10,696 research outputs found

    Community Development Evaluation Storymap and Legend

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    Community based organizations, funders, and intermediary organizations working in the community development field have a shared interest in building stronger organizations and stronger communities. Through evaluation these organizations can learn how their programs and activities contribute to the achievement of these goals, and how to improve their effectiveness and the well-being of their communities. Yet, evaluation is rarely seen as part of a non-judgemental organizational learning process. Instead, the term "evaluation" has often generated anxiety and confusion. The Community Development Storymap project is a response to those concerns.Illustrations found in this document were produced by Grove Consultants

    HUK-COBURG: The Implementation of an AI-Enabled Behavioural Insurance Business Model using Geo-Spatial Data

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    Automotive insurance is undergoing digital transformation that exploits new forms of big data and Artificial Intelligence (AI) systems. Geo-spatial data from GPS and telematics systems enables innovative risk modelling to evaluate driver behaviour and leads to the creation of new insurance services and novel insurance business models. A research framework is proposed to analyse AI-enabled business models and applied to a detailed case analysis of behavioural insurance in HUK-COBURG. The results illustrate the application of geo-spatial data in an insurance context and demonstrate the utility of the research framework to analyse new AI-enabled business models. The analysis identifies important implementation issues and shows that the strategic logic, regulatory and ethical context are important elements of business models. The empirical analysis reveals the strategic properties and effects of the data flywheel concept, which has general applicability. The theory framework and empirical results have important implications for other markets and theoretical contexts

    Consumer Data Research

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    Big Data collected by customer-facing organisations – such as smartphone logs, store loyalty card transactions, smart travel tickets, social media posts, or smart energy meter readings – account for most of the data collected about citizens today. As a result, they are transforming the practice of social science. Consumer Big Data are distinct from conventional social science data not only in their volume, variety and velocity, but also in terms of their provenance and fitness for ever more research purposes. The contributors to this book, all from the Consumer Data Research Centre, provide a first consolidated statement of the enormous potential of consumer data research in the academic, commercial and government sectors – and a timely appraisal of the ways in which consumer data challenge scientific orthodoxies

    Lookalike Targeting on Others\u27 Journeys: Brand Versus Performance Marketing

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    Lookalike targeting is a widely used model-based ad targeting approach that uses a seed database of individuals to identify matching “lookalikes” for targeted customer acquisition. An advertiser has to make two key choices: (1) who to seed on and (2) seed-match rank range. First, we find that seeding on others’ journey stage can be effective in new customer acquisition; despite the cold start nature of customer acquisition using Lookalike audiences, third parties can indeed identify factors unobserved to the advertiser that move individuals along the journey and can be correlated with the lookalikes. Further, while journey-based seeding adds no incremental value for brand marketing (click-through), seeding on more downstream stages improves performance marketing (donation) outcomes. Second, we evaluate audience expansion strategies by lowering match ranks between the seed and lookalikes to increase acquisition reach. The drop in effectiveness with lower match rank range is much greater for performance marketing than for brand marketing. Performance marketers can alleviate the problem by making the ad targeting explicit, and thus increase perceived relevance; however, it has no incremental impact for higher match lookalikes. Increasing perceived targeting relevance makes acquisition cost comparable for both high and low match ranks

    Enhancing human-centered design methods through jobs to be done

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    Muitas vezes, a criação de produtos digitais tende a priorizar o Design da interface ao invés de focar em como resolver os problemas do usuário. Para realizar uma pesquisa de usuário mais profunda e criar produtos melhores, a metodologia Jobs To Be Done (JTBD) pode ser uma adição viável à caixa de ferramentas geral de UX. Apesar de o framework JTBD já existir há algum tempo, ele ganhou popularidade entre os UX Designers recentemente. No entanto, no momento desta pesquisa, não há pesquisas ou informações suficientes disponíveis sobre como combinar essas metodologias. Portanto, esta pesquisa realizou um estudo comparativo entre a metodologia Jobs To Be Done (JTBD) e a Metodologia UX, a fim de entender se sua fusão é viável e benéfica na realização de User Research. Por meio de uma revisão de literatura seguida de uma pesquisa online, entrevista UX e entrevista JTBD, buscou-se entender as diferenças e semelhanças nas informações obtidas com cada framework. Por fim, com as informações obtidas, aplicamos os resultados a artefatos tangíveis, incluindo uma Análise do Concorrente, Persona do Usuário e Mapa de Jornada do Cliente, a fim de comparar visualmente a metodologia UX com o framework JTBD. Este estudo levou a insights positivos sobre a combinação das metodologias JTBD e UX, pois concluímos que a fusão não é apenas alcançável, mas necessária para a metodologia HCD. As descobertas seriam benéficas para a comunidade de design, bem como para as empresas e instituições que investem no desenvolvimento de software e aplicativos e, mais importante, para o usuário final.Often, the creation of digital products tends to prioritize the Design of the interface instead of focusing on how to solve the user’s problems. In order to undertake deeper User Research and build better products, the Jobs To Be Done (JTBD) methodology might be a feasible addition to the general UX toolbox. Despite that the JTBD framework has been around for a while now, it has gained popularity among UX Designers just recently. Nevertheless, at the moment of this research, there is not enough research or information available about how to combine these methodologies. Therefore, this research carried out a comparative study between the Jobs To Be Done (JTBD) methodology and the UX Methodology, in order to understand if their merge is viable and beneficial in carrying out User Research. Through a literature review followed by an online survey, UX interview and JTBD interview, we sought to understand the differences and similarities in the information obtained with each framework. Finally, with the information obtained, we applied the results to tangible artifacts, including a Competitor Analysis, User Persona and Customer Journey Map, in order to visually compare the UX methodology with the JTBD framework. This study led to positive insights about the combination of JTBD and UX methodologies, as we concluded that the merge is not only attainable but necessary for the HCD methodology. The findings would be beneficial to the Design Community, as well as the companies and institutions investing in software and app development, and most importantly, for the end user

    Modelling User Behaviour in Market Attribution: finding novel data features using machine learning

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    This paper presents an exploration of market attribution methods and the integration of user behaviour. Attribution is the measurement of interaction between marketing touchpoints and channels along the customer journey, improving customer insights and driving smarter business decisions. Improving the accuracy of attribution requires a deeper understanding of user behaviour, not just marketing channel credit assignment. Evidence has been provided regarding the problems in the standardized approach to behavioural modelling and alternatives have been presented. The study explores data provided by a British based jewellery company with an investigation into pre-existing data features that can aid with the analysis of user behaviour. The study contains over 10 million rows collected over 2 years and presents the initial findings made in the first 15 months of a PhD study

    Using Customer Segmentation to Build a Hybrid Recommendation Model

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    Camacho, P., de Almeida, A., & António, N. (2021). Using Customer Segmentation to Build a Hybrid Recommendation Model. In J. V. de Carvalho, P. Liberato, Á. Rocha, & A. Peña (Eds.), Advances in Tourism, Technology and Systems - Selected Papers from ICOTTS20 (pp. 299-308). (Smart Innovation, Systems and Technologies; Vol. 208). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-33-4256-9_27The growing trend in leisure tourism has been closely followed by the number of hospitality services. Nowadays, customers are more sophisticated and demand a personalized and simplified experience, which is commonly achieved through the use of technological means for anticipating customer behavior. Thus, the ability to predict a customer’s willingness to buy is also a growing trend in hospitality businesses to reach more customers and consolidate existing ones. The acquisition of a transfer service through website reservation generates data that can be used to perform customer segmentation and enable recommendations for other products or services to a customer, like recreation experiences. This work uses data from a Portuguese private transfer company to understand how its private transfer business customers can be segmented and how to predict their behavior to enhance services cross-selling. Information extracted from the data acquired with the private transfer reservations is used to train a model to predict customer willingness to buy, and based on it, offer leisure services to customers. For that, a hybrid classifier was trained to offer recommendations to a customer when he/she is booking a transfer. The model employs a two-phase process: first, a binary classifier asserts if the customer who’s buying the transfer would eventually buy a service experience. In that case, a multi-class model decides what should be the most likely experience to be recommended.authorsversionpublishe

    A methodological approach to consumer research on second-hand fashion platforms in Italy: online clothing reselling platforms: perceptions and preferences of Italian consumers

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    The research focuses on second-hand fashion platforms (Vinted, Vestiaire Collective, Depop, Zalando Second-hand) in Italy from a consumer standpoint. The study assesses the platform's positioning, and most preferred characteristics, as well as the potential consumer segments in the Italian market. By conducting surveys with consumers, and applying market research techniques such as perceptual maps, conjoint analysis, and k-means clustering, we were able to learn consumers' perceptions, preferences, and their relevance to the platforms. The main discoveries are then used to suggest recommendations for the companies to improve their market presence and competitive edge
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