The informational role of average rating and variance of customer ratings in the differential patterns of consumer behavior

Customer retention management: A complementary use of data mining and soft systems methodology

BACKGROUND: Customer retention and management of customer churn are deemed as among the most significant issues for businesses. Given the fact that customer churn is not typically predictable easily, identifying and analyzing customer churn is necessary for businesses. OBJECTIVE: Therefore, the current research was conducted to employ a complementary approach to identify the reasons influencing customer churn. METHODS: To do so, initially, customers’ data were clustered by recruiting the K-means method. Each cluster represented customers who held similar values and the probability of churn behavior. In the next step, stakeholder groups are identified based on the K-means algorithm. Then, Soft Systems Methodology (SSM) was employed to encapsulate each of the identified interested groups’ world-view to better understand logical reasons for churned customers. Purposeful activity modeling (human activity system) was adopted for each interested group utilizing SSM techniques. RESULTS: Using SSM techniques, purposeful activity modeling (human activity system) for each interested group adopted. Utilizing human activity systems for structuring debate sessions about change actions, short-term and long-term plans have been proposed to maintain and improve customer retention programs. CONCLUSIONS: SSM can be considered as an overarching approach that can afford a better understanding of the processes derived from data mining.

Too obvious to ignore: Influence of popular reviews on consumer online purchasing decisions

BACKGROUND: Online reviews, as an important way for consumers to understand product information, have an important impact on consumers’ online shopping decisions. A lot of useful explorations have been made on the role of online reviews in existing empirical research, but the interaction between online reviews and its subdivided dimensions have not been explored. OBJECTIVE: Based on the two-step flow theory, this article aims to explore the impact of online review valence, review volume, and their interactions on online sales, focusing on the question of what are the factors that influence customer purchase decisions and what is the moderating effect of popular reviews on review valence. METHODS: Empirical analysis was done by tracking the product information and online sales data of mobile phone products and laptops in search goods category on the Amazon.cn website. RESULTS/CONCLUSION: The research results found that: (1) in terms of review valence, the average score significantly promotes online sales, and negative word-of-mouth significantly decreases online sales; (2) as for review volume, the number of total reviews and popular reviews have significantly promote online sales; (3) regarding the interactions between the review valence and review volume, popular reviews significantly enhance the impact of review valence on online sales, playing a complementary effect for review valence.