Modeling dynamic effects of promotion on interpurchase times

In this paper we put forward a duration model to analyze the dynamic effects of marketing-mix variables on interpurchase times. We extend the accelerated failure-time model with an autoregressive structure. An important feature of our model is that it allows for different long-run and short-run effects of marketing-mix variables on interpurchase times. As marketing efforts usually change during the spells, we explicitly deal with time-varying covariates. Our empirical analysis of purchases in three different categories reveals that, for some segments of households, the short-run effects of marketing-mix variables are significantly different from the long-run effects.Dynamic duration model;Error-correction model;Time-varying covariates;Unobserved heterogeneity

Modeling Dynamic Effects of the Marketing Mix on Market Shares

To comprehend the competitive structure of a market, it is important to understand the short-run and long-run effects of the marketing mix on market shares. A useful model to link market shares with marketing-mix variables, like price and promotion, is the market share attraction model. In this paper we put forward a representation of the attraction model, which allows for explicitly disentangling long-run from short-run effects. Our model also contains a second level, in which these dynamic effects are correlated with various brand and product category characteristics.Based on the findings in for example Nijs et al. (2001), we postulate the expected signs of these correlations. We fit our resultant Hierarchical Bayes attraction model to data on seven categories in two geographical areas. This data set spans a total of 50 brands. Our main finding is that, in absolute sense, the short-run price elasticity usually exceeds the long-run effect. Moreover, we find that the longrun price effects are strongly correlated with relative price and coupon intensity of a brand.market shares;marketing mix;hierarchical bayes;long-term effects

Incorporating Responsiveness to Marketing Efforts When Modeling Brand Choice

In this paper we put forward a brand choice model which incorporates responsiveness to marketing efforts as a form of structural heterogeneity. We introduce two latent segments of households. The households in the first segment are assumed to respond to marketing efforts while households in the second segment do not do so. Whether a specific household is a member of the first or the second segment at a specific purchase occasion is described by household-specific characteristics and characteristics concerning buying behavior. Households may switch between responsiveness states over time.We compare the in- and out-of-sample performance of our model with various versions of the MNL model. We conclude that, while using the smallest amount of parameters, our model outperforms all MNL variants on forecasting. This, together with the face validity of our parameter results, leads us to believe that incorporating responsiveness seems to be a worthwhile exercise.mixtures;Marketing-instrument effectiveness;multinomial logit;state dependence;structural heterogeneity

Performance of Seasonal Adjustment Procedures: Simulation and Empirical Results

In this chapter we use a simulation experiment to examine whether theseasonal adjustment methods Census X12-ARIMA and TRAMO/SEATS effectivelyremove seasonality properties from time series data, while preserving otherfeatures like the stochastic trend. As data generating processes we use avariety of processes that are actually found in practice. These processesinclude constant seasonality, changing seasonal patterns due to seasonalunit roots and processes with periodically varying parameters. To check forseasonality, we consider tests for seasonal unit roots, for deterministicseasonality, for seasonality in the variance, and for periodicity in theparameters. Our simulation results show that both adjustment methods areable to remove stochastic seasonal patterns from the data with the exceptionof changing seasonal patterns due to periodicity in the parameters. Onaverage, the two methods perform equally well.

Econometric Analysis of the Market Share Attraction Model

Market share attraction models are useful tools for analyzing competitive structures. The models can be used to infer cross-effects of marketing-mix variables, but also the own effects can be adequately estimated while conditioning on competitive reactions. Important features of attraction models are that they incorporate that market shares sum to unity and that the market shares of individual brands are in between 0 and 1. Next to analyzing competitive structures, attraction models are also often considered for forecasting market shares. The econometric analysis of the market share attraction model has not received much attention. Topics as specification, diagnostics, estimation and forecasting have not been thoroughly discussed in the academic marketing literature. In this chapter we go through a range of these topics, and, along the lines, we indicate that there are ample opportunities to improve upon present-day practice.model selection;forecasting;Market share attraction model;diagnostics;estimation

Unification of step bunching phenomena on vicinal surfaces

We unify step bunching (SB) instabilities occurring under various conditions on crystal surfaces below roughening. We show that when attachment-detachment of atoms at step edges is the rate-limiting process, the SB of interacting, concentric circular steps is equivalent to the commonly observed SB of interacting straight steps under deposition, desorption, or drift. We derive a continuum Lagrangian partial differential equation, which is used to study the onset of instabilities for circular steps. These findings place on a common ground SB instabilities from numerical simulations for circular steps and experimental observations of straight steps

A hierarchical Bayes error correction model to explain dynamic effects

For promotional planning and market segmentation it is important to understand the short-run and long-run effects of the marketing mix on category and brand sales. In this paper we put forward a sales response model to explain the differences in short-run and long-run effects of promotions on sales. The model consists of a vector autoregression rewritten in error-correction format which allows us to disentangle the long-run effects from the short-run effects. In a second level of the model, we correlate the short-run and long-run elasticities with various brand-specific and category-specific characteristics. The model is applied to weekly sales of 100 different brands in 25 product categories. Our empirical results allow us to make generalizing statements on the dynamic effects of promotions in a statistically coherent way.vector autoregression;sales;hierarchical Bayes;short and long run effects

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