The dark side of CRM: advantaged and disadvantaged customers

Purpose – CRM treats various profiles of customers or individual customers differently, purposively favoring certain customers while deliberately disadvantaging others. This research aims to provide insights into how advantaged (favored) and (non-favored) disadvantaged customers perceive fairness in retailers’ marketing tactics. Design/methodology/approach – A multiple study approach has been adopted, influenced by a three-stage process, which involved exploratory interviews, pilot tests, and the main survey. Findings – The results have provided marketers with a perspective on maintaining and enhancing relationships. Service and marketing communications concern the advantaged customers most, while pricing is the most important aspect for the disadvantaged customers. Practical implications – In terms of handling customers, there are important implications from recognizing how those who are favored and those who are not so advantaged perceive their treatment. Failure to appreciate the pitfalls for visibly treating certain customers more favorably and others demonstrably less so, will have stark consequences for retail management and consumer marketing. Originality/value – Contributions are made to the literatures on CRM and on unfairness, particularly in terms of how to address the inevitable inequities inherent in retailers’ CRM offerings. Identification of the advantaged and disadvantaged customers and their respective views allows marketers to develop more appropriate approaches for handling customers who are sensitive to perceived unfairness.

Why your customers’ social identities matter

The article looks at the role of consumers' social identities in their purchasing decisions, and hence in the creation of effective marketing strategies. It says that people generally belong to multiple social groups, any one of which may have the most salience for them in a given situation. It reports on social psychology research on how a person's connection with a particular social identity can be triggered and discusses the idea in the context of marketing products including the Toyota Prius hybrid-electric automobile, Nescafé instant coffee, and the Jeep all-terrain vehicle. INSET: Lessons of the Stanford Prison Experiment.

Effect of pair interactions on transition probabilities between inactive and active states: achieving collective behaviour via pair interactions in social insects

To understand the evolution of well-organized social behaviour, we must first understand the mechanism by which collective behaviour establishes. In this study, the mechanisms of collective behaviour in a colony of social insects were studied in terms of the transition probability between active and inactive states, which is linked to mutual interactions. The active and inactive states of the social insects were statistically extracted from the velocity profiles. From the duration distributions of the two states, we found that 1) the durations of active and inactive states follow an exponential law, and 2) pair interactions increase the transition probability from inactive to active states. The regulation of the transition probability by paired interactions suggests that such interactions control the populations of active and inactive workers in the colony.

Analysis and clustering of residential customers energy behavioral demand using smart meter data

Clustering methods are increasingly being applied to residential smart meter data, providing a number of important opportunities for distribution network operators (DNOs) to manage and plan the low voltage networks. Clustering has a number of potential advantages for DNOs including, identifying suitable candidates for demand response and improving energy profile modelling. However, due to the high stochasticity and irregularity of household level demand, detailed analytics are required to define appropriate attributes to cluster. In this paper we present in-depth analysis of customer smart meter data to better understand peak demand and major sources of variability in their behaviour. We find four key time periods in which the data should be analysed and use this to form relevant attributes for our clustering. We present a finite mixture model based clustering where we discover 10 distinct behaviour groups describing customers based on their demand and their variability. Finally, using an existing bootstrapping technique we show that the clustering is reliable. To the authors knowledge this is the first time in the power systems literature that the sample robustness of the clustering has been tested.