by Meridith Levinson

The Brain Behind the Big, Bad Burger and Other Tales of Business Intelligence

May 15, 200719 mins
Business Intelligence

It’s been called “THE FAST FOOD EQUIVALENT of a snuff film” by one health and nutrition advocacy group. Jay Leno made cracks about it on The Tonight Show. Even The New York Times devoted an editorial to its excesses.

The Monster Thickburger, the latest piece de resistance from burger joint Hardee’s, consists of:

  • Two charbroiled 100 percent Angus beef patties, each weighing in at a third of a pound (150 grams)
  • Three slices of processed cheese
  • Four crispy strips of bacon

It’s topped with a dollop of mayonnaise that oozes from a toasted buttery sesame seed bun.

The Monster Thickburger tips the scales at a whopping 1420 calories (5945 kilojoules) and an artery-clogging 107 grams of fat. It quite possibly is the most fattening mass-produced burger on the planet, and it’s selling like gangbusters, according to Jeff Chasney, CIO and executive vice president of strategic planning at CKE Restaurants, the company that owns and operates Hardee’s.

You’d think that CKE would have thought twice about rolling out such an over-the-top concoction in the midst of a national obsession with the growing epidemic of obesity in the US. But CKE was able to introduce the Monster Thickburger nation-wide on November 15, 2004, with such confidence (if not impudence) that the US public would receive it with open mouths because of the insights the company obtained from its business intelligence (BI) system. BI refers to a variety of software applications that analyze an organization’s raw data and extract useful insights from it. BI as a discipline is made up of many related activities, including data mining, online analytical processing, querying and reporting.

CKE used its BI system, known ironically inside the company as CPR (CKE Performance Reporting), to monitor the performance of its big, bad burger in test markets. Specifically, CKE used BI to see if the hamburger was actually contributing to increases in sales at restaurants or if it was just cannibalizing sales of other, lesser burgers. The company wanted to evaluate whether the increases in sales from the burger were worth the cost to produce it. CKE used its BI software to study a variety of factors – such as menu mixes, the cost to produce a Monster Thickburger, average unit volumes for the Thickburger compared with other burgers, gross profits and total sales for each of the test stores, and the contribution that each menu item (including the Monster Thickburger) made to total sales. Because the Monster Thickburger exceeded expectations in test markets, the company decided to roll it out nation-wide and to devote around $US7 million in advertising to promoting it. CPR gave CKE the confidence it needed to introduce such a burger and to know that the advertising dollars behind it wouldn’t be a waste.

And, in fact, it’s been a resounding success; sales of the burger bomb continued to exceed expectations in December 2004. Sales at Hardee’s stores that have been open at least a year were up 5.8 percent for December, and “the Monster Thickburger was directly responsible for a good deal of that increase”, says Brad Haley, Hardee’s executive vice president of marketing.

Smart Food

Restaurant chains such as Hardee’s, Wendy’s, Ruby Tuesday, T.G.I. Friday’s and others are heavy users of BI software. Many of the big chains have been using BI for the past 10 years, according to Chris Hartmann, managing director of technology strategies at HVS International, a restaurant and hospitality consultancy. They use BI to make strategic decisions, such as what new products to add to their menus, which dishes to remove and which underperforming stores to close. They also use BI for tactical matters like renegotiating contracts with food suppliers and identifying opportunities to improve inefficient processes.

Because restaurant chains are so operations-driven, and because BI is so central to helping them run their businesses, they are among the elite group of companies across all industries that are actually getting real value from these systems. Want proof?

Carlson Restaurants Worldwide, the privately held company that operates T.G.I. Friday’s and Pick Up Stix restaurants, saved $US200,000 in 2003 by renegotiating contracts with food suppliers based on discrepancies between contract prices and the prices suppliers were actually charging restaurants. Carlson’s BI system, which at the time was from Cognos, had identified these discrepancies.

Ruby Tuesday’s profits and revenue have grown by at least 20 percent each year as a result of the improvements the chain has made to its menu and operations based on insights provided by its BI infrastructure, which consists of a data warehouse, analytical tools from Cognos and Hyperion, and reporting tools from Microsoft.

CPR helped CKE, which was on the brink of bankruptcy five years ago, increase sales at restaurants open more than a year, narrow its overall losses and even turn a profit in 2003. A home-grown proprietary system, CPR consists of a Microsoft SQL server database and uses Microsoft development tools to parse and display analytical information.

In June 2003, Wendy’s decided to accept credit cards in its restaurants based on information it got from its BI systems. Because of that decision, Wendy’s restaurants have boosted sales; customers who use a credit card spend an average of 35 percent more per order than those who use cash, according to Wendy’s executive vice president and CIO John Deane.

These restaurant chains’ successes are unusual considering the indigestion companies in other industries have got from their BI initiatives. “Most BI implementations fall below the midpoint on the scale of success,” says Ted Friedman, an analyst with Gartner. Restaurant chains use BI effectively and realize value from it for a variety of reasons, and other industries would do well to pay more attention to restaurant chains, according to Hartmann. Because their industry is so competitive, they have to be agile, so their cultures are accustomed to rapid change. Also, their BI initiatives are closely aligned with their business strategies, and the insights that their BI systems produce contribute to improving operations and the bottom line. Finally, they’ve found ways to address three of the biggest barriers to BI success: having to winnow through voluminous amounts of irrelevant data, poor data quality and user resistance.

“If you’re just presenting information that’s neat and nice but doesn’t evoke a decision or impart important knowledge, then it’s noise,” says CKE’s Chasney. “You have to focus on what are the really important things going on in your business,” he says.

At Ruby Tuesday – as at most restaurants and, indeed, in most companies – sales, products and service are the most important levers in its business. So, in August 2003, when the chain’s BI system identified a restaurant in Knoxville, Tennessee, that was underperforming, it used the very same system to drill down into that store’s specific problems in an effort to help the company determine what corrective actions to take.

The company’s BI software indicated that customers were waiting longer than normal for tables and for their orders once they were seated. It was a recipe for customer dissatisfaction, and of course poor sales. Management at corporate headquarters wanted to know what specifically was wrong. Was the restaurant not adequately staffed? Was the problem with the kitchen staff, a server, an assistant manager, a general manager – or with something beyond the company’s control, like the location?

Managers used BI tools to study food costs. High food costs might have indicated inadequately trained cooks who were ruining a lot of food before getting dishes right, which would have contributed to increased wait times. But food costs were normal.

Managers then assessed the time it took for a table to change hands from one patron to the next, using the BI system to calculate the time between when a waitstaffer opened a docket on the point of sale to the time the customer paid the tab. Nick Ibrahim, senior vice president and CIO of Ruby Tuesday, says the average time it takes a restaurant to turn over a table from one customer to the next is 45 minutes. So if the company sees in its BI system that it takes 55 to 60 minutes to close a bill at a particular restaurant, people aren’t getting their food as fast as they should. (The problem is rarely a matter of diners lingering over their meals, especially if it’s taking the waitstaff at every table 55 minutes to close the docket.) Management concluded based on this information and by visiting the restaurant that the long wait times were a result of increased demand. The area had been through an economic boom, and the restaurant was running at full capacity. The company made changes to the layout of the kitchen, the placement of food and the location of cooks so that everyone had easy access to the food and equipment they needed to produce dishes faster, to move more customers through the restaurant and ultimately to increase sales. The changes increased the rate at which tables were turned by 10 percent, which in turn decreased wait times for customers.

Insights Are the Meat; Data Is the Relish

The problem with so many BI tools, says Chasney, is that they’re no different from the standard corporate reporting tools that have been around for years, which churn out old data like curdled butter and don’t provide information that executives can chew on. If companies really want to get value from BI, he says, they need a system that provides them with insights, not just mountains of data. “There’s nothing worse, in my opinion, than a business intelligence system that reports changes on a weekly basis,” he says, because those systems don’t provide any context as to what factors are influencing those changes. Without that context, you don’t know whether the data is good or bad; it’s just useless.

When charting a course for BI, Chasney advises companies to first analyze the way they make decisions and to consider the information that executives need to facilitate more confident and more rapid decision making, as well as how they’d like that information presented to them (for example, as a report, a chart, online, hard copy). Discussions of decision making will drive what information companies need to collect, analyze and publish in their BI systems.

When Chasney started building CPR in 2000, he asked the company’s CEO and the chief operating officers of CKE’s three restaurant chains – Hardee’s, Carl’s Jr and La Salsa Fresh Mexican Grill – what information is most important in their efforts to run the company. The CEO wanted to know what caused changes in sales. The COOs wanted something that would indicate business opportunities they could pursue as well as clear indicators as to which restaurants were underperforming. The discussions taught Chasney that BI systems need to focus on a company’s most important performance indicators – including sales and cost of sales; exceptions, such as those areas of the business that are outperforming or underperforming other segments; and historical and forward-looking business trends – if they’re to provide the company with any value.

Good BI systems also need to give context. It’s not enough that they report sales were X yesterday and Y a year ago that same day, says Chasney. They need to explain what factors influencing the business caused sales to be X one day and Y on the same date the previous year. CPR uses econometric models, which the company reviews and refines each month, to provide context and to explain performance. The econometric models take into consideration 44 factors, including the weather, holidays, coupon activity, discounting, free giveaways and new products. If the CEO wants to find out why sales were down on any given day at Hardee’s, all he has to do is click the “explain” button on his computer screen, and the model performs its magic. The CEO will see, for example, that 5 percent of the 8 percent decrease was due to torrential rain in the US Northeast and 2 percent was due to free giveaways.

“If your business intelligence system is not going to improve your decision making and find problem areas to correct and new directions to take, nobody’s going to bother to look at it,” says Chasney.

Start with the Freshest Ingredients

The key to getting accurate insights from BI systems is standard data. “Data quality remains a very overlooked issue in business intelligence, but a massive one,” says Gartner’s Friedman. “I continue to see failures due to a lack of attention to data quality.” Data is the most fundamental component of any BI endeavour. It’s the building blocks for insight. Companies have to get their data stores and data warehouses in good working order before they can begin extracting and acting on insights. If not, they’ll be operating based on flawed information.

Ruby Tuesday’s Ibrahim advises companies to develop plans that outline what they’re going to do with data once they get it, practices for preventing redundant data and methods for organizing it in a way that makes sense to the business. For instance, Ruby Tuesday organizes its data around three categories – sales, labour and food costs – that happen to be the key drivers of its business. Those three categories are tracked in a database and put into separate table spaces for ease of reporting and processing, Ibrahim says. That way, information on what products are selling does not get mixed up with information on labour and vice versa.

Knowing that the key to using information to improve decision making is ensuring that the transactional data collected at the point of sale is consistent and accurate, Ibrahim standardized all of the company’s restaurants (700 at the time), including those run by franchisees, on a common technology platform in 2001. He also moved the company onto a Microsoft SQL server and open-architecture databases, which makes it easier for business analysts to get to the data they need. The open architecture lets analysts run specific queries against databases when they’re looking to find out, say, how many margaritas the company sold on Cinco de Mayo, rather than having to sift through mountains of data to get the answer.

Unfortunately, few companies have the luxury of replacing disparate technology with common systems across all of their units. Wendy’s is a case in point. While all 1500 of the company-owned restaurants use the same technology, approximately 5000 franchises don’t. The sales data that franchises send to corporate headquarters looks different from the data that company-owned stores submit because franchise data is reported on a weekly basis at an aggregate level. By contrast, more granular transactional data collected directly from the point-of-sale systems of company-owned stores is sent to corporate headquarters on a daily basis. As a result of those differences, Wendy’s corporate doesn’t have the highest possible level of visibility into its franchise operations.

Wendy’s Deane acknowledges that this less-than-ideal environment for BI creates problems for the company when it needs to compare aggregated sales information from franchises with transactional data from company-owned stores – it’s a hamburgers to cheeseburgers comparison. He says the company needs to increasingly make these comparisons as it looks to expand the pool of stores it uses for product testing and as it attempts to improve supply chain integration. To compensate for their suboptimal data collection environment, Deane is using an XML standard to collect more detailed information from franchisees who operate a large number of stores. (For smaller franchises, Wendy’s uses a Web-based data collection system.) He also uses heuristics, or rules of thumb, based on activity at company-owned stores to extrapolate meaning from the aggregate data that franchises provide. For example, if a franchise-owned store does $US30,000 worth of business in a week, Wendy’s corporate can make assumptions as to how that $U S30,000 would break down into sales of french fries, baked potatoes, hamburgers, chicken sandwiches and the like based on sales from company-owned stores in similar markets with similar aggregate sales histories. Proxies such as these may not be perfect, but they are a practical workaround and can be modified as needed to accommodate further integration with other systems, like the point of sale. Wendy’s has no plans to get its franchises on standard technology because it sees its franchisees as entrepreneurs capable of making their own decisions about their operations, including choice of technology.

Because Wendy’s is starting to understand the importance of having standard data to fuel business initiatives such as supply chain integration, the company was able to replace the phone lines and unstable modems that stores were using to transmit data to headquarters with a satellite connection in September 2002. The new, stable network helped improve the amount and quality of data that headquarters collects from both franchise- and company-owned stores. Where in the past Wendy’s would miss information from as many as 40 stores out of 1200 due to unstable modems, it now gets consistent information from 1483 out of 1488 stores every night.

Why Force-Feeding Won’t Work

Like so many technology projects, BI won’t yield returns if users feel threatened by, or are sceptical of, the technology and refuse to use it as a result. And when it comes to something like BI, which, when implemented strategically ought to fundamentally change how companies operate and how people make decisions, CIOs need to be extra attentive to users’ feelings.

When Wendy’s began using its BI system to generate sales forecasts for stores, operators were sceptical. They didn’t think technology could possibly take into consideration how local factors – such as weather, events and traffic patterns – affect their sales. Deane recognized that it’s tough for people to quit relying on their experience and gut, so he listened to operators’ concerns. Instead of forcing them to accept the forecasts, which he knew to be extremely accurate, he told them they could modify the forecasts from the BI system so long as they explained why and provided they later compared actual sales with what they forecasted and what the system predicted. The operators who modified the forecasts realized that the technology was often more accurate than they were. When they saw that they could improve their operations by better staffing their restaurants and more accurately ordering food to meet forecasted demand, they increasingly embraced BI. In effect, Deane let the users come to the trough on their own terms.

One might argue that Wendy’s could have got better results more quickly had it forced store managers to use the forecasts. However, if it had, it would have run the risk of facing mutiny from the operators. And had store operators fought the forecasts, that would have disrupted operations much more than the delay the company experienced by letting operators modify the forecasts. Deane says being sensitive to users’ concerns was more important, even at the expense of slowing down the rate of return.

“Trying to convince 1500 store managers to automatically accept a new tool that is going to have an impact on their ability to perform in their store is no trivial matter. You have to be very, very careful how you deal with the change management and the acceptance side of an implementation,” says Deane. And if you do it right, you can realize an ROI of 430 percent over a five-year period, according to IDC. “Of all the projects that one attempts to do as a CIO, business intelligence, if well managed – and it’s not always well managed – contributes far, far more than it costs,” Dean adds.

SIDEBAR: Tips for Getting BI Right

  • Analyze how executives make decisions.
  • Consider what information executives need in order to facilitate quick, accurate decisions.
  • Pay attention to data quality.
  • Devise performance metrics that are most relevant to the business.
  • Provide the context that influences performance metrics.
  • Take into account users’ feelings, and address their concerns up front.

SIDEBAR: The Value of Plastic

CREDIT CARDS: To accept them at Wendy’s restaurants or not to accept them? That was the question facing executives in early 2003. Sure, customers would appreciate the convenience of being able to pull out the plastic when they were short on cash to purchase a value meal, but would such an option be a losing proposition? Executives decided to test the impact that credit cards would have on sales by accepting them in select stores. The company used its business intelligence system to determine how a credit card purchase affects sales and speed of service, and to measure the amount of cannibalization from credit cards – in other words, the number of transactions that would have been in cash but that are now on credit because it’s an available option. To their surprise, executives learned that people who use credit cards spend more and buy more than they would if they were using cash. People who pay cash tend to buy value meals, which, while good for consumers’ pockets, are less profitable for Wendy’s. By contras t, consumers who pay with plastic tend to order a la carte, which tallies up to a larger tab. Indeed, the average docket paid for by credit card was 35 percent higher than dockets paid for in cash. With sales numbers like that, Wendy’s introduced credit card readers nationally in June 2003. SIDEBAR: How BI Uncovered Rip-offs

Carlson Restaurants Worldwide (CRW), the Dallas-based operator of T.G.I. Friday’s and Pick Up Stix restaurants, uses business intelligence software from Cognos to identify discrepancies between prices they’ve negotiated for food supplies from their vendors and what their vendors are actually charging them on invoices.

Here’s how it works: Cognos parses all of the company’s invoices from suppliers and contracts with food vendors in CRW’s data warehouse to see if any of the vendors are actually charging restaurants for food at a higher rate than the chain had negotiated. Since CRW contracts with a variety of different suppliers for meats, dairy products and produce, it also uses Cognos to pinpoint the suppliers that offer the best deals – and stick to them.

The company saved $US200,000 in 2003 by remedying these discrepancies and by giving more business to suppliers with the most competitive prices.