Moderating Factors of Immediate, Dynamic, and Long-run Cross-Price Effects

In this article the authors describe their comprehensive analysis of moderating factors of cross-brand effects of price changes and contribute to the literature in five major ways. (1) They consider an extensive set of potential variables influencing cross-brand effects of price changes. (2) They examine moderators for the immediate as well as the dynamic cross-price effect. (3) They decompose price into regular and promotional price and study both cross-price effects separately. (4) They compare their findings with previous literature on the moderating factors of own-price effects to understand which factors influence own-price elasticity through affecting brand switching. (5) The authors use an advanced Bayesian estimation technique. The results show evidence of the neighborhood price effect and suggest that it is conditional on whether the promoted brand is priced above or below its competitor. The promoted brand's activities turn out to play a much more important role in determining the cross-price promotional effects than its competitor's similar activities. The authors outline conditions when cross-brand post-promotion dips tend to occur. Finally, they argue that the brand choice portion of the overall own-brand effect of a promotion depends on the brand's marketing strategy and on category-specific characteristics

Modeling category-level purchase timing with brand-level marketing variables

Purchase timing of households is usually modeled at the category level. Marketing efforts are however only available at the brand level. Hence, to describe category-level interpurchase times using marketing efforts one has to construct a category-level measure of marketing efforts from the marketing mix of individual brands. In this paper we discuss two standard approaches suggested in the literature to solve this problem, that is, using individual choice shares as weights to average the marketing mix, and the inclusive value approach. Additionally, we propose three alternative novel solutions, which have less limitations than the two standard approaches. The new approaches use brand preferences following from a brand choice model to capture the relevance of the marketing mix of individual brands. One of these approaches integrates the purchase timing model with a brand preference model. To empirically compare the two standard and the three new approaches, we consider household scanner data in three product categories. One of the main conclusions is that the inclusive value approach performs worse than the other approaches. This holds in-sample as well as out-of-sample. The performance of the individual choice share approach is best unless one allows for unobserved heterogeneity in the brand choice models, in which case the three new approaches based on modeled brand preferences are superior

Advanced Econometric Marketing Models

De recente toename in de beschikbaarheid van grote marketingdatabases heeft geleid tot een groei in de vraag naar geschikte econometrische modellen. Over het algemeen bevatten deze databases gegevens over de voorkeuren van consumenten. Deze voorkeuren kunnen worden weergegeven door marktaandelen, totale verkopen of merkkeuzebeslissingen. Econometrische modellen vormen handige hulpmiddelen om nuttige informatie over consumentengedrag uit deze databases te halen. In dit proefschrift bespreken wij econometrische modellen voor marktaandelen, tussenaankooptijden en merkkeuzebeslissingen. Deze modellen kunnen bijvoorbeeld worden gebruikt om inzicht te verkrijgen in het effect van marketinginstrumenten op het gedrag van consumenten. Voorbeelden van de onderwerpen die worden besproken zijn heterogeniteit in beslissingsprocessen en de ontwikkeling van eenvoudig te interpreteren modellen voor dynamische eigenschappen van marktaandelen en tussenaankooptijden. Daarnaast leveren wij een bijdrage aan de econometrische literatuur door de uitbreiding en ontwikkeling van modellen en schattingstechnieken.The present availability of large databases in marketing, concerning, for example, store-level sales or individual purchases, has led to an increased demand for appropriate econometric models to deal with these data. The typical database contains information on revealed preferences, measured by for example sales, market shares or brand choices. In this thesis we study econometric models for some of these series, to be precise, we consider market shares, purchase timing and brand choices. These models allow us to, for example, gain insight into the effect of marketing instruments on consumer behavior. Examples of topics we discuss are heterogeneity in decision processes and the development of easily interpretable models to capture dynamical features in market shares and interpurchase times. Additionally, we contribute to the econometric literature by

Boosting business with data analysis

__Abstract__ Pretty much every modern organisation collects a mountain of data on a daily basis as it goes about its business. But all that data is of little real value unless it is properly analysed and used to anticipate client behaviour and needs

Impulse-response analysis of the market share attraction model

We propose a simulation-based technique to calculate impulse-response functions and their confidence intervals in a market share attraction model [MCI]. As an MCI model implies a reduced form model for the logs of relative market shares, simulation techniques have to be used to obtain the impulse-responses for the levels of the market shares. We apply the technique to an MCI model for a five-brand detergent market. We illustrate how impulse-response functions can help to interpret the estimated model. In particular, the competitive and dynamic structure of the model can be analyzed

Random Coefficient Logit Model for Large Datasets

We present an approach for analyzing market shares and products price elasticities based on large datasets containing aggregate sales data for many products, several markets and for relatively long time periods. We consider the recently proposed Bayesian approach of Jiang et al [Jiang, Renna, Machanda, Puneet and Peter Rossi, 2009. Journal of Econometrics 149 (2) 136-148] and we extend their method in four directions. First, we reduce the dimensionality of the covariance matrix of the random effects by using a factor structure. The dimension reduction can be substantial depending on the number of common factors and the number of products. Second, we parametrize the covariance matrix in terms of correlations and standard deviations, like Barnard et al. [Barnard, John, McCulloch, Robert and Xiao-Li Meng, 2000. Statistica Sinica 10 1281-1311] and we present a Metropolis sampling scheme based on this specification. Third, we allow for long term trends in preferences using time-varying common factors. Inference on these factors is obtained using a simulation smoother for state space time series. Finally, we consider an attractive combination of priors applied to each market and globally to all markets to speed up computation time. The main advantage of this prior specification is that it let us estimate the random coefficients based on all data available. We study both simulated data and a real dataset containing several markets each consisting of 30 to 60 products and our method proves to be promising with immediate practical applicability

Seasonality on non-linear price effects in scanner-data based market-response models

Scanner data for fast moving consumer goods typically amount to panels of time series where both N and T are large. To reduce the number of parameters and to shrink parameters towards plausible and interpretable values, multi-level models turn out to be useful. Such models contain in the second level a stochastic model to describe the parameters in the first level. In this paper we propose such a model for weekly scanner data where we explicitly address (i) weekly seasonality in a limited number of yearly data and (ii) non-linear price effects due to historic reference prices. We discuss representation and inference and we propose an estimation method using Bayesian techniques. An illustration to a market-response model for 96 brands for about 8 years of weekly data shows the merits of our approach

Testing Earning Management

Earnings management to avoid earnings decreases and losses implies that the time series properties of the last quarter in the fiscal year differ from those of the other three quarters. We propose a simple parametric methodology to diagnose such differences. Application to a random sample of 390 firms in the Compustat database gives strong evidence of earnings management

Modelling the Diffusion of Scientific Publications

This paper illustrates that salient features of a panel of time series of annual citations can be captured by a Bass type diffusion model. We put forward an extended version of this diffusion model, where we consider the relation between key characteristics of the diffusion process and features of the articles. More specifically, parameters measuring citations’ ceiling and the timing of peak citations are correlated with specific features of the articles like the number of pages and the number of authors. Our approach amounts to a multi-level non-linear regression for a panel of time series. We illustrate our model for citations to articles that were published in Econometrica and the Journal of Econometrics. Amongst other things, we find that more references lead to more citations and that for the Journal of Econometrics peak citations of more recent articles tend to occur later

Forecasting Market Shares from Models for Sales

Dividing forecasts of brand sales by a forecast of category sales, when they are generated from brand specific sales-response models, renders biased forecasts of the brands' market shares. In this paper we therefore propose an easy-to-apply simulation-based method which results in unbiased forecasts of the market shares. An illustration for five tuna fish brands emphasizes the practical relevance of the advocated method