Machine Learning in Marketing
Special issue of the Journal of Business Research; Deadline 31 Mar 2021
Author: Dennis Herhausen
Machine learning in marketing
Today, marketing decision makers are often struggling to adequately capture and transform (big) customer data into meaningful insights (De Luca et al. 2020; Sheth and Kellstadt 2020). Recent research indicates that machine learning (ML)—a field of computer science dedicated to developing learning algorithms, often using big data, to generate predictions needed to make decisions (Agarwal et al. 2018)—can help companies managing the flood of data (e.g., Davenport et al. 2020; Hagen et al. 2020; Ma and Sun 2020; Vermeer et al. 2019). ML and AI have been trending topics in many industries for quite a while now, the marketing industry will be no exception; and it is being used in various industries, both in B2C and B2B context (e.g., Herhausen et al. 2020; Kumar, Shankar & Alijohani, 2019; Luo et al. 2019; Rust 2020). ML and AI hold great promise for making marketing more intelligent, efficient, consumer-friendly, and, ultimately, more effective (Huang and Rust 2018). Perhaps more pointedly, though, pronounced capabilities in ML and AI will soon move from being a “nice-to-have” functionality to a “have-to-have” capability (Rosenberg 2018).
For example, ML can optimize the user experience and transform customer-facing services by providing quick resolutions and responses in real time. Chatbots that leverage natural language processing can give customers the impression of talking to an actual customer service executive in real time. They can answer questions, track and fulfil orders, and provide help to solve simple issues. With the help of ML, marketing managers can track customers’ spending habits and use this data to recommend them similar products or related services. They can also use ML to classify the customer base and improve sales with targeted advertisements. Some companies have already started to use conversational ML/AI in their marketing strategies to engage customers across several social media platforms and their native apps, which can improve their value and brand image. Moreover, ML/AI allows brands to make strategic decisions by identifying market trends and even predicting trends for the near future. This helps the brands to avoid redundancy, reduce digital advertising costs while keeping their expenditure streamlined.
Faced with these developments, the aim of this special issue is to examine the current and future impact of ML and related technologies in marketing. Thus, this special issue will highlight novel, practical, and high-quality research regarding the applications of ML and AI in marketing. In particular, we wish to bridge the gap between the managerial and the technical perspective. All managerial, technical and strategical perspectives on ML and AI are welcome. Methodologically, we embrace a variety of methods, including surveys, applied research, field experiments, quantitative research, secondary data analytics, market research studies, and qualitative research (among others).
The list of possible topics for this special issue includes, but is not limited to:
- How can marketing managers leverage ML/AI-based data to inform their marketing strategies?
- How can advanced ML/AI (e.g., NLP, deep learning, semantic search) aid marketing decision making and turn data into revenue?
- How can marketers use ML/AI for extracting useful information from texts, images, audio, and video data sets for optimizing firm communication?
- How can marketers avoid algorithmic biases and ensure algorithmic fairness?
- How are ML/AI, chatbots, AR, and VR transforming marketing strategies and converting visitors to customers, as well as other components of the marketing mix modelling?
- How can marketers use ML/AI in hyper-targeted advertising to deliver more relevant advertisements to customers through the combination and aggregation of new and old data sources?
- How can marketers use social listening and sentiment analysis to analyze the conversations around their brand on social media platforms and target future campaigns?
- How can ML/AI be used to make the process of tagging and categorizing products more efficient?
- How can ML/AI-powered knowledge support sales and marketing in the (post) digital era?
- How can ML/AI techniques be used to overcome the complexities posed by big data sets?
- How can marketers use ML/AI in programmatic advertising to adjust bidding strategies based on CLV and invest more in valuable, potential, and influential customers?
- How do ML/AI-driven Segmentation, Targeting and Positioning (STP) generate increased revenue and provide insights for marketing decisions?How can ML/AI help in distinguishing fake reviews from genuine ones?
Papers submitted to the Special Issue will be subject to the JBR review process and submission guidelines. Authors should select “Special Issue: ML in Marketing” as manuscript type.
Paper submissions open: 1st December 2020
Paper submissions deadline: 31st March 2021
Please contact one of the following Guest Editors for any questions:
Dennis Herhausen, Kedge Business School, France
Ajay Kumar, EMLYON Business School, France
Dursun Delen, Spears School of Business, Oklahoma State University, USA
Eric W. T. Ngai, The Hong Kong Polytechnic University, Hong Kong
Stefan Bernritter, King’s College London, UK
Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24-42.
De Luca, L. M., Herhausen, D., Troilo, G., & Rossi, A. (2020). How and when do big data investments pay off? The role of marketing affordances and service innovation. Journal of the Academy of Marketing Science, forthcoming.
Hagen, L., Uetake, K., Yang, N., Bollinger, B., Chaney, A. J., Dzyabura, D., … & Wang, Y. (2020). How can machine learning aid behavioral marketing research? Marketing Letters, forthcoming.
Herhausen, D., Miočević, D., Morgan, R. E., & Kleijnen, M. H. (2020). The digital marketing capabilities gap. Industrial Marketing Management, 90, 276-290.
Huang, M. H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155-172.
Kumar, A., Shankar, R. Alijohani N. (2019). A big data driven framework for demand-driven forecasting with effects of marketing-mix variables, Industrial Marketing Management, forthcoming.
Luo, X., Tong, S., Fang, Z., Qu, Z. (2019). Frontiers: Machines vs. humans: The impact of artificial intelligence chatbot disclosure on customer purchases Marketing Science, 38 (6), 913-1084
Ma, L., & Sun, B. (2020). Machine learning and AI in marketing–Connecting computing power to human insights. International Journal of Research in Marketing, forthcoming.
Rosenberg D. (2018), How marketers can start integrating AI in their work, Harvard Business Review
Rust, R. T. (2020). The future of marketing. International Journal of Research in Marketing, 37(1), 15-26.
Sheth, J., & Kellstadt, C.H. (2020). Next frontiers of research in data driven marketing: Will techniques keep up with data tsunami? Journal of Business Research, forthcoming.
Vermeer, S. A., Araujo, T., Bernritter, S. F., & van Noort, G. (2019). Seeing the wood for the trees: How machine learning can help firms in identifying relevant electronic word-of-mouth in social media. International Journal of Research in Marketing, 36(3), 492-508.