Todays business environment is going through fundamental changes. Market saturation, increased commoditisation, shorter product life cycles and above all increased competition and business risks are influencing the way organisations see and plan to approach their customers. Customer asset value is increasingly becoming a key topic and relationship marketing is rapidly gaining importance. At the same time data warehousing technology is enabling companies to store and access large amounts of (hopefully) clean data on their customers. Knowledge discovery algorithms allow the detection of hitherto unknown patterns within these data. This paper shows how the combination of soft data from customer surveys with hard data from databases can be combined within one single data model. This data model will allow powerful customer analyses, such as segmentations based on needs, profitability and attrition. Additionally causal modelling to find drivers of retention, forecasting up-selling potential, prospect acquisition and return on quality analyses will be reviewed.
When a company is developing several important new compounds and one is facing challenges in formulation development, it is important to establish whether the potential for the product with an alternative formulation would be viable in sales terms. The market research investigation, which was conducted in the United States, Japan and Europe with an overall total of 298 respondents, used an adaptive conjoint approach (ACA) together with several interesting interlocking questions to assess the importance of the dosage form. It was possible to assess and model market penetration which clearly showed the impact of not having the oral formulation. Despite this, the product based on the conjoint work backed up by evaluation of an outline profile, demonstrated a very significant sales opportunity. The project was a high profile study within Zeneca which helped to enhance the standing of marketing research input.
This paper reviews many of the problems associated with forecasting in China, discusses alternatives to get round these problems and then reports a case history using a novel approach to forecast sales of a valuable but high cost drug in China.
In this paper we concentrate on a forecasting model for the growth of sales. This model is very interesting, because predictions of the sales for specific shop types are very difficult to make. The models of costs and profits depend on the sales model and are not discussed here. The outline of this paper is as follows. First, the panel framework and corresponding issues are discussed in section 2. The next section describes the forecasting model for the yearly growth of retail sales and discusses the behaviour of the model, using the forecasting results of 1993. Finally, conclusions will be made with reference to the research performed.
The paper discusses the application of sales prediction techniques to new product development in Latin America. It shows that in packaged goods product fields, both of the two mainstream methodologies, the Laboratory Test Market exemplified by SENSOR, and the concept/product test exemplified by MicroTest, deliver very similar consumer responses in the Latin American context as those found in other markets, notably Europe. This gives confidence in the validity of the consumer data. The problems lie more is obtaining accurate measures of market size, distribution and consumer purchasing due to the incomplete coverage of retail audit and consumer panel services. The extension of prediction techniques to durables and services, implications for the Latin America context, are also discussed.
A number of different sales forecasting systems have been developed for predicting the sales volume potential of new products, particularly in the FMCG area. While most of these systems have a reliable track record for more conventional new products, they do not tend to be as good for innovative, image based products. This paper identifies some of the reasons why image based products are more difficult to deal with than most others. In particular it gives an example of a recent research project conducted by the authors in which a sales forecasting model was developed to this end. The model includes an application of catastrophe theory, a mathematical technique which helps explain discontinuities in behaviour.
This paper reviews the applicability of the main current market research methods which attempt to predict the sales potential of a new product mix in the field of fast-moving consumer goods. The high costs and risks of new product development have been amply documented elsewhere. It has become increasingly critical for manufacturers to obtain reliable sales predictions as early as possible in the development process, and a variety of methods have been used. These are outlined and their applicability discussed in section 2. Most of the methods under discussion have been presented and evaluated in detail in published papers. Over the last few years there has been a dramatic growth in the use of the group of techniques we term Simulated Test Markets, but there has been little published discussion of their applicability and power. Section 3 therefore discusses in some detail the types of new product marketing problem for which these methods are most suitable, and Section 4 illustrates how these techniques can be applied to specific new product evaluations, and how some apparent problems can be overcome. Section 5 summarizes and draws conclusions from the review.
Forecasting new Fast-Moving Consumer Goods (FMCG) sales potential prior to test market has been available, and quite successful for twenty -20 years. During the past eight years, much effort has been devoted to duplicating the forecasting success into non-FMCG areas such as financial services, subscription-type products or services, durable goods and other service areas. The authors have been requested by these non-FMCG marketers during this period to investigate the applicability of a modified standard concept and concept/product test procedure to calibrate the forecasting models. In this paper, we share these experiences with the European colleagues some case histories on testing procedure and Year 1 sales forecasting for these types of new services and products. Since each type of service or product is so different from one another, the test procedure needs to be modified to fit each case. However, the general principle, procedure and questionnaire sequence remain virtually the same.
In France, the new product sales forecasting activity has progressively left the area of measurements done in a real environment in favour of simulated test: those tests are usually cheaper, shorter and much more confidential. The main purpose of this paper is to show empirical evidences that : - Some of the underlying assumptions made a few years ago by the model-builders are to be reviewed today; - It is impossible to ignore definitely measurements done in real conditions.
Goelette II, which succeeds to Goelette I, is a sales forecasting system for dispatching at the time of each issue MARIE-CLAIRE Group magazines into the distribution network of the "Presse en France". The primary objective of the system is to keep an economically acceptable percentage of unsold copies without risking being out of stock organisation (N.M.P.P.). It is also an instrument of analysis permitting, through the use of seasonal coefficients, the grading in a given situation of a serie of published issues, and by the calculation of deviations between forecasts and actual achievements to put forward hypothesis concerning the influence of factors external to the model (coverage, competition, date of publication, etc.). Finally, it is a management tool permitting the execution of statistical analysis and controls necessary for the conduct of business.