Marketing Analytics for Data-Rich Environments
By M. Wedel, P. K. Kannan, Ralph J. Tyser. 2016
1,012 citations recorded by the source corpus.
Subfields: Management Information Systems, Marketing
View Paper: https://www.semanticscholar.org/paper/b62c541434938d407b91f01db2b2063b8a901aba
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Management Information Systems
- Ming-Hui Huang
- Roland T. Rust
- Thomas Ritter
- Brent A. Scott
- Robert W. Palmatier
- Praveen K. Kopalle
- Lars Witell
- Dovev Lavie
In Marketing
- Ming-Hui Huang
- Roland T. Rust
- Janet R. Mccoll-Kennedy
- Johannes Habel
- Manish Gangwar
- A. Monga
- Ahmed Ali Abbasi
- Brent A. Scott
Citations in the corpus
- Big Data and Data Science Methods for Management Research (2016, methodology): “Bayesian statistical inference may provide a solution (Wedel & Kannan, 2016) as it assumes the data to be fixed and the parameters to be random, unlike the frequentist approach which assumes that the data can be resampled.”
- A strategic framework for artificial intelligence in marketing (2020, background): “Also, big data marketing analytics is now a main-stream approach for generating marketing insights (Berger et al. 2019; Chintagunta et al. 2016; Liu et al. 2016; Wedel and Kannan 2016).”
- Engaged to a Robot? The Role of AI in Service (2020, background): “Big data and machine learning applications personalize recommendations to customers (Chung, Rust, and Wedel 2009; Chung, Wedel, and Rust 2016) and marketing analytics personalize marketing (Wedel and Kannan 2016).”
- Digitization capability and the digitalization of business models in business-to-business firms: Past, present, and future (2020, background): “While the interest in applying data and analytics in marketing continues (e.g. Cao et al., 2019; Germann et al., 2013; Hanssens & Pauwels, 2016; Wedel & Kannan, 2016), recent marketing research has also addressed issues related to computerization (Good & Stone, 2000), the role of computers and the…”
- Path to Purpose? How Online Customer Journeys Differ for Hedonic Versus Utilitarian Purchases (2020, methodology): “Finally, we contribute to the growing literature of Big Data marketing (Bradlow et al. 2017; Kitchens et al. 2018; Sudhir 2016; Wedel and Kannan 2016) by demonstrating the great potential unlocked by “Big Data” through a multidata, multimethod approach.”
- Evolution of Consumption: A Psychological Ownership Framework (2020, background): “…sensitive source of experiential personal data comes from the Internet of Things, as many home appliances (refrigerators, washers) and systems (electrical, HVAC, water) are continuously monitored and their output harvested, capturing activity about consumers’ daily lives (Wedel and Kannan 2016).”
- Digital transformation: A multidisciplinary reflection and research agenda (2021, neutral): “The omnipresence of big data (e.g., Dong & Yang, in press; Wedel & Kannan, 2016) and advent of emerging digital technologies, such as arti fi cial intelligence (AI), blockchain, internet-of-things (IoT), and robotics, are projected to have far-reaching e ff ects on business (Chen, Chiang, & Storey,…”
- The Role of Big Data and Predictive Analytics in Retailing (2017, background): “Wedel and Kannan (2016) provide an excellent commentary on marketing analytics past, present and future.”
- Artificial Intelligence in Service (2018, background): “We can see marketing analytics in strong demand both in academic education and in business world (PricewaterhouseCoopers 2017; Wedel and Kannan 2016).”
- Digital marketing: A framework, review and research agenda (2017, neutral): “Within this section we highlight the managerial questions that could be answered using data within the firm and environment, however, we do not focus on the methodological aspects as these issues are well covered in extant research (see, for example, Wedel & Kannan, 2016).”
- Sales profession and professionals in the age of digitization and artificial intelligence technologies: concepts, priorities, and questions (2019, background): “Well-known examples include Amazon and Netflix, which provide personalized product recommendations to fulfill customers' needs (Wedel and Kannan 2016).”
- Understanding Large-Scale Dynamic Purchase Behavior (2020, neutral): “The need for computationally efficient algorithms to estimate models involving large-scale data was also recently highlighted in Wedel and Kannan (2016).”