Predictive Modeling – Burger King Identifies Core Customers

Picture of Contrarian Marketing

The case study below on Burger King and Predictive Modeling is from Chapter 2 of Contrarian Marketing, and is instructive to understanding how segmentation strategies can apply to many businesses – including fast food!  We hope you find this case study to be helpful in building skills to understand how contrarian marketing is ‘not magic’, but rather ‘just math‘.


Burger King ranks second only to McDonald’s as a fast-food empire chain. With 10,000+ restaurants in over 70 different countries. But in the last decade, sales at Burger King Corp. had plummeted with hundreds of its U.S. restaurants closing, and four of its ten largest franchisees filing for bankruptcy protection. After being run by 11 chief executives and four parent companies throughout two decades, Burger King was in danger of losing its place to Wendy’s International, Inc.

Private owners took over and the CEO was replaced with Harvard-trained MBA Greg Brenneman, who moved quickly to improve relationships with franchisees, comprising about 90 percent of the U.S. restaurants. His next step was to identify core customers and give them the products they wanted most.

What drove Burger King, the company soon determined, were “super fans,” the 25 percent of the customer base who did 50 percent of the spending, and dined at Burger King 15 or more times per month. Getting just one more visit from the “super fan”, was like a ten percent increase in comparable sales. The goal was to boost the restaurant’s annual sales figure from $970,000 to about $1.3 million.

Picture of Burger Kings Superfan stratgy

When the recession took hold in 2009, Burger King slowed its expansion efforts, yet continued to convert and court its customer base of 18-25 year-old male customers. This was the segmented group identified as frequently hungry, loyal, and steady, or “super fans”. Unlike the white-collar weight-conscious, these blue- collar workers burned a serious amount of calories on their jobs.

“If you look at the Enormous Omelet Sandwich,” said Brenneman, “we didn’t beat around the bush with the name. It’s an indulgent breakfast item, and it’s absolutely geared to our ‘super fan’.”

It was not only smart business, but rather a marketing and sales strategy that understood what their particular segmented customers wanted, and then gave it to them. As a result, Burger King was among the few restaurant chains doing well in the pits of the recession.

The strategy Burger King used was a form of predictive modeling, a way to utilize patterns that identify both risks and opportunities. Credit scoring companies use predictive analysis to determine a customer’s rank and the likelihood they will make future payments on time.

Warren Buffett, investor, entrepreneur extraordinaire, and a respected economic voice in any industry, was referring to predictive modeling when he advised Microsoft founder Bill Gates, “to just keep things simple. Boil it down; work on those things that really count; think through the basics. Then it’s amazing what you can do.” Gates referred to Buffett’s advice as “a special form of genius” (, by A. Crippen, 06/22/09).

Picture of Warren Buffett Quote on Keeping Things Siple

In college, I read the autobiography of Sam Walton, founder of Wal-Mart, who credited his company’s success (among many things) to its ability to work faster than the competition. He believed that since customers’ interests always changed, and since every market is different, Wal-Mart needed a core competency to test customer interest and identify customer trends. It was essential that they be ready to move quickly when the market shifted.

Wal-Mart has a culture of ‘try it, fix it, do it again…rapidly’ in which predictive modeling allows them to use data to manage every inch of their business. This data tracking gives them instant information to stay current with customer preferences and changing demands. As a result, their best selling items are always in stock, leading to substantially increased profitability.

From my experience working with other companies, I have found Buffet’s advice on developing predictive models helpful. When I have implemented customer relationship management (CRM) systems over the past 10+ years, what I have had in mind was more than just growing a customer base; I wanted to cultivate the right customer base. In other words, I wanted to pick our customers before they picked us.

Source:  Excerpt from the Contrarian Marketing Book  Chapter 2

By Nick Mavrick

You can find Nick Mavrick on Google+

Intelligent Response specializes in managing and securing Strategic Marketing and Web Development projects from start to finish in Washington DC.

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