handle on Big Data with Hadoop,” BusinessWeek, September 7, 2011. Says Kosmix founder Anand Rajaraman (who previously sold a firm to Amazon and was an early investor in Facebook), “The first generation of e-commerce was about bringing the store to the Web. The next generation will be about building integrated experiences that leverage the store, the Web and mobile, with social identity being the glue that binds the experience.”C. Nicholson, “Wal-Mart Buys Social Media Firm Kosmix,” New York Times, April 19, 2011.

Sharing Data, Keeping Secrets

While Wal-Mart is demanding of its suppliers, it also shares data with them, too. Data can help firms become more efficient so that Wal-Mart can keep dropping prices, and data can help firms uncover patterns that help suppliers sell more. P&G’s Gillette unit, for example, claims to have mined Wal-Mart data to develop promotions that increased sales as much as 19 percent. More than seventeen thousand suppliers are given access to their products’ Wal-Mart performance across metrics that include daily sales, shipments, returns, purchase orders, invoices, claims and forecasts. And these suppliers collectively interrogate Wal-Mart data warehouses to the tune of twenty-one million queries a year.K. Evans-Correia, “Dillman Replaced as Wal-Mart CIO,” SearchCIO, April 6, 2006.

While Wal-Mart shares sales data with relevant suppliers, the firm otherwise fiercely guards this asset. Many retailers pool their data by sharing it with information brokers like Information Resources and ACNielsen. This sharing allows smaller firms to pool their data to provide more comprehensive insight on market behavior. But Wal-Mart stopped sharing data with these agencies years ago. The firm’s scale is so big, the additional data provided by brokers wasn’t adding much value, and it no longer made sense to allow competitors access to what was happening in its own huge chunk of retail sales.

Other aspects of the firm’s technology remain under wraps, too. Wal-Mart custom builds large portions of its information systems to keep competitors off its trail. As for infrastructure secrets, the Wal-Mart Data Center in McDonald County, Missouri, was considered so off limits that the county assessor was required to sign a nondisclosure statement before being allowed on-site to estimate property value.M. McCoy, “Wal-Mart’s Data Center Remains Mystery,” Joplin Globe, May 28, 2006.

Challenges Abound

But despite success, challenges continue. While Wal-Mart grew dramatically throughout the 1990s, the firm’s U.S. business has largely matured. And as a mature business it faces a problem not unlike the example of Microsoft discussed at the end of Chapter 14 "Google in Three Parts: Search, Online Advertising, and Beyond"; Wal-Mart needs to find huge markets or dramatic cost savings in order to boost profits and continue to move its stock price higher.

The firm’s aggressiveness and sheer size also increasingly make Wal-Mart a target for criticism. Those low prices come at a price, and the firm has faced accusations of subpar wages and remains a magnet for union activists. Others had identified poor labor conditions at some of the firm’s contract manufacturers. Suppliers that compete for Wal-Mart’s business are often faced with a catch-22. If they bypass Wal-Mart they miss out on the largest single chunk of world retail sales. But if they sell to Wal-Mart, the firm may demand prices so aggressively low that suppliers end up cannibalizing their own sales at other retailers. Still more criticism comes from local citizen groups that have accused Wal-Mart of ruining the market for mom-and-pop stores.C. Fishman, “The Wal-Mart You Don’t Know,” Fast Company, December 19, 2007.

While some might see Wal-Mart as invincibly standing at the summit of world retail, it’s important to note that other megaretailers have fallen from grace. In the 1920s and 1930s, the A&P grocery chain once controlled 80 percent of U.S. grocery sales, at its peak operating five times the number of stores that Wal-Mart has today. But market conditions changed, and the government stepped in to draft antipredatory pricing laws when it felt A&Ps parent was too aggressive.

For all of Wal-Mart’s data brilliance, historical data offers little insight on how to adapt to more radical changes in the retail landscape. The firm’s data warehouse wasn’t able to foretell the rise of Target and other up-market discounters. And yet another major battle is brewing, as Tesco methodically attempts to take its globally honed expertise to U.S. shores. Savvy managers recognize that data use is a vital tool, but not the only tool in management’s strategic arsenal.

Collecting Data

Data drives the firm. Caesars collects customer data on just about everything you might do at their properties—gamble, eat, grab a drink, attend a show, stay in a room. The data’s then used to track your preferences and to size up whether you’re the kind of customer that’s worth pursuing. Prove your worth, and the firm will surround you with top-tier service and develop a targeted marketing campaign to keep wooing you back.V. Magnini, E. Honeycutt, and S. Hodge, “Data Mining for Hotel Firms: Use and Limitations,” Cornell Hotel and Restaurant Administration Quarterly, April 2003, http://www.entrepreneur.com/tradejournals/article/101938457.html.

The ace in the firm’s data collection hole is its Total Rewards loyalty card system. Launched over a decade ago, the system is constantly being enhanced by an IT staff of seven hundred, with an annual budget in excess of $100 million.P. Swabey, “Nothing Left to Chance,” Information Age, January 18, 2007. Total Rewards is an opt-inProgram (typically a marketing effort) that requires customer consent. This program is contrasted with opt-out programs, which enroll all customers by default. loyalty program, but customers consider the incentives to be so good that the card is used by some 80 percent of patrons, collecting data on over forty-four million customers.M. Wagner, “Harrah’s Places Its Bet On IT,” InformationWeek, September 16, 2008; and L. Haugsted, “Better Take Care of Big Spenders; Harrah’s Chief Offers Advice to Cablers,” Multichannel News, July 30, 2007.

Customers signing up for the card provide Caesars with demographic information such as gender, age, and address. Visitors then present the card for various transactions. Slide it into a slot machine, show it to the restaurant hostess, present it to the parking valet, share your account number with a telephone reservation specialist—every contact point is an opportunity to collect data. Between three hundred thousand and one million customers come through Caesars' doors daily, adding to the firm’s data stash and keeping that asset fresh.N. Hoover, “Chief of the Year: Harrah’s CIO Tim Stanley,” Information Week Research and Reports, 2007. 

Who Are the Most Valuable Customers?

All that data is heavily and relentlessly mined. Customer relationship management should include an assessment to determine which customers are worth having a relationship with. And because Caesars has so much detailed historical data, the firm can make fairly accurate projections of customer lifetime value (CLV)The present value of the likely future income stream generated by an individual purchaser.. CLV represents the present value of the likely future income stream generated by an individual purchaser.“Which Customers Are Worth Keeping and Which Ones Aren’t? Managerial Uses of CLV,” Knowledge@Wharton, July 30, 2003, http://knowledge.wharton.upenn.edu/article.cfm?articleid=820.  Once you know this, you can get a sense of how much you should spend to keep that customer coming back. You can size them up next to their peer group, and if they fall below expectations, you can develop strategies to improve their spending.

The firm tracks over ninety demographic segments, and each responds differently to different marketing approaches. Identifying segments and figuring out how to deal with each involves an iterative model of mining the data to identify patterns, creating a hypothesis (customers in group X will respond to a free steak dinner; group Y will want ten dollars in casino chips), then testing that hypothesis against a control group, turning again to analytics to statistically verify the outcome.

The firm runs hundreds of these small, controlled experiments each year. Loveman says that when marketers suggest new initiatives, “I ask, did we test it first? And if I find out that we just whole-hogged, went after something without testing it, I’ll kill ’em. No matter how clever they think it is, we test it.”J. Nickell, “Welcome to Harrah’s,” Business 2.0, April 2002. The former ops professor is known to often quote quality guru W. Edwards Deming, saying, “In God we trust; all others must bring data.”

When Caesars began diving into the data, they uncovered patterns that defied the conventional wisdom in the gaming industry. Big money didn’t come from European princes, Hong Kong shipping heirs, or the Ocean’s 11 crowd—it came from locals. The less than 30 percent of customers who spent between one hundred and five hundred dollars per visit accounted for over 80 percent of revenues and nearly 100 percent of profits.P. Swabey, “Nothing Left to Chance,” Information Age, January 18, 2007.

The data also showed that the firm’s most important customers weren’t the families that many Vegas competitors were trying to woo with Disneyland-style theme casinos—it was Grandma! The firm focuses on customers forty-five years and older: twenty-somethings have no money, while thirty-somethings have kids and are too busy. To the premiddle-aged crowd, Loveman says, “God bless you, but we don’t need you.”L. Haugsted, “Better Take Care of Big Spenders; Harrah’s Chief Offers Advice to Cablers,” Multichannel News, July 30, 2007.

Data-Driven Service: Get Close (but Not Too Close) to Your Customers

The names for reward levels on the Total Rewards card convey increasing customer value—Gold, Diamond, and Platinum. Spend more money at Caesars and you’ll enjoy shorter lines, discounts, free items, and more. And if Caesars’ systems determine you’re a high-value customer, expect white-glove treatment. The firm will lavish you with attention, using technology to try to anticipate your every need. Customers notice the extra treatment that top-tier Total Rewards members receive and actively work to improve their status.

To illustrate this, Loveman points to the obituary of an Ashville, North Carolina, woman who frequented a casino his firm operates on a nearby Cherokee reservation. “Her obituary was published in the Asheville paper and indicated that at the time of her death, she had several grandchildren, she sang in the Baptist choir and she was a holder of the [the firm’s] Diamond Total Rewards card.” Quipped Loveman, “When your loyalty card is listed in someone’s obituary, I would maintain you have traction.”G. Loveman, Speech and Comments, Chief Executive Club of Boston College, January 2005; emphasis added.

The degree of customer service pushed through the system is astonishing. Upon check-in, a Caesars customer who enjoys fine dining may find his or her table is reserved, along with tickets for a show afterward. Others may get suggestions or special offers throughout their stay, pushed via text message to their mobile device.M. Wagner, “Harrah’s Places Its Bet On IT,” InformationWeek, September 16, 2008. The firm even tracks gamblers to see if they’re suffering unusual losses, and Caesars will dispatch service people to intervene with a feel-good offer: “Having a bad day? Here’s a free buffet coupon.”T. Davenport and J. Harris, Competing on Analytics: The New Science of Winning (Boston: Harvard Business School Press, 2007).

The firm’s CRM effort monitors any customer behavior changes. If a customer who usually spends a few hundred a month hasn’t shown up in a while, the firm’s systems trigger follow-up contact methods such as sending a letter with a promotion offer, or having a rep make a phone call inviting them back.G. Loveman, Speech and Comments, Chief Executive Club of Boston College, January 2005.

Customers come back to Caesars because they feel that those casinos treat them better than the competition. And Caesars’ laser-like focus on service quality and customer satisfaction are embedded into its information systems and operational procedures. Employees are measured on metrics that include speed and friendliness and are compensated based on guest satisfaction ratings. Hourly workers are notoriously difficult to motivate: they tend to be high-turnover, low-wage earners. But at Caesars, incentive bonuses depend on an entire location’s ratings. That encourages strong performers to share tips to bring the new guy up to speed. The process effectively changed the corporate culture at Caesars from an every-property-for-itself mentality to a collaborative, customer-focused enterprise.V. Magnini, E. Honeycutt, and S. Hodge, “Data Mining for Hotel Firms: Use and Limitations,” Cornell Hotel and Restaurant Administration Quarterly, April 2003, http://www.entrepreneur.com/tradejournals/article/101938457.html. 

While Caesars is committed to learning how to make your customer experience better, the firm is also keenly sensitive to respecting consumer data. The firm has never sold or given away any of its bits to third parties. And the firm admits that some of its efforts to track customers have misfired, requiring special attention to find the sometimes subtitle line between helpful and “too helpful.” For example, the firm’s CIO has mentioned that customers found it “creepy and Big Brother-ish” when employees tried to greet them by name and talk with them about their past business history with the firm, so it backed off.M. Wagner, “Harrah’s Places Its Bet On IT,” InformationWeek, September 16, 2008.

Innovation

Caesars is constantly tinkering with new innovations that help it gather more data and help push service quality and marketing program success. When the introduction of gaming in Pennsylvania threatened to divert lucrative New York City gamblers from Caesars’ Atlantic City properties, the firm launched an interactive billboard in New York’s Times Square, allowing passersby to operate a virtual slot machine using text messages from their cell phones. Players dialing into the video billboard not only control the display, they receive text message offers promoting Caesars' sites in Atlantic City.“Future Tense: The Global CMO,” Economist Intelligence Unit, September 2008. 

At Caesars, tech experiments abound. RFID-enabled poker chips and under-table RFID readers allow pit bosses to track and rate game play far better than they could before. The firm is experimenting with using RFID-embedded bracelets for poolside purchases and Total Rewards tracking for when customers aren’t carrying their wallets. The firm has also incorporated drink ordering into gaming machines—why make customers get up to quench their thirst? A break in gambling is a halt in revenue.

The firm was also one of the first to sign on to use Microsoft’s Surface technology—a sort of touch-screen and sensor-equipped tabletop. Customers at these tables can play bowling and group pinball games and even pay for drinks using cards that the tables will automatically identify. Tech even helps Caesars fight card counters and crooks, with facial recognition software scanning casino patrons to spot the bad guys.S. Lohr, “Reaping Results: Data-Mining Goes Mainstream,” New York Times, May 20, 2007.

And Total Rewards is going social, too. Caesars has partnered with Silicon Valley—based TopGuest to tie social media to its loyalty program. Caesars customers who register with TopGuest (which also works with clients Virgin America, Holiday Inn, and Avis, among others) can receive fifty Total Rewards bonus credits for each geolocation check-in, tweet, or Instagram photo taken at participating venues.“Topguest Rewards Vegas Visitors for Social Check-Ins,” Pulse of Vegas Blog (hosted at Harrahs.com), April 22, 2011.

Strategy

A walk around Vegas during Caesars’ ascendency would find rivals with bigger, fancier casinos. Says Loveman, “We had to compete with the kind of place that God would build if he had the money.…The only thing we had was data.”P. Swabey, “Nothing Left to Chance,” Information Age, January 18, 2007.

That data advantage creates intelligence for a high-quality and highly personal customer experience. Data gives the firm a service differentiation edge. The loyalty program also represents a switching cost. And these assets combined to be leveraged across a firm that has gained so much scale that it’s now the largest player in its industry, gaining the ability to cross-sell customers on a variety of properties—Vegas vacations, riverboat gambling, locally focused reservation properties, and more.

The firm’s chief marketing officer points out that when the Total Rewards effort started, the firm was earning about thirty-six cents on every dollar customers spent gaming—the rest went to competitors. A climb to forty cents would be considered monstrous. But within a few short years that number had climbed to forty-five cents, making Caesars the biggest monster in the industry.E. Lundquist, “Harrah’s Bets Big on IT,” eWeek, July 20, 2005. Some of the firm’s technology investments have paid back tenfold in just two years—bringing in hundreds of millions of dollars.P. Swabey, “Nothing Left to Chance,” Information Age, January 18, 2007.

The firm’s technology has been pretty tough for others to match, too. Caesars holds several patents covering key business methods and technologies used in its systems. After being acquired by Harrah’s, employees of the old Caesars properties lamented that they had, for years, unsuccessfully attempted to replicate Harrah’s systems without violating the firm’s intellectual property.N. Hoover, “Chief of the Year: Harrah’s CIO Tim Stanley,” Information Week Research and Reports, 2007.

Challenges

Caesars’ efforts to gather data, extract information, and turn this into real profits is unparalleled, but it’s not a cure-all. Broader events can often derail even the best strategy. Gaming is a discretionary spending item, and when the economy tanks, gambling is one of the first things consumers will cut. Caesars has not been immune to the world financial crisis and experienced a loss in 2008.

Also note that if you look up Caesars’ stock symbol you won’t find it. The firm was taken privateThe process by which a publicly held company has its outstanding shares purchased by an individual or by a small group of individuals who wish to obtain complete ownership and control. in January 2008, when buyout firms Apollo Management and TPG Capital paid $30.7 billion for all of the firm’s shares. At that time Loveman signed a five-year deal to remain on as CEO, and he’s spoken positively about the benefits of being private—primarily that with the distraction of quarterly earnings off the table, he’s been able to focus on the long-term viability and health of the business.A. Knightly, “Harrah’s Boss Speaks,” Las Vegas Review-Journal, June 14, 2009. Plans for a late 2010 IPO were put on hold as the economy continued to sour.C. Vannucci and L. Spears, “Harrah’s Pulls $531 Million Private Equity-Backed IPO,” BusinessWeek, November 19, 2011.

But the firm also holds $24 billion in debt from expansion projects and the buyout, all at a time when economic conditions have not been favorable to leveraged firms.P. Lattman, “A Buyout-Shop Breather,” Wall Street Journal, May 30, 2009.  A brilliantly successful firm that developed best-in-class customer relationship management is now in a position many consider risky due to debt assumed as part of an overly optimistic buyout occurring at precisely the time when the economy went into a terrible funk. Caesars’ awesome risk-reducing, profit-pushing analytics failed to offer any insight on the wisdom (or risk) in the debt and private equity deals.