Knowledge that Transforms

The aim of this article is to discuss some fundamental problems with regard to the development of the basic plan, a means of turning the conception of marketing into action. The author explains the role of planning; he emphasizes the advantages of planning in organizing the business activities of the enterprise. In order to prove his point the author describes for the reader the fundamental functions of the business activities, connecting each of them with marketing. A special emphasis is put on mutual connections of such activities on the one hand and on the dependence of these functions from the rational organization and functioning of the marketing. The author discusses the elaboration of the marketing plan. He explains the marketing system, that enables the necessary model of planning to develop. In discussing the model (see the diagram) he elaborates in great detail the stages involved in creating the plan, and also elements of planning within each particular stage. He emphasizes that rational planning is based on the segmentation of the market, also explaining the procedure, characteristics and targets of such segmentation. In the end the author points at possible procedures in the conditioned forecasting of the market segments; he also writes about the organizational problems of planning. The article closes with a survey of the role and function of control and re-estimates.

New economic environment is characterized by domination of technological factor and by orientation to global market. Firms have to adopt new and different internal and external strategies in order to compete in such circumstances. Main components of new strategic orientation become pattern and relationship among different forms of investment. Domination of investment comparing to export is central feature of external firm strategy. Increasing importance of intangible investment and their complementarity to physical investment become core point of internal firm strategy.

Three decades ago in a classic article on marketing channels, McVey addressed the retailer's role in the marketing channel: The middleman (retailer) acts primarily as a purchasing agent for his customers, and only secondarily as a selling agent for his supplier. He is interested in selling any product which customers desire to buy for him (McVey I960). Trends in the retail sector in the U. S. and other developed countries around the world (Brown 1987) have shown that McVey's observation has become more apparent than ever u retailers have become increasingly powerful and indeed are the dominant force in many marketing channels.

In this paper author has discussed about both different approaches in the marketing literature and core comments about contents as well as profile of marketing activities. Modem marketing cannot be realized as an exchange only. Rather one could realize marketing process in the practice as special relationship between players in the market game. In such context author pays attention on different effects as well as output emerged from marketing activities.

Empirical research indicates that some consumers form price expectations which may impact their purchase behavior. While literature in operations research has built purchase policy models incorporating uncertain price expectations, these models have been built for commodities. Consumers face an environment with multiple brands. In this paper, we develop a model that incorporates consumer preferences and price expectations for multiple brands as determinants of normative consumer purchase behavior. The model demonstrates how commodity purchase policy models recognizing price uncertainty can be adapted to the study of multi-brand markets. The model is used to analyze the normative impact of changes in price promotion policies and holding costs on individual purchase behavior. It is also used in a Monte-Carlo market simulation that illustrates some scenarios where a post-promotion dip is more or less evident, and provides an explanation for the nonexistent post-promotion dip.

Multiattribute market share models (MMSM) using supplier, product, and buyer attributes as explanatory variables generally use a logit model structure. The literature indicates two basic types of approaches for estimating coefficients, regression type approaches generally using log-share ratios and a nonregression approach which is based on maximum-likelihood principles. This article discusses a nonregression estimation approach, a minimum discrimination information (MDI), whose formulation differs from MDI procedures previously applied to the marketing literature. The MDI formulation enables researchers to estimate a logit model with proportions as the dependent variable and is computationally easy because it uses the same estimators as an MLE approach which assumes independence of trials. Equivalence between MDI and MLE estimates, under certain conditions, are discussed. The estimation methods are applied to a real world data set. The results indicate the MDI/MLE method does best in predicting the market shares of the suppliers according to some basic error criteria both at disaggregate and aggregate levels.

This paper presents a new multidimensional scaling (MDS) methodology which operationalizes the Krumhansl (1978) distance-density model for the analysis of asymmetric proximity data. In Krumhansl's conceptualization, the symmetric Euclidean interbrand distances typically associated with the traditional MDS model are augmented by the spatial density around the brands in the derived space. This modification allows the distance-density model to accommodate many of the empirically observed violations of the metric axioms (such as asymmetry). An operationalization of the distance-density model is particularly attractive to marketers who often work with asymmetric brand switching data to investigate market structure and competition. We describe this new MDS procedure which is sufficiently flexible to fit a number of competing hypotheses of proximity. The algorithm employed for estimation is technically presented together with various program options. An analysis of an asymmetric brand switching matrix for automobiles is presented to illustrate the methodology. The results of this analysis are also compared with the solutions obtained from several of the currently available procedures for handling asymmetric proximity data. Finally, technical extensions to three-way analyses, hybrid models, etc., are discussed.

Motivated by poor performance of standard estimation methods in our application, the problem of modeling the diffusion of a new service (or a product) is considered without the assumption of a homogeneous population. The model consists of a two-stage procedure where customers receive purchase occasions according to a conventional diffusion model and at each purchase occasion they buy according to a binary choice model. This approach permits explicit incorporation of individual customer demographics and product attributes, whence one can study changes in diffusion as a function of changes in product attributes, prices, advertising, and customer demographics. The model has provided excellent results in a number of applications including the one on fax penetration reported here. In our applications this approach has been useful in market segmentation, studying the effect of marketing strategies, and in evaluating the effects of new service features. A limited validation is carried out to judge the forecasting performance of the model used in the fax example. A simulation study is carried out to compare the proposed solution with the result obtained by fitting the Bass model to a set of market segments separately. The simulation indicates that the proposed approach substantially improves over the aggregate fitting.

Multinomial logit models, especially those calibrated on scanner data, often use explanatory variables that are nonlinear functions of the parameters to be estimated. A common example is the smoothing constant in an exponentially weighted brand loyalty variable. Such parameters cannot be estimated directly using commercially available logit packages. We provide a simple iterative method for estimating nonlinear parameters at the same time as the usual linear coefficients. The procedure uses standard multinomial logit software and, in experience to date, converges rapidly. We prove that, under suitable conditions, the resulting parameter values are maximum likelihood estimates and show how to calculate asymptotic standard errors from normal computer output. Three applications illustrate the method in practice.

There are many products which are repeatedly purchased by consumers. In such cases it is likely that choice history, that is the sequence of choices made in the past, as well as marketing variables affect subsequent choice decisions. Attempts to model the effects of choice history have been generally based on the inclusion of variables that represent brand loyalty and/or variety seeking behavior. In this paper we present a model of dynamic choice behavior which is more general and incorporates four characteristics. The first characteristic labeled preference reinforcement and preference reduction represents loyalty and variety seeking. The second is the short-term reluctance of a consumer to move from the current brand (inertia) or the willingness to move to another brand (mobility). The third characteristic captures the effect of repetitive consumption (the long term effect) on inertia and mobility. The fourth characteristic incorporates the similarity or dissimilarity of choice alternatives. This is important in a dynamic model because choice on the current purchase occasion can be affected by whether a similar or dissimilar alternative was chosen on the previous occasion. Similarities of alternatives are represented in terms of distances. The effect of price on choice behavior is also modeled. Individual-level purchase data from a consumer panel are used to estimate a covariance probit and an independent probit specification of the model. From a substantive perspective the model gives interesting insights into the dynamics of choice behavior. The model predicts switches better than a benchmark model which incorporates only loyalty. In addition, it is superior to three benchmark models in overall predictive ability.