Customer profiling and segmentation of starbucks Malaysia: Empirical investigation during CMCO 2.0

• Main Author: Kamaruzzaman, Zetty Ain
• Format: Conference or Workshop Item
• Language: English; English
• Published: AIP Publishing 2022
• Subjects: H Social Sciences (General); HA Statistics; Q Science (General)
• Online Access: http://umpir.ump.edu.my/id/eprint/36151/1/5.0092729.pdf; http://umpir.ump.edu.my/id/eprint/36151/7/Customer%20profiling%20and%20segmentation%20of%20starbucks%20Malaysia.pdf; http://umpir.ump.edu.my/id/eprint/36151/; https://doi.org/10.1063/5.0092729

The economic uncertainties due to Covid-19 pandemic has forced businesses to survive, maintain their long-term profitability and remain competitive through the unexpected market upsets. As businesses have to shut down, jobs are lost, people will suffer and economy will be shrinking. Thus, understanding the purchasing behavior of customer is a vital step not only in building but also in maintaining a business. Good customer management comes from good customer measurement. With the latest advent of customer analytics, businesses nowadays can thoroughly comprehend their consumers at all phases of the purchasing process, recognizing patterns in customer data, forecasting the actions that their customers will do, and then making decisions about how to enhance their business in order to attract new customers and retain existing ones. The objective of this paper is to perform customer profiling and customer segmentation of Starbucks Malaysia during the hit of Covid-19 pandemic in Malaysia. Dataset are collected during the second Conditional Movement Control Order (CMCO 2.0). Customer profiling tries to gain a deeper understanding of customers and describe their personalities types or personas, while customer segmentation is a powerful technique to understand the patterns that differentiate a customer. A customer segment is a grouping of customers that share certain characteristics. In this paper, k-means clustering algorithm is used to segment the customers of Starbucks Malaysia according to their income and spend data. From the clustering analysis, one can determine the optimal number of clusters and comprehend the underlying customer segments to identify the statistical patterns of Starbucks customers. This research will contribute both theoretically where this research can uplift the theoretical foundation of customer analytics and practically towards Starbucks Malaysia and other organization and marketing teams where they can understand their customers better and can increase their revenue with the improved marketing campaigns especially during this long-run Covid-19 crisis.