Knowledge Discovery in Databases to Support
Business Decision in Customer Relationship Management
Student Name: Manepalli P.K.V.Subba Rao
This was a comprehensive case
study in data mining from data warehouses containing data on demographics and
purchase patterns of``members'' (card holders) of a non-grocery business
with nation-wide chain of retail outlets for better customer relationship
management. In particular, the main business objectives were Customer
Segmentation, Customer Profiling and Product Affinity analysis for generating
ideas for cross selling and general understanding of customer preferences.
Customer segmentation was done with respect to customer demographics, purchase
patterns (after defining appropriate stochastic processes) and purchase
interests using an artificial neural network with unsupervised learning
involving Kohonen's feature map. Customer Profiling was done using a
feed-forward neural network with customer demographics as the inputs and four
``purchase pattern'' segments, developed in the previous step, as the output.
Product Affinity analysis was performed by computing various joint, conditional
and ratios of probabilities (which are respectively called the support,
confidence and lift in data mining jargon) of sale of various products and
brands and some interesting ``rules'' regarding product$\rightarrow$product,
product$\rightarrow$brand, brand$\rightarrow$product and
brand$\rightarrow$ brand were discovered. These discovered Product Affinity
rules and the earlier Customer Segmentation and Customer Profiling results were
then leveraged to generate Cross Selling ideas, apart from providing some
specific recommendations at strategic, tactical and operational level.