A strategic analysis of market share for a non-seasonal packaged product

Review of Business, Spring, 2009 by Raja R. Vatti

Executive Summary

There are many skin cream products for women, and it is very difficult for a single brand to capture the major share of the market. The competition is intense to get the attention of the target audience, and therefore each brand strives to increase the rate at which women try new products and make repeat purchases, through advertising and relative pricing. The purpose of the current study is to quantify the impact of advertising dollars, share of advertising voice, and relative price in order to establish the proper strategic direction of the marketing activities in the efforts to increase or defend the market share of a brand. A nonlinear regression approach is proposed here to study the elasticities of pricing and advertising efforts. The frequently changing marketing environment, with its many new product introductions, would make the long historical data irrelevant, and therefore a few quarters of the immediate past had to be analyzed in order to arrive at conclusions that could be appropriate to the immediate future.

Introduction

Marketing managers make strategic decisions almost every quarter regarding the fixing of proper price levels and advertising expenses, in order to maintain or enhance their brand's market share under the constant onslaught of changing competitive marketing activities of existing and new brands in the market place. The brand manager wants to know how factors such as pricing, advertising, distribution, and product quality affect his brand's share of the marketplace, so that he can determine the correct response to boost his brand's image in the eyes of consumers.

Most of the published research is too complex for easy implementation or easy interpretation. Moreover, the results were based on cross-sectional data and analysis, which most of the time is not relevant to individual brand decisions. The cross-sectional models lack the reliable predictive ability to be useful for a marketing manager of one product.

From the researcher's point of view, they require a large amount of data and complex mathematical functions to quantify the impacts and interactions of activities, and as a consequence the approaches proposed become unwieldy and impractical. The cross-sectional data across brands within an industry, or across brands and industries, may help managers understand the various factors better, but would give no relevant assistance to a single brand.

In order for studies to be helpful in making individual brand decisions, the studies should attempt to analyze individual brands within each industry, and should identify the similarities and differences among brands and industries.

Some Past Market Share Models

There were many modeling approaches to predict the impact of various factors on market share and sales in various industries. The popular ones are linear, additive and attraction models. The most common problem encountered by these marketing strategy researchers is the limited availability of appropriate time series data, especially on competition. As a consequence, the number of factors included in a model was restricted, so the study was small. The emphasis on most published studies has been on a few factors such as price, distribution, advertising and promotions. For example, Bass (1969) concentrated on advertising; Lambin (1972) studied distribution and advertising, and Kuehn, Mcguire and Weiss (1966) looked at price and advertising.

Some authors, instead of making market share the dependent variable, considered the change in market share from the previous period as the dependent variable. Buzzell and Wiersema (1981) utilized this approach to study the reasons for fluctuations in market shares, and they also resorted to the cross-sectional analysis to get around the problem of limited data availability over time. The cross-sectional analysis has its own limitations because it is not relevant to obtain predictions and inferences about individual brands or markets. As a result, most published literature that had a predictive emphasis had to rely on less than sixteen time series observations for their analysis.

The published attraction models are based on Kotler's (1971) fundamental theorem of market share determination, which states that market shares of competitors will be proportional to their marketing efforts. The attraction models faced the same difficulties as the linear predictive time series models because these models require accurate information about the competition, which is not easy to get for many time periods. Kuehn, Mcguire and Weiss (1966) took the attraction modeling approach to analyze market shares. Brodie and Kluyver (1984) compared the Linear and Attraction Models in terms of predictive power.

There were also some published models to identify optimal levels of marketing activity mix, using a game theoretic approach called the Lanchester model of combat for competing brands. Wang and Wu (1974) used the model for competing advertising decisions.

Some authors felt that marketing activities impact market share, and market share in turn influences the levels of factors such as advertising expenditures. Therefore, they suggested the simultaneous econometric equation approach for studying the interactions. Bass (1969) was one of the early researchers who attempted to estimate the effects of mutual dependence between market share and exogenous variables.