Abstract:
The Consumer Buying Behaviour consists of a summation of attitudes, preferences,
intentions and, decisions taken by them. The process that customer buys a product
or service varies for each individual and each category of products they may
purchase. With the development of Information Technology, the products and the
behaviour of purchasing those products have drastically changed and become more
unique to individuals. With respect to these changes, the data collection and analysis
have become more dynamic and customer data has become larger and nosier in terms
of volume and complexity. As a result of that, handling, analysing, and interpreting
customer Point of Sale (POS) data has become a challenge for Retail Supply Chains
(RSC) who wish to segregate customers into specific niche markets. Furthermore, it
makes increasingly difficult for the retailer to find out when a person comes and buys
the products from their outlets and to predict his/her behaviour for the subsequent
purchases. As a solution for the aforementioned problems faced by the retailers, a
novel a consumer buying behaviour profile mechanism is proposed. The profiles are
created with respect to the frequency, time-stamp, and product category using a large
POS dataset. The Unsupervised learning techniques were utilized in categorizing
consumers in determining similar purchasing behaviour using K-means, Expectation
Maximization, and Hierarchical Agglomerative Clustering (HAC). Along with the
above clustering techniques, text mining techniques were used in categorizing the
product descriptions to create the desired product categories. The study has used data
from the UCI machine learning repository with 541,909 POS type records and has
applied the aforementioned unsupervised learning techniques to setup the profiles. It
has unveiled product related and non-product related charateristics for the given POS
data and has laid a novel foundation to construct the profiles to determine buying
behaviour. Furthermore, these profiles can be used in segmentation of consumers,
RSC specific promotions, and to predict future possibilities to minimize inventory
related problems.
