Grey Code Corporation
Grey Code Corporation (GCC) is a media and marketing company involved in magazine and book publishing and television broadcasting. GCC’s portfolio of home and family magazines have been a long running strength, but the company has expanded to become a provider of a spectrum of services (market research, communications planning, Web site advertising, etc.) that can enhance its clients’ brands. GCC’s relational database contains over a terabyte of data encompassing 75 million customers. GCC uses the data in its database to develop campaigns for new customer acquisition, customer reactivation, and identification of cross-selling opportunities for products. For example, GCC will generate separate versions of a monthly issue of a magazine that will differ only by the advertisements the magazines contain. They will mail a subscribing customer the version with the print ads that the GCC database has determined will most interest that customer. A problem facing GCC is how to boost the customer response rate to the renewal offers that it mails to its magazine subscribers. The industry response rate is about 2 percent, but GCC has historically had a higher response rate. GCC’s director of database marketing, Chris Grey, wants to ensure that GCC maintains its place as one of the top achievers in targeted marketing. In one effort directed at maintaining this position, GCC is currently considering the development of a targeted marketing strategy for a hobby-based magazine. The file GCC contains 38 columns (each corresponding to a distinct variable) and over 40,000 rows (each corresponding to a distinct customer). The Description worksheet contains a description of each variable in the file GCC. The Data worksheet contains the data to be used to train, validate, and test a classification method. The NewDataToPredict worksheet contains data on a new set of former subscribers to the hobby-based magazine whom GCC would like to classify as likely or not likely to respond to a targeted renewal offer.
Managerial Report
Play the role of Chris Gray and construct a classification model to identify customers who are likely to respond to a mailing. your report should include the following analyses:
1. The data provided is still relatively raw. Prepare the date for data mining by addressing missing data entries and transforming variables. you may want to use XLMiner’s Missing Data Handling utility and Transform Categorical Data utility.
2. Explore the data. Due to the large number of variables, you may want to identify means of reducing the dimensions of the data. In particular, analyze relationships between variables using correlation and pivot tables.
3. Experiment with various classification methods and propose a final model for identifying customers who will respond to the targeted marketing.
a. Provide a summary of the classification model that you select.
b. Provide a decile-wise lift chart and analysis of your model’s performance on the test set.
c. Provide a chart of the Class 1 and Class 0 error rates on the test set for various values of the cutoff probability.
d. Classify the customers in the NewDataToPredict worksheet.