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Data Mining BS/MS Project
Clustering for
Market Segmentation
Presentation by Mike Calder
Clustering
• Used for market segmentation
– Researchers want to find groups that can be
targeted with the same marketing strategy
• Given data of which users click on certain
adds, derive discriminative clusters
Strategy seen in use for almost 2 decades!
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Motivation
• Marketing companies want to produce as
few ads as possible while tailoring to the
largest possible audiences
• Search engines already collect enough
statistics to make these derivations
– No extra methods needed to obtain data
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Sample Click Data from Yahoo!
Taken from (Haider, 2012)
Represents the volume of advertisement clicks
on Yahoo! (different colors indicate categories)
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How Can We Use The Data?
• Sample Data Attributes
– Time advertisement was clicked on
– Location of the click on the page
– Category the advertisement falls into
– Type of marketing strategy used in the ad
• Must decide on a clustering algorithm and
a number of clusters to use
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Clustering Method Options
• Algorithms
– K-means
– Hierarchal
– Centroid-based
– Distribution-based
– Novel combinations of the above
• Attempting to maximize “log-likelihood”.
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Sample Algorithm Testing
Taken from (Haider, 2012)
Novel algorithm details are described in
Discriminative Clustering for Market Segmentation
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References
• P. Haider. “Discriminative Clustering for Market
Segmentation”. in Proceedings of the 18th ACM SIGKDD
International Conference on Knowledge Discovery and
Data Mining (KDD ’12). New York, NY, USA: ACM, pp.
417–425. 2012.
• S. Dolnicar. “Using cluster analysis for market
segmentation”. Australian Journal of Market Research,
11(2), 5-12. 2003.
• F. Pratter. “Clustering for Market Segmentation”. Abt
Associates Inc.. 1997.
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