The Research of Classifying Customers and Building New Product Forecasting Model Based on Product Attributes
Date Issued
2009
Date
2009
Author(s)
Lai, Yu-Ching
Abstract
When companies deal with new product marketing decisions, they usually analyze the historical market data of similar products in order to conduct sales forecast. Because each product is different from one another, there would be an obvious gap between forecast and reality. Furthermore, how to access different customer clusters through various marketing implications is one of the main concerns of companies. This research regards the customer database of TV-Commerce Company E as the analyzed subject. In place of product categories and functions, we conduct product classification by product attributes on which customers regard when making purchasing decisions. This research also clusters customers into several groups according to their emphasis on different product attributes. This research attaches the label of product attributes to 168 products by Content Analysis , and then generalized six categories as the purchasing factors by Logistic Regression. The core customers (top 20%) in TV-Commerce Company E`s database can be classified into 5 clusters by Binary Logistic Regression. It reveals high relevance between product attributes and customer clusters. his research succeeds in building up a New Product Forecasting Model (NPFM). This model could help companies to find out proper match between products and customers, rise up the marketing efficiency.
Subjects
product attribute
customer classification
Content Analysis
Logistic Analysis
new product forecasting model
Type
thesis
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