Artificial intelligence marketing

Artificial intelligence marketing (AIM) is a concept of in direct marketing based on leveraging database marketing techniques as well as AI concept and model such as machine learning and Bayesian Network. The main difference resides in the reasoning part which suggests it is performed by computer and algorithm instead of human. Artificial intelligence marketing provides a set of tools and techniques that enable behavioral targeting.

It is based mainly in 3 steps:

• Collect: collect useful data to create correct market decisions.
• Reason: use the AI methods and machine learning techniques to create insights about the problem. In this steps data is transformed in useful synthesized information.
• Act: in marketing context act would be some sort of communications that would attempt to influence a prospect or customer purchase decision using incentive driven message.

It is usually applied by market segmentation, target marketing and the use of recommender systems as a extra-functionality.

See also

Market segmentation, Target market

Papers

• Bart, Baesens; Viaene, Stijn; Van den Poel, Dirk; Vanthienen, Jan & Dedene, Guido.(2002), Bayesian Neural Network Learning for Repeat Purchase Modelling in Direct Marketing, European Journal of Operational Research, 138 (1), 191-211.
• Büchner, A. G., & Mulvenna, M. D. (1998). Discovering internet marketing intelligence through online analytical web usage mining. ACM Sigmod Record, 27(4), 54-61.
• Wierenga, B. (2010). Marketing and artificial intelligence: Great opportunities, reluctant partners. In Marketing intelligent systems using soft computing (pp. 1-8). Springer Berlin Heidelberg.
• Cheung, K. W., Kwok, J. T., Law, M. H., & Tsui, K. C. (2003). Mining customer product ratings for personalized marketing. Decision Support Systems, 35(2), 231-243.

Books

• Blattberg, R. C., Glazer, R., & Little, J. D. (1994). The marketing information revolution. Harvard Business School Press.