Adjusting Choice Models to Better Predict Market Behavior

The emergence of Bayesian methodology has facilitated respondent-level conjoint models, and deriving utilities from choice experiments has become very popular among those modeling product line decisions or new product introductions. This review begins with a paradox of why experimental choices should mirror market behavior despite clear differences in content, structure and motivation. It then addresses ways to design the choice tasks so that they are more likely to reflect market choices. Finally, it examines ways to model the results of the choice experiments to better mirror both underlying decision processes and potential market choices.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47012/1/11002_2005_Article_5885.pd

The Impact of Brand Quality on Shareholder Wealth

This study examines the impact of brand quality on three components of shareholder wealth: stock returns, systematic risk, and idiosyncratic risk. The study finds that brand quality enhances shareholder wealth insofar as unanticipated changes in brand quality are positively associated with stock returns and negatively related to changes in idiosyncratic risk. However, unanticipated changes in brand quality can also erode shareholder wealth because they have a positive association with changes in systematic risk. The study introduces a contingency theory view to the marketing-finance interface by analyzing the moderating role of two factors that are widely followed by investors. The results show an unanticipated increase (decrease) in current-period earnings enhances (depletes) the positive impact of unanticipated changes in brand quality on stock returns and mitigates (enhances) their deleterious effects on changes in systematic risk. Similarly, brand quality is more valuable for firms facing increasing competition (i.e., unanticipated decreases in industry concentration). The results are robust to endogeneity concerns and across alternative models. The authors conclude by discussing the nuanced implications of their findings for shareholder wealth, reporting brand quality to investors, and its use in employee evaluation

A THEORETICAL AND EMPIRICAL ANALYSIS OF THE OPTIMAL ADVERTISING STRATEGY.

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A Diffusion Model Incorporating Product Benefits, Price, Income and Information

We start by assuming that a major benefit of many new durable products such as dishwashers and microwave ovens is time savings. Others, such as VCRs, also enhance the value of our leisure time. Using a household production framework we demonstrate that a utility maximizing individual will have a reservation price for the product which is a function of the product benefits and his wage rate. By assuming that the wage rate has an extreme value distribution across the population, we are able to derive, for the aggregate process, an income-price dependent logistic adoption equation. We then allow for the possibility that certain eligible consumers may delay their purchase of the product because they are unaware that it exists, are suspicious of its quality, or expect its price to fall. Unawareness, uncertainty and hope for further price declines are assumed to decrease with the increase in the number of previous adopters. The resulting diffusion model has the property that the product life cycle phenomenon can be explained jointly or separately by the income-price process, and the awareness-uncertainty-expectations process. Using data on household income, on durable penetration within different income classes, and on first-purchase sales the aggregate diffusion model and its premises are tested and supported. It is found that both the income-price and awareness-uncertainty-expectations processes are at work. However, the dependence of the awareness-uncertainty-expectations delay process on the number of previous adopters (e.g., word-of-mouth type effect) exists only in certain product categories and is usually relatively weak. It is demonstrated that if the word-of-mouth type effects are weak, a price skimming strategy is optimal for a monopolist and also is likely to be implemented by oligopolists. The sales forecasting implications of the model also are pursued.durable products, diffusion of innovations, life cycle pricing

An Empirical Analysis of the Optimal Advertising Policy

This paper determines an optimal policy for investment in advertising for a firm that wishes to maximize its discounted profits. To that end, an integrated approach consisting of model formulation, empirical investigation, and optimization is carried out. A model of market share response to advertising is formulated as a first-order Markov process, with nonstationary transition probabilities. These probabilities are assumed to be a function of the advertising goodwill accumulated by the firm and its competitors. The model as specified is nonlinear in its parameters, and nonlinear regression techniques are applied to estimate them. It is shown that this nonlinear form offers, via likelihood ratio tests, a unique opportunity for testing the model, and in a resulting empirical test, the model is found to be consistent with the data. Given these empirical findings, an optimal advertising policy is derived by the use of optimal control theory. The managerial implications of the recommended multi-period policy are examined, and the policy's sensitivity to managerial inputs and economic conditions is analyzed and illustrated.

Evaluation of Salesforce Size and Productivity Through Efficient Frontier Benchmarking

The efficient operation of a salesforce is a critical element in the profitability of many firms. Three factors play key roles: the salesforce's size, its allocation and its productivity. This gives rise to the following questions: can salesforce performance be improved by (1) hiring more salespeople, (2) allocating them more effectively to the various sales districts and/or (3) improving salesperson productivity through better calling patterns in terms of consumers and product line items? The practice of most firms and the methodology used in most of the academic literature to address salesforce design and productivity questions is a “Bottom Up” approach. This approach starts with assessments by each salesperson of the sales and effort corresponding to each customer and prospect in their territory. These assessments are then aggregated to the territory, district and national levels. This paper takes an alternative “Top Down” approach. It is based on an estimated relationship between district level sales and salesforce size, effort and other variables. This more macro level decision tool can be used by management in parallel to, and as an objective check of, the more conventional and more subjective “Bottom Up” approach. We develop an efficient frontier methodology which allows us to estimate how total district sales respond to salesforce size, district potential and competitive activity in the firm's best performing districts. The methodology utilized is based on Data Envelopment Analysis (DEA) and yields a benchmark measure of each district's efficient frontier sales (sales assuming the district's salesforce allocates its effort as done in the best performing districts). Based on the estimated response function we discuss the three potential sources of increased profitability: closing the inefficiency gap of each of the lower performing districts, optimally reallocating the current salesforce to the various districts, and changing the current size of the salesforce to its optimal level. The inefficiency gap issue is addressed through comparison of the parameter estimates for the best districts obtained through our methodology with those of an average district sales response function obtained using regression analysis. This comparison points to an important methodological finding. The use of multiple estimation results may lead to an improved understanding of the phenomenon being studied (in our case, the identification of the likely causes of district productivity inefficiencies). The latter two sources of increased profitability, salesforce reallocation and changes in the current salesforce size, are addressed analytically given the district level efficient frontier sales response function. The proposed “Top Down” procedure using the efficient frontier methodology and the insights it provides are examined by evaluating the operations of two different salesforces, one selling manufacturing equipment and the other business equipment. In both cases, regression-based analysis would have resulted in a declaration that the status-quo was close to optimal, while the frontier-based analysis pointed out that strong gains were possible in certain districts. In particular, for both firms, the greatest increases in profit are obtained through improved salesforce efficiency in the lower performing districts, not through salesforce size or district allocation adjustments. At the more micro-level, a comparison of the frontier and regression parameters made it possible to identify which specific changes in the daily operations of the salesforces would allow the realization of these potential productivity gains. In our two cases this could be obtained through more emphasis on pursuing prospective accounts.salesforce, benchmarking, frontier estimation