Capturing multi-stakeholder needs in Customer-Centric Cloud Service Design

Cloud computing applications and services go hand in hand, yet there is no clear mechanism for ensuring that the cloud applications are designed from a customer’s perspective. Likewise services can require adaptation for multiple customers of stakeholders, which require differing user experience outcomes. This paper describes the initial design and development of a predictive analytics cloud service application, which uses historic customer data to predict the existing customers that are most likely to churn. Service blueprinting, a service innovation method, was used as the underlying design model for developing an initial shared understanding of the required service. Personas were used in the requirements analysis to develop insights into multi-stakeholder needs. Using the design science paradigm an extended cloud service design theory is proposed, as an outcome of the ongoing development of this analytics platform

Towards Design Principles for Data-Driven Decision Making: An Action Design Research Project in the Maritime Industry

Data-driven decision making (DDD) refers to organizational decision-making practices that emphasize the use of data and statistical analysis instead of relying on human judgment only. Various empirical studies provide evidence for the value of DDD, both on individual decision maker level and the organizational level. Yet, the path from data to value is not always an easy one and various organizational and psychological factors mediate and moderate the translation of data-driven insights into better decisions and, subsequently, effective business actions. The current body of academic literature on DDD lacks prescriptive knowledge on how to successfully employ DDD in complex organizational settings. Against this background, this paper reports on an action design research study aimed at designing and implementing IT artifacts for DDD at one of the largest ship engine manufacturers in the world. Our main contribution is a set of design principles highlighting, besides decision quality, the importance of model comprehensibility, domain knowledge, and actionability of results

Modeling Attrition in Organizations from Email Communication

Abstract—Modeling people’s online behavior in relation to their real-world social context is an interesting and important research problem. In this paper, we present our preliminary study of attrition behavior in real-world organizations based on two online datasets: a dataset from a small startup (40+ users) and a dataset from one large US company (3600+ users). The small startup dataset is collected using our privacy-preserving data logging tool, which removes personal identifiable information from content data and extracts only aggregated statistics such as word frequency counts and sentiment features. The privacy-preserving measures have enabled us to recruit participants to support this study. Correlation analysis over the startup dataset has shown that statistically there is often a change point in people’s online behavior, and data exhibits weak trends that may be manifestation of real-world attrition. Same findings are also verified in the large company dataset. Furthermore, we have trained a classifier to predict real-world attrition with a moderate accuracy of 60-65 % on the large company dataset. Given the incompleteness and noisy nature of data, the accuracy is encouraging. I

Loyalty Card Membership Challenge: A Study on Membership Churn and their Spending Behaviour

Understand member spending behaviour and their loyalty is important in all industries. By gaining loyalty from customers and understand how they spend, companies are able to retain their customers, increase their revenue and plan their marketing strategy to continue grow their business in a competitive business ecosystem. This research investigates member spending behaviour and membership churn for a loyalty card company in Malaysia. This research conducts exploratory analysis on three key partners registered with the company to understand their outlets’ spending activities and patterns. Meanwhile, this research also model membership churn based on the last 24 months membership data to identify factors that influence membership churn so that effective strategy can be formulated to retain active members in the company

Antecedents of ESG-Related Corporate Misconduct: Theoretical Considerations and Machine Learning Applications

The core objective of this cumulative dissertation is to generate new insights in the occurrence and prediction of unethical firm behavior disclosure. The first two papers investigate predictors and antecedents of (severe) unethical firm behavior disclosure. The third paper addresses frequently occurring methodological issues when applying machine learning approaches within marketing research. Hence, the three papers of this dissertation contribute to two recent topics within the field of marketing: First, marketing research has already focused intensively on the consequences of corporate misconduct and the accompanying media coverage. Meanwhile, the prediction and the process of occurrence of such threatening events have been examined only sporadically so far. Second, companies and researchers are increasingly implementing machine learning as a methodology to solve marketing-specific tasks. In this context, the users of machine learning methods often face methodological challenges, for which this dissertation reviews possible solutions. Specifically, in study 1, machine learning algorithms are used to predict the future occurrence of severe threatening news coverage of corporate misconduct. Study 2 identifies relationships between the specific competitive situation of a company within its industry and unethical firm behavior disclosure. Study 3 addresses machine learning-based issues for marketing researchers and presents possible solutions by reviewing the computer science literature

Predicting early user churn in a public digital weight loss intervention

Digital health interventions (DHIs) offer promising solutions to the rising global challenges of noncommunicable diseases by promoting behavior change, improving health outcomes, and reducing healthcare costs. However, high churn rates are a concern with DHIs, with many users disengaging before achieving desired outcomes. Churn prediction can help DHI providers identify and retain at-risk users, enhancing the efficacy of DHIs. We analyzed churn prediction models for a weight loss app using various machine learning algorithms on data from 1,283 users and 310,845 event logs. The best-performing model, a random forest model that only used daily login counts, achieved an F1 score of 0.87 on day 7 and identified an average of 93% of churned users during the week-long trial. Notably, higher-dimensional models performed better at low false positive rate thresholds. Our findings suggest that user churn can be forecasted using engagement data, aiding in timely personalized strategies and better health results

A conceptual framework for the direct marketing process using business intelligence

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Direct marketing is becoming a key strategy for organisations to develop and maintain strong customer relationships. This method targets specific customers with personalised advertising and promotional campaigns in order to help organisations increase campaign responses and to get a higher return on their investments. There are, however, many issues related to direct marketing, ranging from the highly technical to the more organisational and managerial aspects. This research focuses on the organisational and managerial issues of the direct marketing process and investigates the stages, activities and technologies required to effectively execute direct marketing. The direct marketing process integrates a complex collection of marketing concepts and business analytics principles, which form an entirely ‘self-contained’ choice for organisations. This makes direct marketing a significantly difficult process to perform. As a result, many scholars have attempted to tackle the complexity of executing the direct marketing process. However, most of their research efforts did not consider an integrated information system platform capable of effectively supporting the direct marketing process. This research attempts to address the above issues by developing a conceptual framework for the Direct Marketing Process with Business Intelligence (DMP-BI). The conceptual framework is developed using the identified marketing concepts and business analytics principles for the direct marketing process. It also proposes Business Intelligence (BI) as an integrated information system platform to effectively execute the direct marketing process. In order to evaluate and illustrate the practicality and impact of the DMP-BI framework, this thesis adopts a case study approach. Three case studies have been carried out in different industries including retailing, telecommunication and higher education. The aim of the case studies is also to demonstrate the usage of the DMP-BI framework within an organisational context. Based on the case studies’ findings, this thesis compares the DMP-BI framework with existing rival methodologies. The comparisons provide clear indications of the DMP-BI framework’s benefits over existing rival methodologies