Högskolan i Skövde

This study presents a novel taxonomy of short message service campaigns, for the purpose of building an intelligent marketing system. The main issue of mass marketing is that one size does not fit everybody. In other words, it is challenging to meet different consumer needs. With the help of artificial intelligence, marketers can be supported to overcome some of these challenges. This study uses a mixed methods approach where design science and grounded theory is used to produce a short message service campaign taxonomy for a future intelligent marketing system. Data collection consisted of 386 previously active campaigns used over 33 months to build the taxonomy. An experimental study was conducted to test the effectiveness of the proposed taxonomy. The experiments involved automatic generation of campaign messages. The validity of these campaign messages, and hence the proposed taxonomy, was ascertained by analysing the messages within a business context. The study concludes that the system, intertwined with the taxonomy, performs comparably to a regular campaign. Another proof of concept is that the business context deemed the generated campaign texts to be both semantically and syntactically similar to run them in active campaigns as experiments.

Marketers need new ways of generating campaigns artificially for their marketing activities. Many marketers assume proprietary systems are individualized enough. This article investigates an order of models used to measure how reliably a system can generate campaigns artificially while producing a campaign classification and generation models that are integrated into an intelligent marketing system. The order is between a Classification Model (CM) and a Generation Model (GM). The order also functions as an iterative model improvement process for developing the models by evaluating the models’ accuracy distributions. The CM received a mean accuracy of 100%. The GM received 98.9% mean accuracy and a reproducibility score of 96.2%, implying the vast potential for increased resource savings, marketing precision, and less consumer annoyance. The conclusion is that the developed system can reliantly construct campaigns

Autogenerated Advertisements (AGAs) can be a concern for consumers if they suspect that Artificial Intelligence (AI) was involved. Consumers may have an opposing stance against AI, leading companies to miss profit opportunities and reputation loss. Hence, companies need ways of managing consumers’ con-cerns. As a part of designing such advices we explore consumers’ discernment ability (DA) of AGAs. A quantitative survey was used to explore consumers’ DA of AGAs. In order to do this, we administered questionnaires to 233 re-spondents. A statistical analysis including Z-tests, of these responses suggests that consumers can hardly pick out AGAs. This indicates that consumers may be guessing and thus do not possess any significant DA of our AGAs.

Artificial Intelligence (AI) and Machine Learning (ML) are shaping marketing activities through digital innovations. Competition is a familiar concept for any digital retailer, and the digital transformation provides hopes for gaining a competitive edge over competitors. Those who do not adopt digital innovations risk getting outcompeted by those who do. This study aims to identify AI mar-keting (AIM) adoptions used for ad optimization with Reinforcement Learning (RL). A scoped literature review is used to find ad optimization adoptions re-search trends with RL in AIM. Scoping this is important both to research and practice as it provides spots for novel adaptations and directions of research of digital ad optimization with RL. The results of the review provide several different adoptions of ad optimization with RL in AIM. In short, the major category is Ad Relevance Optimization that takes several different forms depending on the purpose of the adoption. The underlying found themes of adoptions are Ad Attractiveness, Edge Ad, Sequential Ad and Ad Criteria Optimization. In conclusion, AIM adoptions with RL is scarce, and recommendations for future research are suggested based on the findings of the review.