Theory vs. Data-Driven Learning in Future E-Commerce

Kaptein, Maurits; Parvinen, Petri; Pöyry, Essi

doi:10.1109/hicss.2013.549

Cited by 4 publications

(3 citation statements)

References 34 publications

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“…For example, experiments are a widely used research method in marketing, but researchers rely on surveys, observations, or interviews to collect experimental data (Luo et al 2013). Surveys and logs are the two most common methods to acquire data for business intelligence (Kaptein et al 2013). A survey is defined as "collecting information in an organized and methodical manner about characteristics of interest from some or all units of a population using well-defined concepts, methods and procedures, and compiles such information into a useful summary form" (Canada 2010).…”

Section: Datamentioning

confidence: 99%

Demystifying Big Data Analytics for Business Intelligence Through the Lens of Marketing Mix

2015

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Section: Datamentioning

confidence: 99%

Demystifying Big Data Analytics for Business Intelligence Through the Lens of Marketing Mix

2015

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“…Surveys are more suitable when data or information is gathered about the events that seems difficult to capture or directly observed whereas Logs are used to draw conclusion based on users real-time and actual behaviour captured towards the product and service provided by the organisation. In order to examine the correlation between user attitude and performance expectancy behaviour, both the two approaches can be integrated together (Fan et al, 2015;Jacobs, 2009;Kaptein et al, 2013).…”

Section: Data Typementioning

confidence: 99%

Mapping 4Ps Of Marketing with Business Intelligence: The Way to The Future of Marketing

2022

Central European Management Journal

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Data analytics is a field that employs business intelligence gain business insights for better and efficient decision-making process. Business intelligence has been recognised and embraced as revolutionary and disruptive technological breakthrough that will change, alter, transform, and redefine the ways stakeholders and managers perceive data analytics in the present scenario. In this research work, we investigated and explored the business intelligence literature from the perspective of marketing mix parameters. We highlighted and listed the data sources, techniques, methods, and applications associated with the 4Ps of marketing mix ("People", "Place", "Promotion", and "Price") which are also considered as the core of marketing intelligence. The article's primary objective is to highlight how the two different areas of business intelligence and marketing mix can be aligned together to support an organisation's marketing decision making process or system. The paper suggests and defends the claim that organisations need to pay particular attention to the towards implementation and use of business intelligence applications. The research is limited in accordance to the authors' examination of the literature review, there hasn't been much research done on how business intelligence and marketing mix parameters can be mapped together.This area might use a lot more study. By concentrating on the relatively new interaction between business intelligence and marketing intelligence, this research is anticipated to be valuable. The authors anticipate that it will spark and boost important research potential for the future aspects.

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“…To start with, the opportunities to dynamically interact with consumers are ever increasing: marketing communication is increasingly moving toward a genuine dialogue (Bezjian-Avery et al , 1998; Haeckel, 1998; Deighton and Kornfeld, 2009; Finne and Grönroos, 2009). Also, the technological abilities to collect data are evolving: we have moved from a general inability to automatically measure consumer responses, to the use of electronic scanners, to a situation in which, in e-commerce for example, we can measure consumer responses to marketing communication continuously (Parvinen et al , 2014; Kaptein et al , 2013; Kaptein and Parvinen, 2015). This opportunity seems like a treasure throve for IMC scholars: we can now truly reap the fruits of interactive IMC (Jiang and Chia, 2010).…”

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confidence: 99%

Customizing persuasive messages; the value of operative measures

Kaptein

2018

JCM

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Purpose This paper aims to examine whether estimates of psychological traits obtained using meta-judgmental measures (as commonly present in customer relationship management database systems) or operative measures are most useful in predicting customer behavior. Design/methodology/approach Using an online experiment (N = 283), the study collects meta-judgmental and operative measures of customers. Subsequently, it compares the out-of-sample prediction error of responses to persuasive messages. Findings The study shows that operative measures – derived directly from measures of customer behavior – are more informative than meta-judgmental measures. Practical implications Using interactive media, it is possible to actively elicit operative measures. This study shows that practitioners seeking to customize their marketing communication should focus on obtaining such psychographic observations. Originality/value While currently both meta-judgmental measures and operative measures are used for customization in interactive marketing, this study directly compares their utility for the prediction of future responses to persuasive messages.

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Theory vs. Data-Driven Learning in Future E-Commerce

Cited by 4 publications

References 34 publications

Demystifying Big Data Analytics for Business Intelligence Through the Lens of Marketing Mix

Demystifying Big Data Analytics for Business Intelligence Through the Lens of Marketing Mix

Mapping 4Ps Of Marketing with Business Intelligence: The Way to The Future of Marketing

Customizing persuasive messages; the value of operative measures

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