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"Personalisierung"
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Self-learning Recommendation System Using Reinforcement Learning
Originality/Value: This study offers a novel combination of customer segmentation, contextual analysis, and reinforcement learning. It demonstrates that this integrated approach can significantly improve recommendation system efficiency, thereby making a valuable contribution to the field of marketing strategies and customer-focused recommendations.
Journal Article
Personalising learning in open-plan schools
\"How can widely acknowledged challenges facing regional secondary schools with high concentrations of low SES students, ineffectual curricula, and poor levels of student engagement, attendance, and wellbeing, be addressed? In this book we report on key outcomes of the Bendigo Education Plan that aimed to improve the academic attainment and wellbeing of 3000 regional secondary students. This Plan entailed rebuilding four Years 7-10 colleges, and developing a differentiated and personalised curriculum, with teachers team-teaching in open-plan settings. We analyse how and why teachers and students adapted to these new practices. We focus on both generic changes in the schools, around the use of ICTs and the organisation of the curriculum, and on specific approaches to teaching and learning in English, mathematics, science, social studies and studio arts. This book provides research-based guidelines on how the curriculum can be renewed and enacted effectively in these and like schools. In analysing a large-scale attempt to address the challenge of making learning personalised and meaningful for this cohort of students, our book addresses larger questions about quality secondary curriculum and successful teacher professional learning support.\"
Book
Online Behavioral Advertising: A Literature Review and Research Agenda
Advertisers are increasingly monitoring people's online behavior and using the information collected to show people individually targeted advertisements. This phenomenon is called online behavioral advertising (OBA). Although advertisers can benefit from OBA, the practice also raises concerns about privacy. Therefore, OBA has received much attention from advertisers, consumers, policymakers, and scholars. Despite this attention, there is neither a strong definition of OBA nor a clear accumulation of empirical findings. This article defines OBA and provides an overview of the empirical findings by developing a framework that identifies and integrates all factors that can explain consumer responses toward OBA. The framework suggests that the outcomes of OBA are dependent on advertiser-controlled factors (e.g., the level of personalization) and consumer-controlled factors (e.g., knowledge and perceptions about OBA and individual characteristics). The article also overviews the theoretical positioning of OBA by placing the theories that are used to explain consumers' responses to OBA in our framework. Finally, we develop a research agenda and discuss implications for policymakers and advertisers.
Journal Article
Smart Generation System of Personalized Advertising Copy and Its Application to Advertising Practice and Research
Artificial intelligence in programmatic advertising constitutes fertile grounds for marketing communication with tremendous opportunities. Yet, despite its touted benefits, contemporary implementations of programmatic advertising do not harness self-generative technologies so much so that different consumers are exposed to identical content. Consequently, we advance a smart generation system of personalized advertising copy (SGS-PAC) that can automatically personalize advertising content to align with the needs of individual consumers. Analytical results from a user experiment involving about 80 subjects underscore that personalized advertising copies generated by SGS-PAC can bolster click rate in online advertising platforms. Findings from this study bear significant implications for the application of artificial intelligence in online advertising.
Journal Article
Making Recommendations More Effective Through Framings
Companies frequently offer product recommendations to customers, according to various algorithms. This research explores how companies should frame the methods they use to derive their recommendations, in an attempt to maximize click-through rates. Two common framings—user-based and item-based—might describe the same recommendation. User-based framing emphasizes the similarity between customers (e.g., “People who like this also like…”); item-based framing instead emphasizes similarities between products (e.g., “Similar to this item”). Six experiments, including two field experiments within a mobile app, show that framing the same recommendation as user-based (vs. item-based) can increase recommendation click-through rates. The findings suggest that user-based (vs. item-based) framing informs customers that the recommendation is based on not just product matching but also taste matching with other customers. Three theoretically derived and practically relevant boundary conditions related to the recommendation recipient, the products, and other users also offer practical guidance for managers regarding how to leverage recommendation framings to increase recommendation click-throughs.
Journal Article
Developing Personalized Education
Personalized education—the systematic adaptation of instruction to individual learners—has been a long-striven goal. We review research on personalized education that has been conducted in the laboratory, in the classroom, and in digital learning environments. Across all learning environments, we find that personalization is most successful when relevant learner characteristics are measured repeatedly during the learning process and when these data are used to adapt instruction in a systematic way. Building on these observations, we propose a novel, dynamic framework of personalization that conceptualizes learners as dynamic entities that change during and in interaction with the instructional process. As these dynamics manifest on different timescales, so do the opportunities for instructional adaptations—ranging from setting appropriate learning goals at the macroscale to reacting to affective-motivational fluctuations at the microscale. We argue that instructional design needs to take these dynamics into account in order to adapt to a specific learner at a specific point in time. Finally, we provide some examples of successful, dynamic adaptations and discuss future directions that arise from a dynamic conceptualization of personalization.
Journal Article
How consumer digital signals are reshaping the customer journey
Marketers are adopting increasingly sophisticated ways to engage with customers throughout their journeys. We extend prior perspectives on the customer journey by introducing the role of digital signals that consumers emit throughout their activities. We argue that the ability to detect and act on consumer digital signals is a source of competitive advantage for firms. Technology enables firms to collect, interpret, and act on these signals to better manage the customer journey. While some consumers’ desire for privacy can restrict the opportunities technology provides marketers, other consumers’ desire for personalization can encourage the use of technology to inform marketing efforts. We posit that this difference in consumers’ willingness to emit observable signals may hinge on the strength of their relationship with the firm. We next discuss factors that may shift consumer preferences and consequently affect the technology-enabled opportunities available to firms. We conclude with a research agenda that focuses on consumers, firms, and regulators.
Journal Article
The Importance of Trust for Personalized Online Advertising
•Personalization depth and breadth define a retargeting banner's ad personalization.•Trust in the retailer moderates the impact of ad personalization on consumers.•For more trusted retailers, high-depth/narrow-breadth banners increase usefulness.•For less trusted firms, high-depth ads always elicit reactance and privacy concerns.•Reactance and privacy concerns are distinct negative responses to personalized ads.
With the amount of online advertising on a steady rise, generic ads noticeably lose effectiveness. In order to break through the clutter, retailers employ a method called retargeting to tailor their advertisements to individual consumers based on inferred interests and preferences. However, while personalization should generally make ads more appealing, the authors use field data to show that the effectiveness of retargeting considerably hinges on consumers’ trust in a respective retailer. To uncover the underlying mechanisms of this phenomenon, they investigate how trust moderates the impact of ad personalization on consumers’ internal and external responses in the lab. They propose a two-dimensional conceptualization of ad personalization: First, a banner's personalization depth defines how closely the ad reflects a consumer's interests. Second, its personalization breadth determines how completely the banner reflects these interests. The lab results show that more trusted retailers can increase the perceived usefulness of their ads through a combination of high depth and narrow breadth of personalization without eliciting increased reactance or privacy concerns. On the other hand, for less trusted retailers, banners with higher depth are not perceived more useful, but instead trigger increased reactance and privacy concerns, regardless of their personalization breadth. These effects directly translate into consumers’ click-through intentions so that retailers should adjust their personalization strategies accordingly in order to increase the effectiveness of their online advertising.
Journal Article