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This study investigates the role of individual differences in channel choice and switching behavior in a multichannel environment using latent class analysis on data from 1512 customers. Psychographic variables from five domains (risk attitudes, cognitive ability, motivation, personality, and decision-making style) serve as covariates for multichannel customer behavior. We identify six segments that differ significantly on six psychographic variables (readiness to take risks, need for cognition, autotelic and instrumental need for touch, and rational and intuitive decision-making styles). The results advance the theory-building of multichannel customer behavior and present insights for proactively managing customer journeys of distinct segments.
Toward a notation for modeling value driver trees: Classification development and research agenda
(2024)
In-depth analysis of customer journeys to broaden the understanding of customer behaviors and expectations in order to improve the customer experience is considered highly relevant in modern business practices. Recent studies predominantly focus on retrospective analysis of customer data, whereas more forward-directed concepts, namely predictions, are rarely addressed. Additionally, the integration of robotic process automation (RPA) to potentially increase the efficiency of customer journey analysis is not discussed in the current field of research. To fill this research gap, this paper introduces “customer journey mining”. Process mining techniques are applied to leverage digital customer data for accurate prediction of customer movements through individual journeys, creating valuable insights for improving the customer experience. Striving for improved efficiency, the potential interplay of RPA and customer journey mining is examined accordingly. The research methodology followed is based on a design science research process. An initially defined customer journey mining artifact is operationalized through an illustrative case study. This operationalization is achieved by analyzing a log file of an online travel agency functioning as an orientation for researchers and practitioners while also evaluating the initially defined framework. The data is used to train seven distinct prediction models to forecast the touchpoint a customer is most likely to visit next. Gradient-boosted trees yield the highest prediction accuracy with 43.1%. The findings further indicate technical suitability for RPA implementation, while financial viability is unlikely.
Against the setting of an increasing need for innovation and low margins, companies in the logistics
sector are facing highly competitive pressure. One field with high potential for optimization lies within
damage quotas. The use of big data analytics or data mining represents a promising approach to face
this challenge. However, within supply chain management, data mining is hardly being researched on
regarding damage quotas and thus not being utilized to its full possible extend. At the current time it
seems to predominantly be used for route and utilization optimization while the analysis of delivery
damages is hardly considered.
The aim of this research is therefore to showcase an initial approach for data mining in logistics to predict
delivery damage probabilities and to validate this by means of a multiple case study research. To create
a sound basis for evaluation, the groundwork is laid out based on CRISP-DM by the analysis of reference
data (German road-cargo market).
As a central result it is noted that data mining can systematically be used to help reducing the damages
by forecasting the probabilities of damages occurring during transport in dependence of different factors.
The approach can be utilized across different markets as long as sufficient data tracking delivery
damages is being collected within a company. Challenges arise in the field of air- and sea-freight.
The urge for personalisation and the rise of technological advancements
in the 21st century is pushing for more innovative marketing strategies. As such, this dissertation examines the impact of personality-tailored
campaigns (PTC) and how it affects purchasing decisions among
Generation Z, focusing on theoretical and practical implications.
A conceptual framework for the process of personality-tailored marketing has been developed to provide tangible value for businesses of
various industries in particular the fragrance, smartphone, and food
industry.