Daniel Klapper

Search Engine Advertising Effectiveness in a Multimedia Campaign

Search engine advertising has become a multibillion-dollar business and one of the dominant forms of advertising on the Internet. This study examines the effectiveness of search engine advertising within a multimedia campaign, with explicit consideration of the interaction effects between search engine advertising and television and banner advertising. An advertising tracking study with about 300 respondents interviewed before and about 4,700 respondents interviewed after the advertising campaign examines the effects on four consumer metrics: advertising awareness, brand awareness, brand image, and brand consumption. We estimate advertising effectiveness and control for correlations across the four ordinal response metrics using a multivariate ordered probit model. The results show that search engine advertising has significant effects on several consumer metrics, even among consumers who do not click on the sponsored advertisement. Television advertising also affects the consumer metrics. However, a negative interaction effect emerges between search engine advertising and television advertising. Banner advertising exerts a positive impact, but only in combination with television advertising. These substantial interaction effects indicate that firms must consider the investments in various media channels simultaneously when they design multimedia campaigns.

Forecasting market share using predicted values of competitive behavior: further empirical results

Abstract Forecasting is an important marketing activity for evaluating the expected performance of alternative marketing plans, especially in order to predict earnings, sales or market shares. The purpose of this paper is fourfold. Firstly, we develop and evaluate alternative econometric approaches to predict competitors’ future actions. Secondly, the forecasting performance of attraction models is compared to those of linear and multiplicative market share models not only if competitors’ actions are known a priori but also if competitors’ actions are forecasts. Thirdly, the effects of alternative structural specifications of attraction models on the forecasting accuracy are investigated. Finally, we reinvestigate the impact of OLS estimation versus GLS estimation on the forecasting performance. The adopted empirical methods account for the interdependence of marketing instruments. We also allow for competitive reactions up to 10 periods ago and introduce a new approach concentrating on so-called marketing events characterizing directly the contemporaneous choice of several promotional activities within a brand. Analyzing weekly scanner data from three markets we find that attraction models outperform the share predictions of the linear and multiplicative models even if competitors’ actions are forecast. This result is valid on the market and brand level. In addition, response models outperform the naive model on the market level irrespective of whether competitors’ actions are known a priori or if they are forecasts. On the brand level the superiority of response models over naive models diminishes though it still exists. With respect to the best method of predicting competitors’ actions it turns out that parsimonious specifications like autoregressive price predictions or binary logit models perform conveniently.

The analysis of price competition between corporate brands

Abstract The methodology developed in this paper provides a means to analyze price competition between corporate brands. Corporate brands are considered to be produced and marketed by the same company. We establish price competition from an array of cross-price elasticities across time, which provides the necessary information to uncover the competitive interaction effects between corporate brands. The cross-price elasticities across time form a three-mode three-way array. The Constrained TUCKALS3-approach is developed and introduced for the analysis of the complex array of cross-elasticities. The approach takes explicitly a priori information about the competitive reaction or pattern of the marketing activities in a competitive market into account. This new methodology will enable brand management to gain further and deeper insight into the competitive interaction effects in the market. The Constrained TUCKALS3-model parameters provide the basis to investigate cannibalistic effects between corporate brands and thus helps to improve the marketing-mix, especially the price management of these brands. Furthermore, the results of the Constrained TUCKALS3-approach can serve to determine idealized market share estimates for certain a priori defined competitive conditions. The applicability and the methodological advantages of this approach will be shown by an empirical study on the price competition between two corporate brands. The reported results provide managerial useful information for the development and improvement of marketing-mix-strategies of these corporate brands.

An econometric analysis of product variety impact on competitive market conduct in consumer goods markets

Abstract.Decisions on product variety are a central part of the (strategic) marketing planning process of many consumer goods manufacturers. However, we have only limited information about the effects of product variety on competitive market conduct and profitability. In this paper, we introduce a simple econometric methodology for studying market conduct in prices and variety between rival brands of consumer goods markets. Our study follows the recent trend in empirical industrial organization, it is fully structural and starts from the specification of demand and supply functions. We introduce a number of different game theoretic regimes and characterize the equilibrium of each of these games. The equilibrium of each game is considered to be unique. On the basis of non-nested model selection, we can identify the form of competitive market conduct that is most suitable for the underlying data. Our empirical study identifies Nash behavior in pricing and collusive behavior in variety among the two leading brands in the market. The estimated parameters offer theoretically founded insight into the competitive rules in the market and the impact of prices and variety on profits.

Determinants of Margins in the Distribution Channel: An Empirical Investigation

In this paper we describe how margins in the channel vary over time within a product category and identify the market, manufacturer, and retailer characteristics that explain this variation. To obtain the equilibrium margins, we explicitly model the behavior of the various agents in the marketplace. Because the behavior of the agents changes in response to changes in the economic environment, we observe shifts in the total channel margins and the way they are split between the channel members. We explain this variation by examining the impact of directly measurable factors on total margins in the distribution channel and the share of these margins that manufacturers and retailers obtain. We illustrate the proposed approach using data for the ground coffee category in Germany. Our empirical analysis demonstrates that while the market-level factors affect total margins in the channel, size and other characteristics of manufacturers and retailers have a larger impact on the way margins are split. Our findings have immediate implications for the product portfolios offered by manufacturers, the positioning of store brands, and the retail service level.

Optimal Pricing Strategy for Quantity Discount Promotions

referees for their helpful and constructive comments. Daniel Klapper is a Professor at Goethe-University Frankfurt, School of Business and Economics, Department of Marketing, Grueneburgplatz 1, 60323 Frankfurt am Main, Germany, Phone: ++49-69-798-34649, Fax: ++49-69-79-35001, E-Mail: dklapper@wiwi.unifrankfurt.de. Sebastian Oetzel is a Ph.D. Student at Goethe-University Frankfurt, School of Business and Economics, Department of Marketing, Grueneburgplatz 1, 60323 Frankfurt am Main, Germany, Phone: ++49-69-79834649, Fax: ++49-69-79-35001, E-Mail: oetzel@wiwi.unifrankfurt.de. Optimal Pricing Strategy for Quantity Discount Promotions

Product Variety and Competitive Pricing in Consumer Goods Markets

Daniel Klapper is Professor of Marketing at Johann Wolfgang Goethe-Universität Frankfurt, Mertonstr. 17, D-60054 Frankfurt am Main, Germany, Phone: +49-69-798-23161, Fax: +49-69-798-23167, E-Mail: dklapper@wiwi.unifrankfurt.de Toker Doganoglu is Assistant Professor at Ludwig-Maximilians-Universität München, Seminar für Ökonometrie, Finanzökonometrie und Statistik, Institut für Statistik, Akademiestr. 1/I, D-80799 München, Germany, Phone: +49-89-2180-3224, Fax: +49-89-2180-5044, E-Mail: toker@athene.stat.unimuenchen.de Product Variety and Competitive Pricing in Consumer Goods Markets

Freemium Pricing: Evidence from a Large-scale Field Experiment

Firms commonly run field experiments to improve their freemium pricing schemes. However, they often lack a framework for analysis that goes beyond directly measurable outcomes and focuses on longer term profit. We aim to fill this gap by structuring existing knowledge on freemium pricing into a stylized framework. We apply the proposed framework in the analysis of a field experiment that contrasts three variations of a freemium pricing scheme and comprises about 300,000 users of a software application. Our findings indicate that a reduction of free product features increases conversion as well as viral activity, but reduces usage – which is in line with the framework’s predictions. Additional back-of-the-envelope profit estimations suggest that managers were overly optimistic about positive externalities from usage and viral activity in their choice of pricing scheme, leading them to give too much of their product away for free. Our framework and its exemplary application can be a remedy.

Sharing in Social Networks: How Signalling Increases Product Appeal

fels received her Doctoral Degree at Goethe University Frankfurt, Grüneburgplatz 1, 60629 Frankfurt, Germany, Phone +49 (0)69 798-34637, Fax +49 (0)69 798-35001, E-Mail: hwvm@wiwi.unifrankfurt.de. Daniel Klapper is Professor of Marketing at Humboldt University Berlin, Unter den Linden 6, 10099 Berlin, Germany, Phone +49 (0)3

Combining Micro and Macro Data to Study Retailer Pricing in the Presence of State Dependence

Consumers of repeat-purchase goods have a higher probability of choosing products that they have purchased in the past. This form of persistence or state dependence has emerged in scanner panel data for many product categories. Considering the existence of state dependence by firms is important for the better understanding of consumer purchase behavior and pricing. If state dependence is a function of loyalty, then firms may want to engage in strategic pricing to control the evolution of preferences. However, manufacturers and retailers have limited access to scanner panel data. In addition, scanner panel data are often not suitable for use in pricing decisions because they provide price information only for those items that a consumer has purchased in a particular store and on a certain day. In this paper, we will show how firms can use readily available store-level scanner data in combination with tracking data, which firms routinely collect, to estimate the impact of state dependence on consumer purchase behavior and determine the resulting effect on the pricing decisions of firms. We model demand using a flexible, random coefficient logit model for aggregated data that takes into account the heterogeneity of brand perceptions and customer responses to pricing and promotions. This model also accounts for the possibility that competing brands exhibit flexible substitution patterns. The results indicate that consumers may change their purchase behavior if they have recently purchased a particular brand. We then use the demand side estimates on the supply side to show how retailer pricing and profitability are affected if a retailer does or does not anticipate state dependence in predicting consumer purchase behavior.