Facial expressions are a strong visual method to convey emotions. 3D Facial Imaging directly records human emotions from facial expressions to better measure consumer response to marketing stimuli (e.g. advertising, packaging, retail displays). This presentation shows how an automated, artificial intelligence based system of facial imaging can be integrated into an online panel in a manner complementing traditional survey based approaches. This produces new insight on both how people answer conventional questions and how to exploit more efficient ways of gathering accurate responses to complex marketing questions.
Customers are increasingly demanding, and successful companies need to design and introduce new ways to offer customer value. However, the process is not complete until they design control systems, provide a support decision tool able to identify and distinguish customer behaviour profiles according to their loyalty, and help marketers to readapt relationship marketing strategies in order to increase efficiency. LAMDA (Learning Algorithm Machine for Data Analysis), an artificial intelligence technique software tool enabling forecasting and identification of customer behaviour, is based on a self-learning classifying technique that relies on the generalizing power of Fuzzy Logic and the interpolation capability of logical hybrid connectives. This paper specifically examines the efficiency of the LAMDA classifier in identifying and distinguishing between the various degrees of customer loyalty. The study carried out in this project is based on data gathered from the customer loyalty cards of a Spanish grocer, Supermercats Pujol, S.A.
Observations of computer simulated agents a (Artificial Life) provide insight into the decision process of consumers. This fresh approach to modeling consumer behavior enables researchers to discover innovative solutions to the facets of marketing.
This paper reviews the impact of new technologies in the research process over the past five years and the incoming new technologies up to the next millennium. The main focus, the effect of speech-to-text Voice Recognition technology - upon the industry in general, processing, research enablement, artificial intelligence and linguistic modelling. In general the effect of speech dictation will first be seen in terms of accuracy, increased response and quality, particularly in difficult and international markets, with the ability to combine use with tele and video conferencing, telecommunications. This new IT will have a significant affect on accuracy, reliability and incisiveness of qualitative research, its interpretation and analysis. While negating traditional bias, this should bring wider use. The paper also looks at how the idea of evolutionary behaviour and psychology may be introduced into todayâs research.
Two factors are necessary for a method to be useful in a variety of country settings. An analysis technique to understand information available is required that is robust and not constrained. And, data of a comparable format and scope of measurement is needed to enable parallel analysis across countries. Artificial intelligence neural networks and Consumer Confidence Surveys fit these criteria. This work provides an example of the power of this combination.
This work demonstrates the learning ability and capacity of artificial intelligence neural networks, and how they are effective in providing information from large data sources. The combination of new techniques of data collection and the appropriation of technology from scientific fields supplies vast capabilities to consumer marketers. At the center of this approach is the artificial intelligence neural network. The impact of adopting this method of data understanding warrants special attention and emphasis, hence the term - - Neural Analysis.
The paper presents a newly developped and implemented system for segmenting respondents 'on the fly' during a computer assisted telephone interview, while at the same time minimizing the number of questions to be asked of the individual respondents. This is accomplished by using a model based segmentation scheme combined with ideas taken from artificial intelligence, especially expert systems based on probabilistic nets. In the paper the underlying the segmentation model is related to artificial intelligence and various ways knowledge representations. The opportunity of making the data collection intelligent or adaptive is explored and the implementation is demonstrated by way of a case study. The system offers several advantages. It makes segmentation based on a large number of variables, e.g. life style segmentation, operational in relation to follow-up surveys. Then, it reduces the costs of the interview in terms of money and respondent fatigue. Finally, it makes the segment variable accessible to the questionnaire designer for purposes of branching, skipping, and other conditional instructions.
Developments in a variety of disciplines have provided the necessary components to assemble a system of artificial intelligence available for use in formulating marketing strategy. Based on artificial intelligence neural networks, the concept of neural marketing is presented. A flexible new technique, neural marketing has abilities to measure and interpret expectations. Neural networks understand data and, in a process that mirrors human trial and error learning, neural nets find the relationships of cause and effect that are present in that data. This ability to learn is complicated with a facility to generalize that acquired knowledge and apply it to new experiences. Market researchers will find neural networks of value for any situation requiring forecasting and prediction. Neural marketing takes the next step by uniting all data sources, marketing practitioners, and a new strategic intelligence. In this paper there is a review of neural network theory, a presentation of the concept of neural marketing, and general examples of the benefit neural marketing provides for measuring expectations.
Knowledge-based systems (KBS), a branch of artificial intelligence, are designed to replicate the functions performed by a human expert. They enable a user to consult a computer system as he/she would consult an expert advisor to figure out how to solve a problem or to make a decision. Marketing decision-making is complex, situational and unstructured. When making decisions in an unstructured problem situation, marketing managers must use judgement and their general problem-solving abilities. Surprisingly little is known about decision- making by practicing managers, and both research results and theoretical models are underutilized. The complexity and uncertainty in marketing require more advanced, efficient and productive tools for decision-making - there is an obvious need for more sophisticated marketing decision-making. In this paper we will examine the main requirements for a successful use of computer- based systems in marketing decision-making. We will focus on strategic market management in a pharmaceutical industry, outline a conceptual framework for strategic market management and discuss the structure of its knowledge base. Our main argument is that support of a KBS for strategic market management promises to improve the decision-making capabilities of the managers.
The central thesis of this paper is that a perspective is needed in introducing new products and services in "hot areas" such as high technology. Artificial Intelligence has been around since the mid-1950s, but has remained largely an academic research pursuit. Then in the 1980s, its best-known subfield, expert systems (ES) --more correctly: knowledge-based systems-- came into its own with high expectations. But predicted growth rates of around 60% per annum soon gave way to pessimistic scenarios of small increases in revenue from commercial applications. Neither the optimists nor the pessimists proved to be right. As we enter the 1990s, the market is nearing $200 million/year in the USA
In this paper, following a review of the historical development of research methods for predicting volume sales and brand shares of new products, a new model (MicroTest) is described which uses information gathered in a concept/product test for volume prediction. The model makes use of brand related parameters (such as advertising and distribution), altitudinal predispositions (e.g. âexperimentalismâ), and circumstantial factors as input to the model, and these are described, together with the method of integrating these for predicting at the individual respondent level. Individual results are then accumulated across a sample of individuals, and grossed up to provide national sales estimates. The paper describes the various development stages undergone in the construction of the model, and the techniques used to assist this process. In particular, the way in which Artificial Intelligence techniques such as ârule inductionâ was used is discussed. Finally, the paper discusses the way in which the basic model may be extended, and some recent work which used the model to generate a measure of cumulative penetration.
The authors propose to identify reasons for the apparent lack of Marketing Research applications. Examples of areas where AI and ES could contribute to increase considerably the efficiency of Marketing Research will then be developed. Finally, the requirements for successful implementation will be examined from the potential user's point of view.