Personalized digital marketing recommender engine

Behera, Rajat Kumar; Gunasekaran, Angappa; Gupta, Shivam; Kamboj, Shampy; Bala, Pradip Kumar

doi:10.1016/j.jretconser.2019.03.026

Cited by 118 publications

(74 citation statements)

References 75 publications

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“…Additionally, although personalized information is recommended in e-customization, 81 consumers' acceptance may be influenced by other factors as well, such as their acceptance of the recommending information. 82,83 This study also supports and develops the findings of Gussen et al, 84 who suggested that development of perception scales may be appropriate for perceived quality in robot-supported systems. The findings enrich the literature on apparel e-customization and online shopping via exploring and testing mixed item sets of multisensory perception and positive emotion.…”

Section: Theoretical and Managerial Implicationssupporting

confidence: 86%

Multisensory perception and positive emotion: Exploratory study on mixed item set for apparel e-customization

Chen

Spence

2020

Textile Research Journal

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With the rapid development of digital technology, many consumers increasingly prefer to buy their clothes online. In order to improve the quality of online services and enrich the consumer experience of apparel e-customization, this paper develops item scales that measure positive emotion, visual perception, and haptic perception. It also evaluates the relationships between the item scales, attitude, and intention. It is an exploratory study on consumers’ multisensory perception and positive emotion mixed item scale (MPPEMIS), collected by experts. Supported by factor analysis and correlation analysis, both laboratory and online studies were conducted to test the reliability and validity between item scales, attitude, and intention. Visual factors (e.g., transparency, brightness, dimness), haptic factors (e.g., comfort, coarseness, softness), and positive emotion (e.g., excitement, attractive, pleasantness) are proved in the MPPEMIS, which positively correlates with the factors of attitude and intention in apparel e-customization. As such, the MPPEMIS may help to assist brand managers, marketers, and retailers by recommending easily understood information and providing item scales for apparel e-customization that can also be adopted in online service strategies and system modularization.

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Section: Theoretical and Managerial Implicationssupporting

confidence: 86%

Multisensory perception and positive emotion: Exploratory study on mixed item set for apparel e-customization

Chen

Spence

2020

Textile Research Journal

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“…Another contribution of this work is to develop a framework that demonstrates the capability of ML models in determining early purchase intention regardless of a user's registration status (registered or unregistered). In the existing studies, it has been highlighted that returns from unregistered consumers is very low as providing personalised offers for unregistered user is a challenging task (Behera et al 2020;Hallikainen et al 2019). Consequently, this work shows that ML models are strong predictors for when the shoppers' behaviours are analysed appropriately and behavioural features are generated dynamically for active sessions.…”

Section: Theoretical Implicationsmentioning

confidence: 89%

“…The e-commerce industry is moving rapidly towards targeted personalised adverts. Recent studies show that offering all potential consumers generic items recommendation has proven to be an ineffective strategy (Behera et al 2020;de Pechpeyrou 2009;Stewart-Knox et al 2016). One of the main issues in online sales is consumer conversion; the amount of online sessions (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…One of the main issues in online sales is consumer conversion; the amount of online sessions (i.e. user visit to a website) that end with a purchase is negligible when compared to the total number of session/visits (Liu et al 2019;Zhou et al 2019;Behera et al 2020). A substantial number of these abandoned sessions are due to lack of purchase intention from the consumers, which means that there is almost no chance for conversion; therefore, rendering marketing strategies ineffective.…”

Section: Introductionmentioning

confidence: 99%

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Towards early purchase intention prediction in online session based retailing systems

2020

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Purchase prediction has an important role for decision-makers in e-commerce to improve consumer experience, provide personalised recommendations and increase revenue. Many works investigated purchase prediction for session logs by analysing users’ behaviour to predict purchase intention after a session has ended. In most cases, e-shoppers prefer to be anonymous while browsing the websites and after a session has ended, identifying users and offering discounts can be challenging. Therefore, after a session ends, predicting purchase intention may not be useful for the e-commerce strategists. In this work, we propose and develop an early purchase prediction framework using advanced machine learning models to investigate how early purchase intention in an ongoing session can be predicted. Since users could be anonymous, this could help to give real-time offers and discounts before the session ends. We use dynamically created session features after each interaction in a session, and propose a utility scoring method to evaluate how early machine learning models can predict the probability of purchase intention. The proposed framework is validated with a real-world dataset. Computational experiments show machine learning models can identify purchase intention early with good performance in terms of Area Under Curve (AUC) score which shows success rate of machine learning models on early purchase prediction.

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“…Nessa situação de crise, uma das formas de potencializar as vendas e minimizar perdas financeiras é investir em marketing por meio de mídias digitais. Com essa estratégia, os e-business utilizam-se da análise de dados para publicizar e manter suas transações mercadológicas ativas (Behera, Gunasekaran, Gupta, Kamboj, & Bala, 2020). Em um sentido mais amplo, as TICs intensificam relacionamentos entre usuários, clientes, organizações e colaboradores (Rindfleisch, 2019).…”

Section: Ambientes Digitais: Estratégias Laborais E Mercadológicasunclassified

COVID-19 e organizações: estratégias de enfrentamento para redução de impactos

Castro

Oliveira

Morais

et al. 2020

RPOT

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As organizações sofrem com os impactos socioeconômicos do cenário de crise global provocados pela pandemia de COVID-19. Nesse contexto é primordial adotar medidas objetivas para minimizar os reflexos nocivos dentro da gestão organizacional. Este estudo buscou identificar as principais estratégias para redução de impactos da COVID-19 nas organizações brasileiras. Trata-se de um ensaio teórico baseado em uma revisão de literatura, através de análise de artigos de bases a partir de dados nacionais e internacionais sobre a COVID-19, bem como investigação relacionada às experiências em pandemias anteriores, assim como a legislação brasileira vigente no período pandêmico. Considerando esse cenário de incertezas, sugere-se a adoção de estratégias para minimizar impactos desfavoráveis às atividades corporativas, tais como: comunicação eficiente; planejamento do trabalho; aprimoramento digital; tele-trabalho; adoção de medidas visando o bem-estar dos trabalhadores. Identificou-se a necessidade de planejamentos emergenciais para o enfrentamento da pandemia, incluindo: políticas empáticas, fornecimento de suporte para tele-trabalho e ações para adaptação e manutenção organizacional durante a crise.

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Personalized digital marketing recommender engine

Cited by 118 publications

References 75 publications

Multisensory perception and positive emotion: Exploratory study on mixed item set for apparel e-customization

Multisensory perception and positive emotion: Exploratory study on mixed item set for apparel e-customization

Towards early purchase intention prediction in online session based retailing systems

COVID-19 e organizações: estratégias de enfrentamento para redução de impactos

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