CA Auto Club Designs Data Warehouse
Two experts from the California State Automobile Association have dealt with this issue within the context of a major company. They built an effective database and offer much-needed answers.
Alexandra Morehouse and Daniel St. John worked together on database marketing projects at American Express for 15 years before joining the California AAA 18 months ago. As vice president of membership relationship development and director of database marketing, respectively, their assignment was to build a data warehouse; they were given a budget of $17 million to $22 million.
The California AAA is a massive 100- year-old organization with 7,000 employees in 80 different offices throughout the state. It provides road service, insurance, travel and other services. It's also the largest travel agency in California. The company's 102 different legacy systems had never been combined before into a single-marketing database. And, there was a lot of data - but very little information. People throughout the company needed information and could not get it. Thus, data warehouse was obviously a huge undertaking.
Morehouse and St. John knew from experience that a data warehouse should take at least two years to build. They had seen other organizations get bogged down building warehouses were never used and which lost support from top management because of the costs and delays associated with construction. They did not want to lose support for their project, however, so they took a very brave step: They turned down the $22 million, telling management they could build a marketing database for less than a tenth of that total - as well as have it ready in less time.
The marketers at the AAA wanted answers to simple questions like Who is most likely to want cruises to Hawaii? Six programmers were kept busy writing code to get the answers. The 102 different legacy feeds had different formats and were in many different languages. There was no contact management or promotion history available to users. The AAA marketers had no way of knowing if they had recently talked to a given customer or what the message had been.
The team struck out on simple but important business objectives. Instead of building a warehouse, they wanted to solve immediate marketing problems by building a robust profile of each customer with a cross-product view. Since the 80 AAA offices were scattered statewide, the team wanted a point-and-click campaign system that could be accessed through the Internet. Using the Web eliminated the need for setting up a costly statewide LAN system.
From experience, they knew that business users had to have tools they could use. In any organization, there are a few power users who really understand marketing databases, but that was not good enough for the team. The team wanted to reach the hundreds of marketers who wanted something they could use to make their customer retention and cross-selling programs work better, without having to know advanced computer programming or statistical analysis.
Of course, they had to sell the system to top management, which had initially requested a data warehouse. What management really wanted, however, was to see what their customers looked like. So the team suggested they do a marketing database. Instead of amassing together all the information in the company, why not just collect the data pertaining to their customers? They knew that if you can get a home run early on and prove that your system is working, you are much more likely to get top-level support.
The requirements for the new system had to include: a campaign manager; a flexible table structure to support SAS and Cognos; a mechanism to support multiple feeds from multiple sources; and scalable growth. They built it, at first, for only a few users and a few projects. But the system had to be capable of expanding rapidly to handle thousands of users, millions of customers and thousands of simultaneous projects.
Selecting the vendor. From their vast experience, Morehouse and St. John knew that the build process had to be outsourced. They issued an RFP to a dozen different vendors. The team visited various vendors' clients, including Schwab, Autodesk and Macromedia. In the end, they picked E.piphany, a Silicon Valley start-up, as their vendor. A site visit to E.piphany convinced them that this vendor would provide them with high-quality people who would be with them throughout the project.
The project methodology involved a phased approach getting the data mart up and running right away to handle membership, emergency road service and other basic requirements. Integration with the travel service was reserved for phase two, and the insurance and claims systems saved for phase three. To ensure that the tools would be of service to business users, Morehouse and St. John selected 15 people as a beta test group. They gave this group the tools first. Then the group helped them debug the system and get it up and running. At the same time, they developed an exit strategy.
Adding travel, insurance, claims and the Web. Phase two focused on the use of the travel data. There was lots of operational data that had never been used before. Using the system, they could develop a profile, of those using the travel service. It gave them an important advantage and convinced management of the benefit of the system. The return on investment was very high.
Phase three involved adding the Web data, the insurance program and the claims system. The server selected was a Compaq 45000, which included four 400 Mhz processors, four gigabytes of RFM, 16 of the 18 gigabyte disks, an SQL 7.0 Enterprise query tool, and Microsoft Internet Explorer 4.0 as the Web browser for the users. One year after launch, there were 150 business users who had direct access to data and were using it in their marketing programs. They used easy analytical tools that they accessed over the Web. The old practice of queuing of requests was terminated. Everyone got and printed what they wanted directly on their own printers, working through the Web.
This system cut the time of getting a mailing tape out, from two weeks to two or three days. Two years worth of promotion history was loaded into the system from more than 100 legacy systems. They now had a cross-functional view of their members, with lifetime value computed and appended to each member's record. And importantly, compared to a data warehouse, the California AAA marketing database is very inexpensive. The total cost the first year was $1.2 million.
Lessons learned. Probably the most important lesson was that the "start small and start fast" approach really worked well. Instead of taking two years to build a giant warehouse, the team had a functioning marketing database up and running in six weeks.