The Single Source Model is a self-report model and proving far less reliable in a world where consumers are now exposed and interacting with brands across diverse channels and multiple screens, often simultaneously. Whether bravado or curiosity, in 2012 we decided to see whether we could isolate and then measure different components of a communications campaign (offline and online) and tie them back to sales. We have a proven system and method that take into account key aspects of brand campaign, manipulate them and measure a positive impact on sales. The model has been validated using 14 large scale brand campaigns (involving many of The Netherlandsâ largest brands).
This paper discusses the relationship between market orientation and the application of information technology. A scale for measuring market orientation is developed. From a survey the characteristics of organizations with a certain market orientation have been identified. Three clusters of organizations have been found: -1 the internally oriented cluster, -2 the cluster of target group oriented organizations and -3 the customer oriented cluster of organizations. A number of areas of interest and some developments in marketing are determined for each cluster. Among other things it is established that the role of information technology is strongly correlated with the organization's orientation.
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.
This paper discusses some implications of the on-going adoption of scanning for marketing management and marketing research. Attention is given to the use of scanning data for decision support in marketing. This issue is examined empirically using scanning data on a number of brands of a fast moving consumer good sold in The Netherlands. Concise parsimonious market share models are calibrated using 'traditional bi-monthly Nielsen data'. These models are compared with models which are calibrated using scanning data. The model outcomes of these two operations are compared and evaluated. It is found that by using scanning data the quality of the model outcomes may be improved considerably.
This paper deals with short-term forecasting of market shares using market share models. The predictive power of market share models is a subject that has received considerable attention in marketing literature. However, hardly any attention has been paid to the question of how the values of the marketing instruments of competitors can be predicted. This is remarkable since these values constitute the input variables for the market share model. In this paper we will investigate the sensitivity of predicted market shares to different assumptions with respect to competitive behaviour.
In this paper the effects of stagflation on consumer response are determined. This study is part of a larger scaled study on modelling structural shifts in market response over time. In this paper some preliminary results together with some further ideas for research are discussed. The response of consumers is measured in two different ways. First the effects of stagflation are measured by possible changes in price, distribution and advertising elasticities. Next the effects are determined by an approach where the brands' market shares are related to the relative values of marketing instruments and the trend in consumption figures. These analyses are performed using data of 13 brands in three different markets.
In this paper additional support is given to our statement that the quality of panel data must be improved before these data can be used as a firm basis for decision-making in marketing. To this end the case study is extended to cover data which refer to six brands instead of to one brand. The results are shown in the following section. Then we show the results of a formal analysis in which the differences between actual data and consumer panel data are explained by a number of exogenous variables such as price, advertising expenditures and product characteristics. From this analysis a number of guidelines is obtained which may be used to correct consumer panel data in order to decrease the data bias in consumer panel data to a significant degree. We proceed by showing the improvements in the quality of retail audit data which result from a new way of dealing with non-response in retail audits. Finally, we shall summarise our findings.
A major and well-known "tool of our trade" is the collection of information through observation. The major applications of observation as an information-collection method may be classified into the categories of the audit, coincidental recording devices, and a general classification, direct observation. In this study we evaluate data which are obtained by audits which are performed on both distributors and consumers. The evaluation is performed from the point of view of a user of research. When we compare audit data with actual data, we will find a data bias. In this study the sources of data bias which may occur when audit data are used are carefully investigated. Empirical data are used to pinpoint the effects of a nonresponse bias in audit data. An informal analysis shows that the non-response bias is correlated with the factor price.
In efforts to improve "the return on capital invested in market and social research" models can play an important role. To this end models have to be built which can and will be used in private industry or in the public sector, that is, implementable models should be developed. In this paper we consider determinants of model implementation, i.e. the factors or dimensions that will contribute to the likelihood of implementation. These dimensions are put into three categories: 1.model-related dimensions; 2. organization-related dimensions; 3. implementation strategy dimensions. Chances of implementation of marketing models will be improved when these dimensions are carefully taken into account in all steps of the model building process. In order to contribute to these improvements, this process and the determinants of model implementation are summarised and discussed in this paper.
In this paper we concentrate on the explicit relation between models and market data. In order to formulate the subset of data to be stored in the date, bank three rather simple mathematical models are developed, namely: 1. A response model to explain the fluctuation in the demand for a certain product (primary demand); 2. Response model to explain the fluctuations in the selective demand, for a certain brand; 3. A policy model, this means a set of relations which supplement the response relations in order to evaluate the effects of a number of global decision alternatives.
In this paper we consider, optimal allocation of shelf space over article groups by a mass retailer. To this end the general structure of a number of mathematical marketing models is developed. These "risk-evaluation" models allocate shelf space over article groups and account for the risk which is inherent in the choice of article groups. The models are based on models which are used in financial management to analyse and select portfolios. A couple of these models are tested and some future directions of research in this area are indicated.