Artificial and Automated: Innovation in Marketing

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Table of Contents

Introduction
Project Background
Project Aim and Objectives
Intellectual challenge
Research Approach/Programme
Deliverables
Resources
References

Introduction

Automated marketing (AM) is a growing multi-billion dollar industry. Automated marketing uses various Artificial Intelligences (AI) in offering products and services to potential consumers with the specific intention of affecting the purchasing behaviour of those consumers. The effectiveness of the automated marketing communication depends on its AI design. The AI design can create sufficient interest in the commodity that would facilitate the purchasing decision. Thus, a well-designed AI marketing communications can augment sales (Loukis, Sapounas & Aivalis 2008).

The objective of automated marketing in marketing communication is to influence the purchase decision of the consumer through artificial intelligence (AI). Hence, the general research question of the study is the following: “What are the benefits and limitations of AI in marketing communication?”. Specifically, “What are the implications of AI applications in brand communications and positioning in automated marketing?”.

The interest in the use of AI in marketing communication has grown very rapidly. However, the progress made in the development of automated marketing has been slow because of the difficulties associated with knowledge acquisition. Automated knowledge acquisition in general is a promising alternative for automated marketing knowledge acquisition and knowledge base refinement, which will greatly simplify the process of developing and maintaining AI in marketing communication knowledge bases (Ling, San & Nguyen 2012).

Project Background

Exploring the application of AI methodologies in marketing communication, specifically, AI applications in brand communications and positioning, will assess the potential for AI in the future. This will prove to be very useful, and it will provide a fertile area of research for marketing communication and competitive use of artificial intelligence in automated marketing.

The directions of artificial intelligence (AI) research in marketing have been twofold. One has been to build computer models that help in understanding the nature of marketing communication. The other has been to develop automated marketing that demonstrate certain levels of human intelligence in solving marketing communication issues (Ling, San & Nguyen 2012). While the two reciprocate in that the knowledge of one leads to a better understanding of the other, our interest in this study is towards the latter because, typically, marketing applications of AI, primarily, ought to deal with finding solutions that are as good as or even better than solutions found by other means.

Project Aim and Objectives

This study will focus on the benefits and limitations of AI in marketing communication from a broad position addressing key areas relevant to the marketing community and its changing paradigm.

Aim

Specifically, the aim of the study is to find out the implications of an automated marketing agenda and the underlying AI applications in brand communications and positioning.

Objectives

The objectives of the study are the following:

Demonstrate the relevance of direct communication, consumer-driven campaigns, and technology-driven market intelligence in brand communications and positioning;
Investigate the potential of AI in marketing communication.

Intellectual challenge

These new technologies offer a great promise for brand communications and positioning, which are designed to communicate information about each product, including its appearance, cost, range of colors and sizes, other complementary products and accessories, and pertinent information.

It also offers various implications underlying AI applications in brand communications and positioning. Addressing key factors, such as direct communication, consumer-driven campaigns, and technology-driven market intelligence, this research will assess the potential for AI in the future.

Exploring the application of artificial intelligence methodologies in marketing communication, specifically, AI applications in brand communications and positioning will assess the potential for AI in the future. This will prove to be very useful, and provide a fertile area of research for marketing communication and competitive use of artificial intelligence in automated marketing.

Research Approach/Programme

This research will adopt an empirical approach (Marczyk, Dematteo & Festinger 2005). Beginning with a problem to be solved, the researcher will have attempt to find, through scientific intuition and experimentation, the promises of using new AI methodologies of in marketing communication. In doing so, the researcher will be able to gain a better understanding of the processes involved in automated and artificial intelligence in marketing communication.

Deliverables

Deliverables	January 2014				February 2014				March 2014				April 2014
Deliverables	1	2	3	4	1	2	3	4	1	2	3	4	1	2	3	4
Introduction
Literature reviews
Data Collection
Study & Analysis
Research Results
Final Thesis Writing and Review

Resources

I will employ various methods in order to empirically ground specific implications links between AIs and automated marketing communication. Resources for this study will include marketing communication/brand press and financial records, marketing and psychology journals, aggregated social indicators related to marketing communication, brand communications and positioning, marketing textbooks, online/offline marketing/consumerist media, and evidence of various AIs in marketing.

References

Ling, S, San, P, & Nguyen, H 2012, ‘Evolutionary computation, fuzzy logic, neural network and support vector machine techniques ‘, in H Lam, S Ling & H Nguyen (eds) Computational intelligence and its applications, Imperial College Press, London.

Loukis, E, Sapounas, I, & Aivalis, K 2008, ‘The effect of generalized competition and strategy on the business value of information and communication technologies’, Journal of Enterprise Information Management, vol.21, no.1. pp. 24–38.

Marczyk, G, Dematteo, D, & Festinger, D 2005, Essentials of research design and methodology, John Wiley & Sons, Hoboken.

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