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Uncorking a Business Recovery, Part 1

Louise’s Trattoria was hardly the place to be seen in the late 1990s. The Los Angeles-based restaurant chain, which served up low-priced, traditional Italian fare, was bankrupt and losing sales at a rate of 10 percent a year, with few repeat customers. Fast forward three years, and a lot has changed. A revamped Louise’s is […]

Written By

Beth Stackpole

May 21, 2001

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Louise’s Trattoria was hardly the place to be seen in the late 1990s. The Los Angeles-based restaurant chain, which served up low-priced, traditional Italian fare, was bankrupt and losing sales at a rate of 10 percent a year, with few repeat customers.

Fast forward three years, and a lot has changed. A revamped Louise’s is now upscale enough to attract the likes of Barbra Streisand, Jim Carrey and other Hollywood celebrities who regularly stop by for a casual meal.

How did Louise’s new management orchestrate the turnaround? With hard work and one secret ingredient: a data mining initiative that uncorked the demographic makeup, along with their likes and dislikes, of Louise’s best customers.

At a Glance

Company: Louise’s Trattoria is a Los Angeles restaurant chain with annual revenues of $18 million.

Problem: New owners of the bankrupt restaurant chain wanted to boost customer satisfaction to increase sales.

Solution: They bought a service from Gazelle Systems that created in-depth customer profiles, including demographic and psychographic information, of the
chain’s top 500 customers, using point-of-sale credit card data. Based on profiles, Louise’s orchestrated changes, from remodeling restaurants to redesigning wine
lists, to become more attractive to its best customers.

The analysis showed that the chain’s patrons were far more affluent than it had assumed. With that data in hand, Louise’s embarked on a strategy to fine-tune everything from its menu selection and wine list to its atmosphere — all in an effort to appeal to a more discerning clientele.

“The analysis told me the customers we had were not responding to the type of offering we had,” says Fred LeFranc, president and CEO of LT Acquisition Corp., the Los Angeles company that snapped up the 13-restaurant chain in December 1997 and pulled it out of bankruptcy. “We realized we had to create an image to
make it cool to eat at Louise’s — we couldn’t be perceived as a spaghetti palace.”

Slicing and Dicing

Louise’s did what many companies across all industries are doing — use technology to slice and dice customer data and track purchasing behavior to create a more personalized experience for buyers. While airlines, supermarkets, and retailers, among other market segments, have been using this approach for years to get a
closer read on their clients, the restaurant business has operated more by gut instinct and a belief in the product and less by researching customer demand.

“In the old days, as long as you had great food, provided extra service and had a good location, you had a formula to produce repeat customers,” said Cathy Enz, professor of innovation and dynamic management at Cornell University, in Ithaca, N.Y., and executive director of the school’s center for hospitality research. “That’s necessary, but it’s not sufficient any more. Now there needs to be much more strategic use of customer information to make better business decisions.”

The restaurant industry, in part, is taking cues from what’s happening on the Web. Online pioneers like Amazon.com have built businesses by leveraging customer data and information collected during the shopping or browsing experience to personalize subsequent transactions.

That kind of technology was not readily available to the hospitality industry until recently, Enz said, and even if it was, the majority of owners felt their time was better spent doing traditional chores like overseeing staff and kitchen operations or cultivating one-on-one relationships with customers.

“In the restaurant industry, everyone knows their labor and food costs, but what they haven’t been able to do is measure their performance based on customer behavior,” said Charlotte Bogardus, founder and chairman of Gazelle Systems, a customer relationship management and consulting firm, which worked with Louise’s on the data analysis project. “We’re trying to bring the same level of customer personalization that exists in the online world to the on-site world. Up until now, the technology hasn’t been there.”

Making the technology readily available to the hospitality industry is the mission of the 3-year old Gazelle. The Newton Upper Falls, Mass., firm provides a service that harvests historical credit card data from restaurants and matches it with in-depth demographic and psychographic information collected and sold by marketing companies.

Customer Profiles

The patented process, which Gazelle calls a “reverse append,” builds detailed customer profiles around each card number, providing insights into everything from household income to buying patterns, educational level and household location to within a radius of a few square miles. For privacy reasons, Gazelle assigns a unique customer identifier to each card instead of attaching a name to the specific profile, Bogardus said.

Along with the profiles, Gazelle delivers a suite of analytical tools used to create reports. These “Gaz Reports” aim to distill all the customer information provided by the database companies down to a few fields and readable charts, making the data easier to digest. The reports, Bogardus said, allow restaurant owners to monitor indicators like customer frequency as well as churn rates — the percentage of new vs. old customers.

Gazelle said it has whittled the report process from two months to about three weeks.

The CRM company’s approach is unique for other reasons, according to Professor Enz. First, it creates and analyzes a historical snapshot of customer data instead of relying on diary studies that ask current customers about their consumption habits. Second, Gazelle can cover a lot more ground that more manual diary approach.

“The problem with self-reporting studies has been that the person doing them may or may not be the best and most objective record keeper of what they’ve done,” Enz said. “Plus, we can’t do those for millions of people.”

With the help of data mining technology and techniques, Louise’s Trattorria makes a surprising discovery about its customers. Read about it tomorrow in Part 2.