Data-driven Personalization Research Articles

Marketing Strategy of Ocado: A Comprehensive Review of the UK's Leading Grocery Delivery Service

This review critically examines the marketing strategy of Ocado, a leading online grocery delivery service in the UK. Despite its technological innovations, such as AI-powered customer fulfillment centers and data-driven personalization, Ocado holds a modest 1.7% market share, compared to traditional competitors like Tesco and Sainsbury's. The paper explores key factors influencing Ocado's market performance, including its premium positioning, digital-first approach, and sustainability efforts. It also analyzes the company's reliance on affluent customers and the limitations posed by high operational costs. Through a thorough evaluation of Ocado's customer engagement, environmental initiatives, and competitive landscape, this review offers insights into how the company can expand its market presence by embracing emerging technologies like blockchain and AI while adopting hybrid marketing strategies to appeal to a broader customer base. Recommendations for future growth emphasize the need for competitive pricing, a wider product offering, and enhanced personalization strategies.

Data-Driven Personalization : Revolutionizing User Experience

Data-driven personalization has emerged as a transformative approach in digital user experience design, leveraging advanced analytics and machine learning to tailor content and interfaces to individual users. This article explores five key aspects of data-driven personalization: user behavior analysis, segmentation and targeting, machine learning algorithms, real-time adaptation, and privacy and ethical considerations. It examines the significant impact of personalization on business outcomes, including increased revenue and customer engagement, while also addressing implementation challenges, such as technological complexity and privacy concerns. The article provides insights into the methodologies, processes, and best practices for effective personalization, supported by industry statistics and case studies, offering a comprehensive overview of how organizations can harness this powerful approach to create more engaging, relevant, and effective digital experiences.

Is personalization of psychological pain treatments necessary? Evidence from a Bayesian variance ratio meta-analysis.

This is the first study to empirically determine the potential for data-driven personalization in the context of chronic primary pain (CPP). Effect sizes of psychological treatments for individuals with CPP are small to moderate on average. Aiming for better treatment outcomes for the individual patient, the call to personalize CPP treatment increased over time. However, empirical evidence that personalization of psychological treatments can optimize treatment outcomes in CPP is needed. This study seeks to estimate heterogeneity of treatment effect for cognitive behavioral therapy (CBT) as the psychological treatment approach for CPP with the greatest evidence base. For this purpose, a Bayesian variance ratio meta-regression is conducted using updated data from 2 recently published meta-analyses with randomized controlled trials comparing CBT delivered face-to-face to treatment-as-usual or waiting list controls. Heterogeneity in patients with CPP would be reflected by a larger overall variance in the post-treatment score compared with the control group. We found first evidence for an individual treatment effect in CBT compared with the control group. The estimate for the intercept was 0.06, indicating a 6% higher variance of end point values in the intervention groups. However, this result warrants careful consideration. Further research is needed to shed light on the heterogeneity of psychological treatment studies and thus to uncover the full potential of data-driven personalized psychotherapy for patients with CPP.A Bayesian variance ratio meta-regression indicates empirical evidence that data-driven personalized psychotherapy for patients with chronic primary pain could increase effects of cognitive behavioral therapy.

Data-Driven Personalization of Body–Machine Interfaces to Control Diverse Robot Types

Data-Driven Personalization of Body–Machine Interfaces to Control Diverse Robot Types

Strategies For Enhancing Zakat Fundraising Through The Utilization Of Social Media And Digital Campaigns

This study investigates strategies to enhance zakat fundraising through the utilization of social media and digital campaigns. An integrated approach is employed, focusing on data-driven personalization, engagement through interactivity, utilization of analytical tools, simplicity of messages, captivating narratives, responsiveness to changing trends, and integrated message dissemination. The introduction provides background on the context of zakat fundraising and the crucial role of zakat in social welfare. The problem statement highlights challenges in fundraising and the significance of effective strategies. The research objectives involve analyzing social media as a zakat fundraising tool and evaluating the effectiveness of digital campaigns. The research methodology includes a literature review and case studies. The research findings indicate that the integrated approach has a positive impact, with personalization and interactivity being key factors. Emphasis is placed on the necessity of responsiveness to trends and integrated message dissemination to enhance awareness and community participation in zakat fundraising.

Challenges and Opportunities of Novel Retail Paradigms in the Contemporary Market Environment in China

Chinese customers now interact, shop, and engage with companies in whole new ways as a result of the confluence of the Internet, mobile technology, and social media. New retail formats have emerged in this digitally empowered era, altering the shopping experience and going beyond the constraints of conventional brick-and-mortar stores. This paper examines the challenges and opportunities presented by novel retail paradigms in China’s contemporary market landscape, with a focus on TikTok’s innovative social media online shopping model. Through an in-depth analysis of TikTok’s case, the study highlights the transformative impact of short-form videos, influencer-driven marketing, and data-driven personalization in reshaping consumer engagement with products. The paper anticipates future trends such as augmented reality integration and hyper-personalization while providing strategic recommendations encompassing social commerce integration, predictive supply chain management, regulatory adaptability, and fostering an innovation culture. Ultimately, the study emphasizes the need for businesses to embrace innovative approaches and adapt to evolving consumer preferences to thrive in an ever-changing retail environment.

Congruency of multimodal data-driven personalization with shared decision-making for StayFine: individualized app-based relapse prevention for anxiety and depression in young people.

Tailoring interventions to the individual has been hypothesized to improve treatment efficacy. Personalization of target-specific underlying mechanisms might improve treatment effects as well as adherence. Data-driven personalization of treatment, however, is still in its infancy, especially concerning the integration of multiple sources of data-driven advice with shared decision-making. This study describes an innovative type of data-driven personalization in the context of StayFine, a guided app-based relapse prevention intervention for 13- to 21-year-olds in remission of anxiety or depressive disorders (n = 74). Participants receive six modules, of which three are chosen from five optional modules. Optional modules are Enhancing Positive Affect, Behavioral Activation, Exposure, Sleep, and Wellness. All participants receive Psycho-Education, Cognitive Restructuring, and a Relapse Prevention Plan. The personalization approach is based on four sources: (1) prior diagnoses (diagnostic interview), (2) transdiagnostic psychological factors (online self-report questionnaires), (3) individual symptom networks (ecological momentary assessment, based on a two-week diary with six time points per day), and subsequently, (4) patient preference based on shared decision-making with a trained expert by experience. This study details and evaluates this innovative type of personalization approach, comparing the congruency of advised modules between the data-driven sources (1-3) with one another and with the chosen modules during the shared decision-making process (4). The results show that sources of data-driven personalization provide complementary advice rather than a confirmatory one. The indications of the modules Exposure and Behavioral Activation were mostly based on the diagnostic interview, Sleep on the questionnaires, and Enhancing Positive Affect on the network model. Shared decision-making showed a preference for modules improving positive concepts rather than combating negative ones, as an addition to the data-driven advice. Future studies need to test whether treatment outcomes and dropout rates are improved through personalization.

The after party: Cynical resignation in Adtech's pivot to privacy

Digital advertising and technology companies are resigned to a new privacy imperative. They are bracing for a world where third-party tracking will be restricted by design or by law. Digital resignation typically refers to how companies cultivate a sense of powerlessness about privacy among internet users. Our paper looks through this optic from the other end of the lens: How is the digital advertising industry coping with the increasing salience of privacy? Recent developments have forced companies to implement “privacy-preserving” designs—or at least promise some semblance of privacy. Yet, the industry remains dependent on flows of data and means of identification to enable still-desired targeting, measurement, and optimization. Our paper analyzes this contradiction by looking at systems that aim to replicate existing functionalities while protecting user “privacy.” We call this a form of “cynical resignation” and characterize its key maneuvers as follows: (a) sanitizing surveillance; (b) party-hopping; and (c) sabotage. We argue that this “cynical resignation” to a privacy imperative represents a policy failure. In the absence of decisive interventions into the underlying business models of data capitalism, companies offer techno-solutionism and self-regulations that seem to conform to new laws and norms while reinforcing commitments to data-driven personalization. This may benefit the largest tech companies, since their privileged access to first-party data will make more companies reliant on them, and their computational power will be even more valuable in a world where modeling is used to compensate for the loss of third-party data and traditional methods of personal identification.

1071-P: Impact of the GluCare Health Model of Care in Diabetics with NAFLD as Measured Using Shear Wave Ultrasound—A Three-Month Outcomes Study

Introduction: Type 2 diabetes (T2D) and obesity are risk factors for nonalcoholic fatty liver disease (NAFLD). Newer and non-invasive imaging techniques of transient elastography and shear wave elastography provide the opportunity to measure liver stiffness (1). Shear Wave Ultrasound (SWU) is a non-invasive imaging technique that has been increasingly used in the management of patients with diabetes (2). Continuous monitoring and engagement approach, such as the one utilized by GluCare.health, has been proven to effectively improve metabolic conditions associated with diabetes in trials (3). This integrated care model uses data-driven personalization of interventions to increase patient's access and engagement with health provider to surpass outcomes of traditional care methods (4). Methods: Retrospective review of medical records of 47 patients, evaluated at GluCare (Dubai, UAE), from 18 to 70 years old with T2D and body mass index (BMI) over 25. All the patients were under anti diabetic drugs. BMI and data from a SWU model FT100 were collected and compared between the first visit and after 3 months GluCare.health intervention. Correlations between 3-month changes in BMI, liver ultrasound attenuation parameter (UAP) and stiffness were explored using statistical analysis. Results: Average changes in BMI over the first three months of treatment were statistically significant (p<0.001). The average UAP decrease was significant at 10 % level (p=0.086) and every unit decreased in BMI was associated with 7.23 dB/m reduction in UAP (p<0.05). The average change in liver stiffness was not statistically significant (p>0.1). The analysis also found a significant correlation between changes in BMI and stiffness at the 5% significance level, with a moderately strong correlation (r=0.38). Conclusion: The study suggests that the GluCare.Health intervention may be effective in reducing BMI in patients with diabetes, and it may also have some effect on NAFLD. Disclosure M. Caccelli: None. Y. Said: None. S. Aleabova: None. C. Palsky: None. A. Hashemi: None. I. Almarzooqi: None.

Normative Considerations in Data-driven Personalization of Consumer Insurance Contracts

Normative Considerations in Data-driven Personalization of Consumer Insurance Contracts

Chatbots in Online Marketing: A work in progressNajlae Zhani

In the digital era, chatbots have emerged as a transformative tool in online marketing (Crolic et al. 2022). This abstract introduces an ongoing research endeavor that specifically explores the role of chatbots in online marketing strategies. The research is a work in progress, aimed at understanding and contributing to the dynamic field of chatbot marketing. In today's dynamic digital landscape, chatbots have gained prominence as a powerful element of online marketing (Nwachukwu and Affen, 2023). This abstract serves as a preliminary exploration of the role of chatbots and conveys that our research is in progress, intended to provide insights into their evolving significance in online marketing. Chatbots, AI-driven virtual conversational agents, are becoming ubiquitous in digital marketing. Our research in progress focuses on understanding how these chatbots engage with customers, answer queries, and facilitate personalized interactions. We explore how chatbots enhance customer experience and drive engagement on websites and messaging platforms. A key aspect of our research in progress is dedicated to conversational marketing, wherein chatbots play a pivotal role in guiding customers through their journey. We investigate how chatbots create a seamless conversational experience and the impact on lead generation and conversions. Our research explores how chatbots collect and analyze data to offer personalized recommendations and insights. We aim to understand how chatbots leverage data-driven personalization to enhance user experience and provide tailored solutions to customers. As part of our research in progress, we delve into the future of chatbots in marketing. We anticipate discussing emerging trends and potential challenges, such as the integration of chatbots with emerging technologies, including voice assistants and augmented reality. Methodology and Data Collection Our ongoing research employs a mixed-method approach, combining quantitative and qualitative data collection techniques. We conduct surveys, interviews, and user behavior analysis to gather insights into how chatbots are perceived and utilized by businesses and consumers. Implications for Businesses Our research aims to offer practical insights for businesses aiming to leverage chatbots in their marketing strategies. We discuss potential benefits and challenges, providing guidance on the optimal use of chatbots to drive brand engagement and customer satisfaction. In conclusion, this abstract represents an ongoing research initiative focusing on the pivotal role of chatbots in online marketing. By emphasizing that our research is a work in progress, we signal our commitment to contributing to the evolving field of chatbot marketing. As we continue our research journey, we anticipate providing valuable insights to assist businesses in harnessing the potential of chatbots for online marketing success.

Personalizing mathematical content in educational applets repository: human teacher versus machine-based considerations

Personalizing the use of educational mathematics applets to fit learners’ characteristics poses a great challenge. The present study adopted a unique approach by comparing personalization processes implemented by a machine to those implemented by a human teacher. Given the different affordances—the machine’s access to historical log file data, computation and automatization, and the teacher’s mathematical knowledge, pedagogical approach and personal acquaintance—the study hypothesized that different considerations would lead to different personalization and learning outcomes. Mathematical applets were assigned to 77 students in the 4th and 5th grades either by an expert teacher or by an algorithm. The assignment took place in a controlled setting in which the teacher was unaware which students were eventually assigned according to her recommendations. The teacher and the machine each recommended an ordered sequence of ten applets per student. The findings suggest that the teacher-assigned group outperformed the machine-assigned group among 5th graders when the applets were sequenced in increasing order of difficulty. In the 4th grade, only the machine recommended a sequence of increasing difficulty and both groups achieved equal performance. The study concludes that in the case of data-driven personalization processes, machines and teachers should learn from each other’s affordances and considerations.

The Pixelated Person – Humanity in the Grip of Algorithmic Personalisation

This is the introductory chapter to the edited collection on 'Data-Driven Personalization in Markets, Politics and Law' (CUP, 2021) that explores the emergent pervasive phenomenon of algorithmic prediction of human preferences, responses and likely behaviors in numerous social domains and subsequent ‘implementation’ - ranging from personalized advertising and political microtargeting to precision medicine, personalized pricing and predictive policing and sentencing. This chapter reflects on such human-focused use of predictive technology, first, by situating it within a general framework of profiling and defends data-driven individual and group profiling against some critiques of stereotyping, on the basis that our cognition of the external environment is necessarily reliant on relevant abstractions or non-universal generalizations. The second set of reflections centers around the philosophical tradition of empiricism as a basis of knowledge or truth production, and uses this tradition to critique data-driven profiling and personalization practices in its numerous manifestations.

Technology-enabled personalization in retail stores: Understanding drivers and barriers

Smart technologies grant brick-and-mortar retailers novel opportunities to introduce the amenities of online retailing, such as data-driven personalization, into physical interactions. Research on consumer reactions to the novel phenomenon of technology-enabled personalization (TEP) in retail stores is scarce though, so the current article proposes a conceptualization that demarcates TEP from broader notions of personalization. Qualitative data from 25 in-depth consumer interviews reveal five drivers (utilitarian, hedonic, control, interaction, integration) of and four barriers (exploitation, interaction misfit, privacy, and lack of confidence) to consumers’ acceptance of TEP. The juxtaposition of these drivers and barriers, in combination with insights from prior literature, reveals five success paradoxes for TEP (exploration–limitation, staff presence–absence, humanization–dehumanization, personalization–privacy, personal–retailer devices). The findings provide several theoretical and managerial implications, as well as avenues for further research.

Vulnerability in a tracked society: Combining tracking and survey data to understand who gets targeted with what content

While data-driven personalization strategies are permeating all areas of online communication, the impact for individuals and society as a whole is still not fully understood. Drawing on Facebook as a case study, we combine online tracking and self-reported survey data to assess who gets targeted with what content. We tested relationships between user characteristics (i.e. socio-demographic and individual perceptions) and exposure to branded content on Facebook. Findings suggest that social media use sophisticated algorithms to target specific groups of users, especially in the context of gender-stereotyping and health. Health-related content was predominantly targeted at older users, females, and at those with higher levels of trust in online companies, as well as those in poorer health conditions. This study provides a first indication of unfair targeting that reinforces stereotypes and creates inequalities, and suggests rethinking the impact of algorithmic targeting in creating new forms of individual and societal vulnerabilities.

Data-Driven Personalization of a Physiotherapy Care Pathway: Case Study of Posture Scanning

BackgroundAdvanced sensor, measurement, and analytics technologies are enabling entirely new ways to deliver health care. The increased availability of digital data can be used for data-driven personalization of care. Data-driven personalization can complement expert-driven personalization by providing support for decision making or even by automating some parts of decision making in relation to the care process.ObjectiveThe aim of this study was to analyze how digital data acquired from posture scanning can enhance physiotherapy services and enable more personalized delivery of physiotherapy.MethodsA case study was conducted with a company that designed a posture scan recording system (PSRS), which is an information system that can digitally record, measure, and report human movement for use in physiotherapy. Data were collected through interviews with different stakeholders, such as health care professionals, health care users, and the information system provider, and were analyzed thematically.ResultsBased on the results of our thematic analysis, we propose three different types of support that posture scanning data can provide to enhance and enable more personalized delivery of physiotherapy: 1) modeling the condition, in which the posture scanning data are used to detect and understand the health care user’s condition and the root cause of the possible pain; 2) visualization for shared understanding, in which the posture scanning data are used to provide information to the health care user and involve them in more collaborative decision-making regarding their care; and 3) evaluating the impact of the intervention, in which the posture scanning data are used to evaluate the care progress and impact of the intervention.ConclusionsThe adoption of digital tools in physiotherapy has remained low. Physiotherapy has also lacked digital tools and means to inform and involve the health care user in their care in a person-centered manner. In this study, we gathered insights from different stakeholders to provide understanding of how the availability of digital posture scanning data can enhance and enable personalized physiotherapy services.

Thinking Outside the Black-Box: The Case for “Algorithmic Sovereignty” in Social Media

This article is an interdisciplinary critical analysis of personalization systems and the gatekeeping role of current mainstream social media. The first section presents a literature review of data-driven personalization and its challenges in social media. The second section sheds light on increasing concerns regarding algorithms’ ability to overtly persuade—and covertly manipulate—users for the sake of engagement, introducing the emergence of the exclusive ownership of behavioral modification through hyper-nudging techniques. The third section empirically analyzes users’ expectations and behaviors regarding such data-driven personalization to frame a conceptualization of users’ agency. The fourth section introduces the concept of “algorithmic sovereignty.” Current projects that aim to grant this algorithmic sovereignty highlight some potential applications. Together this novel theoretical framework and empirical applications suggest that, to preserve trust, social media should open their personalization algorithms to a social negotiation as the first step toward a more sustainable social media landscape. To decentralize the immense power of mainstream social media, guarantee a democratic oversight, and mitigate the unintended undesirable consequences of their algorithmic curation, public institutions and civil society could help in developing and researching public algorithms, fostering a collective awareness so as to eventually ensure a fair and accountable “algorithmic sovereignty.”

Public Service Broadcasting and Data-Driven Personalization

Through an interview-based study of Swedish public service broadcasting (PSB) companies, I explore the ways in which these institutions react to and interact with a set of normative conceptions of a contemporary digital media ecology characterized by social networking and personalization of the media experience. The respondents were engaged in negotiations of how to realistically maintain public values in a commercially configured online milieu. The nature of organizational adaptation within PSB is found to be complex. Several elements of the Nordic PSB model appear to counteract acquiescence to algorithmically aided personalization: its majoritarian heritage, its institutional caution toward data positivism, favoring more interpretive editorial audience knowledge, and the high costs and structural consequences of making individual users uniquely identifiable. These organizational ambitions and obstacles are embodied in recent innovations that act to mimic a personalized delivery, however, doing so without utilizing algorithmically aided prediction and instead favoring manual editorial selection.

Proactive e-Governance: Flipping the service delivery model from pull to push in Taiwan

Governments are well on their way to realizing many of the efficiency gains and administrative improvements promised by traditional e-government. Leveraging this mature infrastructure, Taiwan and other leading implementers have begun to explore ways to harness IT innovations beyond efficiency to also alter how government delivers services and solves public problems. Accordingly, Taiwan's fourth e-government strategy includes a notable commitment to “proactive” service and information delivery. The aim is to flip the service delivery model by shifting from the “pull” approach of traditional e-government—whereby the citizen must seek out government services—towards a “push” model, whereby government proactively and seamlessly delivers just-in-time services to citizens shaped around their individual needs, preferences, circumstance, and location. The article explores Taiwan's implementation of this new approach through three case studies. From this, the authors extract common trends and characteristics to articulate a consolidated framework for Proactive e-Governance based on citizen-centricity, data-driven personalization, and the empowerment of frontline civil servants. The article concludes with a discussion on the importance of shared global learning as leading e-governments simultaneously seek to uncover new models for governance in the 21st century.

Optimizing gonadotropin stimulation to maximize the likelihood of obtaining euploid embryos

In order to optimize the number of viable oocytes retrieved for IVF, reproductive endocrinologists alter gonadotropin (Gn) dosage and duration of simulation. The ultimate measure of a successful IVF cycle is a euploid embryo. Here we aimed to understand the relationship between commonly adjusted Gn stimulation parameters and the likelihood of obtaining euploid embryos. These insights better enable data-driven personalization of protocols for patients undergoing fertility treatments.