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Abstract:
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, long-term effects, hierarchical bayes

Abstract:
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, long-term effects, hierarchical bayes

Abstract:
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.

Market share attraction model, model selection, estimation, diagnostics, forecasting

Abstract:
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.

Marketing-instrument effectiveness, structural heterogeneity, state dependence, multinomial logit, mixtures

Abstract:
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.

sales models, market shares, forecasting

Abstract:
Sales models are mainly used to analyze markets with afairly small number of items, obtained after aggregating to thebrand level. In practice one may require analyses at a moredisaggregate level. For example, brand managers may be interestedin a comparison across product attributes. For such an analysisthe number of relevant items in the product category make commonlyused sales models difficult to use as they would contain too manyparameters.In this paper we propose a new model, which allows for theanalysis of a market with many items while using only a moderatenumber of easily interpretable parameters. This is achieved bywriting the sales model as a Hierarchical Bayes model. In this waywe relate the marketing-mix effectiveness to item characteristicssuch as brand, package size, package type and shelf position. Inthis specification we do not have to impose restrictions on thecompetitive structure, as all items are allowed to have differentown and cross elasticities. The parameters in the model areestimated using Markov Chain Monte Carlo techniques.As a by-product this model allows to make predictions of sales levels and marketing-mix effectiveness of new to introduce itemsor of attribute changes. For example, one can assess the impact of changing the packaging from plastic to glass, on sales and price elasticity. Besides entering and changing products, our model also allows for items to leave the market.We consider the representation, specification and estimation ofthe model. We apply the model to a ketchup scanner data set with 23 items at the chain level. Our results indicate that the modelfits the sales of most items very well.

sales models, attribute data, SKU level analysis, hierarchical bayes, Markov Chain Monte Carlo

Abstract:
The authors put forward a sales response model to explain the differences in immediate and dynamic effects of promotional prices and regular prices on sales. The model consists of a vector autoregression rewritten in error-correction format which allows to disentangle the immediate effects from the dynamic effects. In a second level of the model, the immediate price elasticities, the cumulative promotional price elasticity and the long-run regular price elasticity are correlated with various brand-speciffic and category-speciffic characteristics. The model is applied to seven years of data on weekly sales of 100 different brands in 25 product categories. We find many significant moderating effects on the elasticity of price promotions. Brands in categories that are characterized by high price differentiation and that constitute a lower share of budget are less sensitive to price discounts. Deep price discounts turn out to increase the immediate price sensitivity of customers. We also find significant effects for the cumulative elasticity. The immediate effect of a regular price change is often close to zero. The long-run effect of such a decrease usually amounts to an increase in sales. This is especially true in categories characterized by a large price dispersion, frequent price promotions and hedonic, non-perishable products.

sales, vector autoregression, marketing mix, promotional and regular price, short and long-term effects, hierarchical bayes

Abstract:
Allocating the proper amount of shelf space to stock keeping units [SKUs] is an increasingly relevant and difficult topic for managers. Shelf space is a scarce resource and it has to be distributed across a larger and larger number of items. It is in particular important because the amount of space allocated to a specific item has a substantial impact on the sales level of that item. This relation between shelf space and sales has been widely documented in the literature. However, besides the amount of space, the exact location of the SKU on the shelf is also an important moderator of sales. At the same time, the effectiveness of marketing instruments of an SKU may also depend on the shelf layout. In practice, retailers recognize that these dependencies exist. However, they often revert to rules of thumb to actually arrange their shelf layout. We propose a new model to optimize shelf arrangements in which we use a complete set of shelf descriptors. The goal of the paper is twofold. First of all, we aim to gain insight into the dependencies of SKU sales and SKU marketing effectiveness on the shelf layout. Second, we use these insights to improve the shelf layout in a practical setting. The basis of our model is a standard sales equation that explains sales from item-specific marketing-effect parameters and intercepts. In a Hierarchical Bayes fashion, we augment this model with a second equation that relates the effect parameters to shelf and SKU descriptors. We estimate the parameters of the two-level model using Bayesian methodology, in particular Gibbs sampling. Next, we optimize the total profit over the shelf arrangement. Using the posterior draws from our Gibbs sampling algorithm, we can generate the probability distribution of sales and profit in the optimization period for any feasible shelf arrangement. To find the optimal shelf arrangement, we use simulated annealing. This heuristic approach has proven to be able to effectively search an enormous solution space. Our results indicate that our model is able to fit and forecast the sales levels quite accurately. Next, when applying the simulated annealing algorithm to the shelf layout, we appear to be able to increase profits for all the stores analyzed. We compare our approach to commonly used shelf optimization rules of thumb. Most sensible rules of thumb also increase expected profits (although not as much as our optimization algorithm). In particular, it is beneficial to put high-margin items close to the beginning of the aisle (or the “racetrack"). Finally, we provide managerial implications and directions for further research.

Shelf Management, Sales Models, Hierarchical Bayes, Markov Chain Monte Carlo, Simulated Annealing

Abstract:
To examine cross-country diffusion of new products, marketing researchers have to rely on a multivariate product growth model. We put forward such a model, and show that it is a natural extension of the original Bass (1969) model. We contrast our model with currently in use multivariate models and we show that inference is much easier and interpretation is straightforward. In fact, parameter estimation can be done using standard commercially available software. We illustrate the benefits of our model relative to other models in simulation experiments. An application to a three-country CD sales series shows the merits of our model in practice.

Diffusion, international marketing, econometric models

Abstract:
We examine how new products are priced over time, where we particularly look at sharp decreases in prices. New durable products like fashion, apparel, and videogames often show a significant price cut some time after the product’s introduction. We call this a price landing and we examine its drivers. Theory predicts that competitive effects or underperforming sales are drivers for such price landings. To our knowledge, however, a systematic empirical study of price landing is unavailable. To examine the drivers of significant price cuts of a new product, we consider a rich dataset concerning sales and prices of 1195 newly released videogames. Prior literature suggests that own sales, competitive sales, competitive prices or simply time could be such drivers. In this paper we put these suggestions to an empirical test. We put forward a mixture model that covers a set of pricing equations with the price landing moment and its speed as key parameters. Second, in a hierarchical model we explain the apparent heterogeneity across the products. Our main finding is that it is not sales thresholds but competition and time itself that makes managers decide to seriously cut prices.

pricing, pricing models, new products

Abstract:
We examine how new products are priced over time, where we particularly look at sharp decreases in prices. New durable products like fashion, apparel, and videogames often show a significant price cut some time after the product’s introduction. We call this a price landing and we examine its drivers. Theory predicts that competitive effects or underperforming sales are drivers for such price landings. To our knowledge, however, a systematic empirical study of price landing is unavailable. To examine the drivers of significant price cuts of a new product, we consider a rich dataset concerning sales and prices of 1195 newly released videogames. Prior literature suggests that own sales, competitive sales, competitive prices or simply time could be such drivers. In this paper we put these suggestions to an empirical test. We put forward a mixture model that covers a set of pricing equations with the price landing moment and its speed as key parameters. Second, in a hierarchical model we explain the apparent heterogeneity across the products. Our main finding is that it is not sales thresholds but competition and time itself that makes managers decide to seriously cut prices.

pricing, pricing models, new products

Abstract:
We examine how new products are priced over time, where we particularly look at sharp decreases in prices. New durable products like fashion, apparel, and videogames often show a significant price cut some time after the product's introduction. We call this a price landing and we examine its drivers. Theory predicts that competitive effects or underperforming sales are drivers for such price landings. To our knowledge, however, a systematic empirical study of price landing is unavailable. To examine the drivers of significant price cuts of a new product, we consider a rich dataset concerning sales and prices of 1195 newly released videogames. Prior literature suggests that own sales, competitive sales, competitive prices or simply time could be such drivers. In this paper we put these suggestions to an empirical test. We put forward a mixture model that covers a set of pricing equations with the price landing moment and its speed as key parameters. Second, in a hierarchical model we explain the apparent heterogeneity across the products. Our main finding is that it is not sales thresholds but competition and time itself that makes managers decide to seriously cut prices.

pricing, pricing models, new products

Abstract:
This article examines the global spill-over of foreign product introductions and takeoffs on a focal country's time-to-takeoff, using a novel data set of penetration data for 8 high tech products across 55 countries. It shows how foreign clout, the susceptibility to foreign influences, and inter-country distances affect global spill-over patterns. The authors find that foreign takeoffs, but not foreign introductions, accelerate a focal country's time-to-takeoff. The larger the country, the higher its economic wealth, and the more it exports, the more clout it has in the global spill-over process. In contrast, the poorer the country, the more tourists it receives and the higher its population density, the more susceptible it is to global spill-over effects. Cross-country spill-over effects are stronger the closer the countries are to one another, both geographically and economically, but not necessarily in terms of culture. The model the authors develop also quantifies the spill-over between each country-pair, allowing it to be asymmetric.

new product takeoff, hazard model, global, cross-country, spill-over

Abstract:
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.

cross-price elasticity, asymmetry, dynamic effects, hierarchical Bayes

Abstract:
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.

cross-price elasticity, asymmetry, dynamic effects, hierarchical Bayes