In this article, we will be reviewing a research paper, titled: ‘Customer segmentation based on buying and returning behaviour’. The focus of this research paper is based on two aspects, which are: To determine if a general-purpose strategy conforms to the clothing businesses that involve e-commerce To properly assess for certain if customer rates of…… Continue reading Customer Segmentation Based on Buying and Returning Behaviour
In this article, we will be reviewing a research paper titled: ‘Intelligent Value-Based Customer Segmentation Method for Campaign Management: A Case Study of Automobile Retailer’ (2008). The research proposes an approach that blends consumer targeting and customer segmentation for an effective marketing strategy that targets the right kind of consumers. The research paper is authored…… Continue reading Intelligent Value Based Customer Segmentation: A Case Study of Automobile Retailer — Summary
In this post, we review the customer segmentation research article, “Customer segmentation issues and strategies for an automobile dealership with two clustering techniques” (2015) by authors from Taiwan: Chih-Fong Tsai from National Central University, Ya-Han Hu from National Chung Cheng University, and Yu-Hsin Lu from Feng Chia University. The Business Case for Segmentation The authors…… Continue reading Customer Segmentation Issues And Strategies For An Automobile Dealership With Two Clustering Techniques – A Summary
Customer segmenting is the process of dividing a group of people into homogeneous subgroups that differ from other subgroups, typically based on behaviors and demographics, grounded on some product, brand, advertisement, or content, with many factors affecting product engagement by customer. The identification of customer segments has been important in marketing and advertising for some…… Continue reading Why is segmentation analytics important?
In this research, my co-researchers and I propose a novel approach for isolating customer segments using online customer data for products that are distributed via online social media platforms. We use non-negative matrix factorization to ﬁrst identify behavioral customer segments and then to identify demographic customer segments. We employ a methodology for linking the two…… Continue reading Customer segmentation using online platforms: isolating behavioral and demographic segments for persona creation via aggregated user data
Using 27 million flight bookings for 2 years from a major international airline company, we built a Next Likely Destination model to ascertain customers’ next flight booking. The resulting model achieves an 89% predictive accuracy using historical data. A unique aspect of the model is the incorporation of self-competence, where the model defers when it…… Continue reading Forecasting the Nearly Unforecastable: Why Aren’t Airline Bookings Adhering to the Prediction Algorithm?
We develop a framework to reduce the number of customer segments to the smallest quantity without losing essential information of the underlying population in the electronic marketplace. We evaluate our approach in a case study using more than 21 million online flight bookings of a major airline company resulting in a 57.5% decrease from 1194…… Continue reading Too few, too many, just right: Creating the necessary number of segments for large online customer populations