Modeling Consideration Sets and Brand Choice Using Artificial Neural Networks

The concept of consideration sets makes brand choice a two-step process. House-holds first construct a consideration set which not necessarily includes all available brands and conditional on this set they make a final choice. In this paper we put forward a parametric econometric model for this two-step process, where we take into account that consideration sets usually are not observed. It turns out that our model is an artificial neural network, where the consideration set corresponds with the hidden layer. We discuss representation, parameter estimation and inference.We illustrate our model for the choice between six detergent brands and show that the model improves upon a one-step multinomial logit model, in terms of fit and out-of-sample forecasting.brand choice;consideration set;artificial neural network

The relationship between in-store marketing and observed sales for organic versus fair trade products

To stimulate sales of sustainable products, such as organic and fair trade products, retailers need to know whether their in-store instruments effectively enhance market shares. This study uses sales data and a multilevel modeling approach to explain the market shares of sustainable products according to shelf layout factors, price level, price promotions, and consumer demographics. It argues that the effect of these variables differs between organic versus fair trade products, as buying motives might differ, organic buyers tend to be more loyal, and price is a more informative signal of quality for organic products. Results show that the number of facings has a positive relationship with the market share of fair trade brands, but not with the market share of organic brands. The same holds for the price difference with the leading brand, which is important for fair trade brands but not for organic brands. In contrast, an arrangement of the product category by brand is associated with higher market share for organic brands but not for fair trade brands. Additionally, placement at eye level and clustering of items benefits both types of sustainable brands, whereas they appear to be not very sensitive to price promotions. Finally, higher sales of sustainable products are found in areas where the customer base is older and has a higher education level. Keywords Organic . Fair trade . Shelf layout . Price promotions . Market share . Sales dat

Modeling Unobserved Consideration Sets for Household Panel Data

We propose a new method to model consumers' consideration and choice processes. We develop a parsimonious probit type model for consideration and a multinomial probit model for choice, given consideration. Unlike earlier models of consideration ours is not prone to the curse of dimensionality, while we allow for very general structures of unobserved dependence in consideration among brands. In addition, our model allows for state dependence and marketing mix effects on consideration.Unique to this study is that we attempt to establish the validity of existing practice to infer consideration sets from observed choices in panel data. To this end, we use data collected in an on-line choice experiment involving interactive supermarket shelves and post-choice questionnaires to measure the choice protocol and stated consideration levels. We show with these experimental data that underlying consideration sets can be successfully retrieved from choice data alone and that there is substantial convergent validity of the stated and inferred consideration sets. We further find that consideration is a function of point-of-purchase marketing actions such as display and shelf space, and of consumer memory for recent choices.Next, we estimate the model on IRI panel data. We have three main results. First, compared with the single-stage probit model, promotion effects are larger and are inferred with smaller variances when they are included in the consideration stage of the two-stage model. Promotion effects are significant only in the two-stage model that includes consideration, whereas they are not in a single-stage choice model. Second, the price response curves of the two models are markedly diferent. The two-stage model offers a nice intuition for why promotional price response is different from regular price response. In addition and consistent with intuition, the two-stage model also implies that merchandizing has more effect on choice among those who did not buy the brand before than among those who already did. It is explained why a single-stage model does not harbor this feature. In fact, the single-stage model implies the opposite for smaller or more expensive brands. Third, we find that the consideration of brands does not covary greatly across brands once we take account of observed effects. Managerial implications and future research are also discussed.Consideration;choice;probit models

Consideration sets, intentions and the inclusion of "Don't know" in a two-stage model for voter choice

We present a statistical model for voter choice that incorporates a consideration set stage and final vote intention stage. The first stageinvolves a multivariate probit model for the vector of probabilities that a candidate or a party gets considered. The second stage of the model is a multinomial probit model for the actual choice. In both stages we use asexplanatory variables data on voter choice at the previous election, as well as socio-demographic respondent characteristics. Importantly, our modelexplicitly accounts for the three types of "missing data" encountered in polling. First, we include a no-vote option in the final vote intention stage. Second, the "do not know" response is assumed to arise from too little difference in the utility between the two most preferred options in the consideration set. Third, the "do not want to say" response is modelled as a missing observation on the most preferred alternative in the consideration set. Thus, we consider the missing data generating mechanism to be non-ignorable and build a model based on utility maximization to describe the voting intentions of these respondents. We illustrate the merits of the model as we have information on a sample of about 5000 individuals from the Netherlands for who we know how they voted last time (if at all), which parties they would consider for the upcoming election,and what their voting intention is. A unique feature of the data set is that information is available on actual individual voting behavior, measured at the day of election. We find that the inclusion of the consideration set stage in the model enables the user to make more precise inferences on the competitive structure in the political domain and to get better out-of-sample forecasts.Bayesian method;Choice model;Election data;Polling;Probit model

The impact of the introduction and use of an informational website on offline customer buying behavior

Do customers increase or decrease their spending in response to the introduction of an informational website? To answer this question, this study considers the effects of the introduction and use of an informational website by a large national retailer on offline customer buying behavior. More specifically, we study a website's effects on the number of shopping trips and the amount spent per category per shopping trip. The model is calibrated through the estimation of a Poisson model (shopping trips) and a type-II tobit model (the amount spent per category per shopping trip), with effect parameters that vary across customers. For the focal retailer, an informational website creates more bad than good news; most website visitors engage in fewer shopping trips and spend less in all product categories. The authors also compare the characteristics of shoppers who exhibit negative website effects with those few shoppers who show positive effects and thus derive key implications for research and practice

Interaction Between Shelf Layout and Marketing Effectiveness and Its Impact On Optimizing Shelf Arrangements

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 resu

Path Dependencies and the Long-term Effects of Routinized Marketing Decisions

The purpose of this paper is to discuss a simulation of marketing budgeting rules that is based on a simplified version of the market share attraction model. The budgeting rules are roughly equivalent to those that may be used in practice. The simulation illustrates the concept of path dependence in dynamic marketing systems and shows how it might result from decision rules potentially applied by marketers and retailers. Path dependence results from positive feedback in dynamic systems that imparts momentum to market choices. Where the potential for path dependence exists, there are implications for defining and measuring long-term effects of marketing decisions in a way that is meaningful to managers and researchers. In the simulations presented we show that limited retails assortment may contribute to path dependence when firms use either percentage-of-revenue rules or "market learning" experiments to set budgets. While other budgeting procedures (e.g., matching competition) may stabilize market share, this stability in the share dimension comes at the cost of instability for budgets and profits.marketing decisions;path dependencies

Studie-evaluaties en marktaandelen van universiteiten

Universiteiten doen hun best om hoge studentevaluaties te krijgen voor hun curriculum. Er wordt gewoonlijk vanuit gegaan dat hogere evaluaties een positief effect hebben op het marktaandeel van de universiteit. In dit artikel wordt aangetoond dat dit niet altijd zo hoeft te zijn

Sales Models For Many Items Using Attribute Data

Sales models are mainly used to analyze markets with a fairly small number of items, obtained after aggregating to the brand level. In practice one may require analyses at a more disaggregate level. For example, brand managers may be interested in a comparison across product attributes. For such an analysis the number of relevant items in the product category make commonly used sales models difficult to use as they would contain too many parameters. In this paper we propose a new model, which allows for the analysis of a market with many items while using only a moderate number of easily interpretable parameters. This is achieved by writing the sales model as a Hierarchical Bayes model. In this way we relate the marketing-mix effectiveness to item characteristics such as brand, package size, package type and shelf position. In this specification we do not have to impose restrictions on the competitive structure, as all items are allowed to have different own and cross elasticities. The parameters in the model are estimated 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 items or 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 of the model. We apply the model to a ketchup scanner data set with 23 items at the chain level. Our results indicate that the model fits the sales of most items very well

Modeling Consideration Sets and Brand Choice Using Artificial Neural Networks

The concept of consideration sets makes brand choice a two-step process. House-holds first construct a consideration set which not necessarily includes all available brands and conditional on this set they make a final choice. In this paper we put forward a parametric econometric model for this two-step process, where we take into account that consideration sets usually are not observed. It turns out that our model is an artificial neural network, where the consideration set corresponds with the hidden layer. We discuss representation, parameter estimation and inference. We illustrate our model for the choice between six detergent brands and show that the model improves upon a one-step multinomial logit model, in terms of fit and out-of-sample forecasting