Pizza Hut orders a data warehouse with everything on it

SEATTLE — Forget thin crust versus cheese-stuffed: the pizza wars are being waged by mining the database, and one chain is not only scoring customer loyalty but speedy return on investment.

Pizza Hut’s customer relationship

management programs are built on a data warehouse of millions of customer records gleaned from point of sale transactions at its restaurants. They know your favourite topping, what you ordered last and whether you like salad with your meat lover’s pie. Much of that has to do with data mining, a technology that converts details from customer data into competitive intelligence that companies use to predict trends and behaviours.

Pizza Hut uses a product called Teradata Warehouse Miner and after a year using it to better manage direct mail campaigns, the chain and its parent company, Yum Brands (Pizza Hut is one of eight restaurants in the group that includes Kentucky Fried Chicken and Taco Bell) are starting to see results.

The chain, which is affiliated with Pepsi Corp., first got into the CRM game by using the more than 10 years of order history in its database, consisting of all purchases delivered to households that ordered by telephone.

Pizza Hut claims to have the largest fast food customer data warehouse in the world with 40 million US households or between 40 and 50 per cent of the US market, according to Keith Jones, the man responsible for analysis and management of direct marketing analytics for Pizza Hut International. Prior to joining Pizza Hut, Jones worked as a senior analyst for companies such as J.C. Penney, Dunn & Bradstreet, Nielsen TV Media Research and American Airlines. He compares the price wars that took place in the airline industry a few years ago to the battle for pizza supremacy now being waged in North America today using highly-competitive television and radio advertising.

For about six years the company did analysis on a small sampling of the 40 million households it knew about, but nothing it could really call data mining, says Jones.

“”There was no prediction or analysis, really,”” he said, speaking at Partners 2003, Teradata’s user group meeting being held here this week. “”There was a lot of error in the data because people moved around a lot and the phone numbers weren’t correct. We had to clean up the data.””

The existence of duplicate households in the warehouse (same family, different phone number) made it difficult to do direct mail campaigns because of higher postage costs. It was also a challenge to target the right homes for a particular campaign and to accurately apply results from a particular mail campaign, he said.

In the first year of using the Teradata Warehouse Miner product, Jones said Pizza Hut was able to recover the cost of licensing, integrating and training staff to use the product and it made money for the company in the first quarter of use.

Jones would not give the dollar value of revenue from using the data mining product, saying the information is now considered critical to the company’s competitive advantage.

“”We made so much money this year on this we’re afraid our competition will start using it,”” said Jones.

Using Warehouse Miner meant an improvement in “”household uniqueness”” from 80 per cent to 95 per cent. While direct mail has traditionally been a breakeven cost centre, Pizza Hut has managed to turn it into a profit centre with increased ROI, Jones said, in excess of 200 per cent. Again, precise ROI was not available as it is “”confidential.””

“”We use it to do target marketing and find the best coupon offer for that household. We can profile and segment customer households for groupings according to patterns of past pizza buying behaviours, offer preferences and price points,”” he said. “”We can also use it to predict the success of a campaign.””

Pizza Hut now tracks not only phone orders, but online orders and can track in real time what commercials people are watching and responding to.

By using Teradata Warehouse Miner, all data mining functions run directly inside the database.

“”A lot of data mining can be sloppy as a result of situations where they are sampling from too small a source and it comes up with conclusions that are too preliminary. With Teradata Miner, you can scan all the data as opposed to sampling,”” he said.

Keith Gonzales, president of El Paso, Tex.-based The Focus Group, Ltd., a company that offers its clients information on managing data warehouses said in-database mining is good for medium and large data volumes, eliminates the cost of data movement and allows for a collaborative environment.

“”When you look at the needs of organizations, you can¡¯t do (data mining) using traditional architectures. You have to marry together data mining into warehouse architecture,”” said Gonzales.

He said database vendors are pushing mining into the warehouse and they are doing it so companies can exploit all the technology of data mining from the database. “”Pizza Hut is taking advantage of this,”” he said.

The added benefits of in-database mining include minimizing data redundancy, reduced proprietary data structures and simplified data and system management.

“”You get better results using a larger amount of detailed data. You eliminate the human factor that can lead to potential errors during data movement and samples,”” he said.

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