With the transactional data standardized and flowing to Parable weekly, “client stores now know the ROI of advertising dollars spent with Parable,” says Blair.
Furthermore, Parable’s mailing team has processed millions of records, resolving many of the data issues that pre-existed.
Tools Used: Microsoft Access for database analysis, Microsoft SQL relational database engine, Microsoft Visual Basic, SAS and FirstLogic Postalsoft.
Case Study: Publisher grapples with its legacy system
Company: A medium-sized company combining publishing and merchandising.
Situation: The company, which prefers not to be named, was using “an antiquated legacy system that managed operations as well as transactional systems,” says Shamez Dharamsi, manager of implementation and senior consultant for Quaero, the Charlotte, N.C.-based marketing performance company that worked with the publisher.
Problem: Over the system’s 20-year life “there had been a lot of patchwork in terms of home-grown processes; a lot of fitting data into fields where it didn’t belong,” explains Dharamsi. “You couldn’t change the length of a field without changing the code. There was a lot of force fitting.”
The company was dissatisfied with the accuracy of the data as well. There were fields where nobody but the original programmer—who was no longer with the company—knew what it was. “They may have force-fed elements into a field and now, 10 years later, that field is being populated and nobody knows how,” says Dharamsi.
Further, the company had limited access to the data through the IT department, and was not able to access multiyear data at all. Because of the way the database was designed, at the end of each publication year the company would make a tape copy of the prior year’s data and essentially hit reset on the data-capture program. Lifetime value and year-to-year sales were almost impossible to track.
Goal: The company wanted easy access to its data and it wanted to be able to contact its customers in a more sophisticated way. Dharamsi hoped to salvage and clean as much of the company’s year-to-year data as possible and incorporate it into a relational database.