Data Mining in Direct Marketing with Purchasing Decisions Data.
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1 Data Mining in Direct Marketing with Purchasing Decisions Data. Randy Collica Sr. Business Analyst Database Mgmt. & Compaq Computer Corp. Database Mgmt. &
2 Overview! Business Problem to Solve.! Data Layout and Brief Definitions.! Data Preparation Methods.! Purchasing Decisions Model Building. " Three different models were built. " Final model uses data cleansing ensemble methods. " Comparison of data cleansing with typical ensemble methods.! Summary.! References and Q & A.! Acknowledgements. 2 Database Mgmt. &
3 Business Problem to Solve.! The problem of telemarketing or telesales calling a potential customer site only to find out that the business makes their purchasing decisions at the parent or headquarters office only!! Time has been wasted calling the wrong site and the caller either has to find out the parent site or skip over to the next business to call.! If the telemarketer already knew what site to call ahead of time, much time and dollars would be saved as well as increasing the opportunity rate 3 Database Mgmt. &
4 Business Problem to Solve.! A similar issue arises for direct mail campaigns. A direct mailer is sent to a site, however, that site does not make the final call on IT purchasing decisions.! Another example is when analyzing customer or prospect data for segmentation analysis. The local vs. parent decision levels is a very important component in segmentation. 4 Database Mgmt. &
5 Data Assay and Brief Definitions.! The data set being modeled contained about 170,000 records.! Other demographic data was appended from syndicated sources e.g. Dun & Bradstreet.! The data partition was set to 70% training and 30% for validation.! Definition of a business site in the original data set prior to demographic appending is slightly different from the Dun & Bradstreet definition of a business site. 5 Database Mgmt. &
6 Data Preparation Methods.! The data preparation methods were done in five basic stages. " First, data was extracted and needed to be cross referenced in order to facilitate merging with a syndicated source; e.g. adding D&B site duns numbers. " Second, the D&B site duns numbers were referenced and added to the data set and mismatches were noted. These steps were done outside of Enterprise Miner. 6 Database Mgmt. &
7 Data Preparation Methods. " Third, the D&B site duns numbers were now merged with the more complete D&B database. " Fourth, the data was placed read into Enterprise Miner. " Fifth, data was then surveyed.! The data survey is a rather important component in the data mining process [1]. " Using the data transformation node data on the number if site employees and corporate employees needed to be transformed. 7 Database Mgmt. &
8 Data Preparation Methods. Distribution of Site Employee Counts 8 Database Mgmt. &
9 Data Preparation Methods. Site Employee Count after bucket transformation 9 Database Mgmt. &
10 Data Preparation Methods.! Using the Input Data Source node, the view of basic frequency distributions of fields is very valuable.! The original data source has three levels in the target or response field. " Local " Parent " Missing! It was then thought best to try and keep the missing in the target data. This was then modified to not include missing values. 10 Database Mgmt. &
11 Purchasing Decisions Model Building.! Three Different Models were tried and built. 11 Database Mgmt. &
12 Purchasing Decisions Model Building.! The first modeling attempt used a fairly standard Decision Tree and a single layer 4 neuron Neural Network. This produced fair results with the Decision Tree but very poor results with the Neural Net.! The second attempt used a similar Decision Tree and also a Decision Tree with an Ensemble node. This was used in the Bagging mode with 10 iterations. See reference [2].! The third attempt uses a new technique called data cleansing or an Ensemble Filter[3]. These will now be explained. 12 Database Mgmt. &
13 Purchasing Decisions Model Building.! An Ensemble model (Bagging) vs.. typical classifier training. A sample of a single classifier on a data set. Original Training Set Training-set 1: 1,2,3,4,5,6,7,8, A sample of Boosting on the same data set. Resampled Training Set Training-set 1: 2,7,8,3,7,6,3,1 Training-set 2: 1,4,5,4,1,5,6,4 Training-set 3: 7,1,5,8,1,8,1,4 13 Database Mgmt. &
14 Purchasing Decisions Model Building.! Ensemble Filters combine the outputs of base- level classifiers and then take a vote to see which instances should be kept. Training Correctly Learning Instances ==> Filter ==> Labeled ==>Algorithm Instances! Instances which are not correctly labeled are then discarded from model training. 14 Database Mgmt. &
15 Purchasing Decisions Model Building.! How decisions on instances to be used in training are determined. " Once different models are fit from x number of sampling methods, one now has predictions of x models. " Two methods: Majority vote vs.. Consensus vote. " Majority Vote: Will tag an instance as mislabeled if more than half of the x classifier models classify it incorrectly. " Consensus Vote: Requires that all of the x classifier models must fail to classify correctly in order for that instance to be eliminated from training. 15 Database Mgmt. &
16 Purchasing Decisions Model Building. Majority Vote: Models from sample sets X X X X O O O O O O X X X X X X O O O O This instance agrees This instance out Consesus Vote: X X X X X X O O X X X X X X X X This instance agrees This instance out 16 Database Mgmt. &
17 Purchasing Decisions Model Building.! Some preliminary results on Purchasing Decisions Model. % Response 17 Database Mgmt. &
18 Purchasing Decisions Model Building. % Captured Response 18 Database Mgmt. &
19 Purchasing Decisions Model Building. Ensemble Model Results 19 Database Mgmt. &
20 Purchasing Decisions Model Building. Data Filtering Method Results 20 Database Mgmt. &
21 Summary! A Purchasing Decisions Model can be useful in Marketing and Campaign programs.! Both Customer and Prospect data analysis can be scored with this model and used in subsequent analysis such as segmentation.! Data Filtering can be used as an Ensemble method to discard instances which are misclassified.! More work on data filtering methods will be investigated for noisy data analysis. 21 Database Mgmt. &
22 References! [1] Pyle, Dorian, Data Preparation for Data Mining, Morgan Kaufman,, San Francisco, 1999.! [2] Brodley,, Carla E. and Friedl,, Mark A., Identifying Mislabeled Training Data, Jou.. of AI Research, vol.. 11, 1999, pp ! [3] Opitz,, David, and Maclin,, Richard, Popular Ensemble Methods: An Empirical Study, Jou.. of AI Research, vol.. 11, 1999, pp Database Mgmt. &
23 Acknowledgements! I would like to thank my cohort who worked on the data cleansing method and implementation in SAS Enterprise Miner.! I would also like to thank Mr.. Scott Berg and Victor Howard for encouraging me to submit this work.! Mr.. William Sommerfeld and Janice Shineman for inviting me to present this work at SUGI25.! Lastly, I would like to thank my supervisor, Mr. Gary Alen who always gives continued support in this area. 23 Database Mgmt. &
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