As the software improves, conjoint analysis is becoming an increasingly useful tool for analysing benefit segments among buyers. In particular, conjoint analysis is suited to industrial marketing research, because the small sample size arising from a small population of buyers is not a problem since analysis is fundamentally at the individual level. Moreover, the mathematical elegance of the technique promises that the findings will be impressively precise. Experience suggests, however, that a great deal of art, or informed judgement, is required to support the scientific application of conjoint analysis. Many pitfalls can catch the unwary researcher by surprise. Close collaboration between commissioning managers and researchers should minimise the pitfalls through an iterative learning process which allows the researcher to make decisions in the context of the particular market and allows the manager to understand the meaning and limitations of the findings. The paper discusses the lessons learned from three industrial marketing surveys, which are described at the outset. Decisions and problems encountered at the design, implementation and analysis stages of the surveys are considered. Practical suggestions for avoiding common flaws are given.
Several recent studies in social psychology, organizational behavior, health research and in most of the social sciences have utilized facet theory as a metatheoretical framework to design models and complex theories. Its history can be traced back to at least 30 years, but it has received little attention in the marketing literature. Theory construction according to facet analysis starts with a theoretical definition of the domain of interest, continues with the description of the correspondence between the theoretical definition and on empirical aspects of the observations, and ends with data analysis and hypotheses testing. This paper summarizes the principles of facet analysis and demonstrates its utilization in marketing research and theory constuction.
While the literature provides considerable guidance about the factors and conditions that influence the use of computers and management information systems, far less is known about the factors and conditions that lead to the use of marketing information systems (MkIS). The paper reports on the results of an investigation of the determinants of Marketing Information Systems (MkIS) usage based on responses from 126 MkIS users. A multivariate model was developed and tested using analysis of covariance. Several specific propositions are proposed and tested. The results indicate that MkIS usage (as measured by frequency of use, number of information technologies in use, number of years of system usage experience and reported usage time) is influenced by factors such as task exceptions, task complexity, organisational maturity, organisational situations, user gender, organisational level, cognitive style, familiarity and perceived usefulness. Based on the research findings, several recommendations are made to improve the use of marketing information system. This paper is divided into five sections. The first section provides an introduction and outlines the research objectives of this paper. Section two reviews the literature and develops a conceptual model. The research approach and instrument validations are discussed in section three. Section four discusses the results and their implications for system designers, vendors, researchers and top management. The concluding comments with the limitations of this study and recommendations for further research are discussed in the last section.
This manuscript discusses the development of the Canadian Media Directors Council (CMDC) Television Commercial Awareness Model. The CMDC model allows advertisers to estimate the awareness levels that are generated from television advertising media schedules. Software has been created that will allow the user to generate "what- if" scenarios to determine which media schedule generates the highest levels of awareness. The CMDC model represents a significant advancement in advertising media planning. The model provides greater predictive accuracy and diagnostic ability than available models by incorporating more influential variables and focusing on individual product categories. Specifically, the model uses GRP levels, the impact of daypart distribution, media schedule, commercial length and quality of the creative execution as input variables. The model can be used with mature brands because it examines recall of specific television commercials/campaigns instead of brand awareness. The model that was developed was based on a modification of Broadbent's ADSTOCK model. This is a distributed lag approach which assumes that advertising in past periods continues to have an effect, but at a diminishing rate. The model that was developed produced error rates from 2.4% - 5.3%. Analysis of the data raised questions regarding one of the most prominent debates in the academic literature: the shape of the advertising response curve. While the literature suggests S-shaped and concave downward response curves, these shapes failed to appear in the CMDC database.
Rule induction and neural networks have been applied to audience data to produce promising results for understanding and predicting audience behaviour. Potential deployment designs suggest that this will be a fruitful way to assist the task of scheduling.
In this paper, a Decison Support System is presented that enables the decision-maker to make optimal use of existing marketing research data in the area of fast-moving consumer goods. Many valuable market data are not analyzed in depth by the decision-maker, who, therefore, do not get all the possible information out of the research budget. "SalesPlan" is a model that combines information that is already available by most brand producers. The main data of the analysis are time-series data of Category Sales, Brand Sales and Market Share, and corresponding values of the brand price, average competitor price, company and competitorsâ distribution, amount of company and competitorsâ in-store promotion, and data of company and competitorsâ retail inventory. All these data are available from Nielsen-Data, which most brand producers buy regularly, typically on a bi-monthly basis. Also, the analysis is based on GRP- and Adstock data of company and competitors' advertising efforts. These data are also available on commercial basis in most product areas. Finally, the decision-support part of the model uses company-internal data of brand production costs. Since data are time series, "SalesPlan" models trend and seasonal patterns if they exist. "SalesPlan" consists of two separate modules: A forecasting module and a decision support module. In the Forecasting Module econometric models of Category Sales, Brand Sales and Market share are estimated as functions of the set of explanatory variables. The ability of the models to describe the historical data provide valuable insight into the relative importance of the marketing decision variables and are basis for the decision support model. Based on forecasts of the important competitors' decision variables and the company's own marketing plans, short term forecasts of Company Sales, Brand Sales, and Market Share are developed.
The main objectives of this article are twofold: first, to illustrate the full range of potentialities of this new approach regarding consumers' segmentation, product positioning and advertising strategies; second, to highlight the close connections of means-end hierarchies with the optimum stimulation level (OSL) concept recently discussed in the academic literature. With respect to these two main research objectives, the analysis is conducted using a structural equation modeling approach which enables the researcher to have a powerful analysis of the causal relationships involved in the means-end hierarchies. The analysis shows the importance of consequences and to a lesser extent attributes, OSL and values for characterizing the nature of the choice of a vacation abroad. Besides, OSL allows for the determination of specific OSL segments bearing specific means-end hierarchies which help understanding the behavior under study. The main implications of this research concern consumers' segmentation, product positioning and advertising strategies.
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.
By defining METALOYALTY a contribution to the clarification of a diversity of loyalty definitions and concepts has been outlined.This cuticle is divided into two parts: First we will focus on the process leading to the definition of metaloyalty and discuss some of the loyalty measurement problems and possibilities. Secondly we shall combine the definition with a model designed for the use of marketing managers in ranking their organisation in a dialogue marketing perspective. We shall support our theory building and findings with results from three years of case study research in five Scandinavian companies which learned that the challenge to the supplier is to be able to build loyalty to individuals in an ever changing market. Our findings were: By reorganizing the marketing function, redesigning and reorganizing the communication and the market systems, the companies achieved a lift in response rates from 5-7% to 70 - 80%. A core challenge to marketing management is to understand, plan and operationalize a concept of relationary loyalty-building by using integrated marketing methods.
A Marketing Management Support Systems can be defined as any device combining (I) information technology, (II) marketing data and/ or knowledge, and (III) analytical capabilities, made available to one or more marketing decision-makers with the objective to improve the quality of marketing decision-making. In this paper we present a categorization scheme for marketing management support systems. Three types of MMSS, developed so far, can be identified: marketing information systems, marketing decision support systems and marketing knowledge-based systems. Each of these systems emphasize different components. Next, we focus on the factors affecting adoption of and satisfaction with MMSS. The outcomes are presented of a large scale study carried out in the Netherlands among 525 companies. In the third part of the paper we answer the question whether MMSS improve the effectiveness of marketing decision-makers and, if so, under which conditions. For this purpose we present the results of an experimental laboratory study in which 80 real life marketing managers and 160 marketing students participated. We conclude the paper with a discussion of the perspectives for Marketing Management Support Systems.
Segmentation research in the marketing literature is reviewed, focusing on a discussion of proposed bases and methods. Classifications of segmentation bases and of segmentation methods are provided. Segmentation bases are reviewed in terms of their ability to generate segments that conform to five criteria for effective segmentation. Segmentation methods are discussed according to the classification provided, and areas in which substantial progress has been made are identified. The recent research thrust towards methods for segmentation, and especially the so called latent class methods suggests the onset of a turbulent change in segmentation theory and practice.
This paper discusses two well-known experimental procedures which can be used as a basis for demand-oriented pricing, v.z. Gabor-Granger-procedures and Brand Price Trade Off-analyses. It demonstrates our experiences with these methods, while the relative advantages and disadvantages are discussed and current problems identified. Furthermore a recently proposed Gabor-Granger model is briefly discussed which allows for the identification of complex and non-linear effects of prices on consumer demand.