1 Introduction
Estimating consumer preferences among discrete choices has a long history in economics and marketing. Domencich and McFadden’s [1975] pioneering analysis of transportation choice articulated the benefits of using choice data to estimate latent parameters of user utility functions [see also Hausman and Wise, 1978]
: once estimated, a model of user utility can be used to analyze counterfactual scenarios, such as the impact of a change in price or of the introduction of an existing product to a new market. McFadden (1974) also highlighted strong assumptions implicit in using offtheshelf multinomial choice models to estimate preferences and introduced variants such as the nested logit that relaxed some of the strong assumptions (including “independent of irrelevant alternatives”).
Analysts have applied the discrete choice framework to a variety of different types of data sets, including aggregate, marketlevel data (see, e.g., Berry et al. [1995], Nevo [2001], and Petrin [2002])^{1}^{1}1This literature grapples with the challenge that to the extent prices vary across markets, the prices are often set in response to the market conditions in those markets. In addition, to the extent that products have quality characteristics that are unobserved to the econometrician, these unobserved quality characteristics may be correlated with the price., as well as data from individual choices for a crosssection of individuals. In this paper, we focus on models designed for a particularly rich type of data, consumer panel data, where the same consumer is observed making choices over a period of time. Supermarket scanner data is a classic example of this type of data, but ecommerce firms also collect panel data and use it to optimize their offerings and prices. Scanner data enables the analyst to enrich the analysis in a variety of ways, for example to account for dynamics (see Keane and Wasi [2013]) for a survey. The vast majority of the literature based on individual choice data focuses on one category^{2}^{2}2Throughout the paper we use category to refer to disjoint sets of products, such that products that are within the same category are partial substitutes. at the time, e.g. Ackerberg [2001, 2003] analyzes yogurt, Erdem et al. [2003] ketchup, Dubé [2004] soft drinks, and Hendel and Nevo [2006] detergent. Often these analyses focus on the impact of marketing interventions, such as advertising campaigns, coupons, or promotions.
In this paper, we analyze the demand for a large number of categories in parallel. This approach has a number of advantages. First, there is the potential for large efficiency gains in pooling information across categories if the consumer’s preferences are related across categories. For example, the consumer’s sensitivity to price may be related across categories, and there may be attributes of products that are common across categories (such as being organic, convenient, healthy, or spicy). These efficiency gains are likely to be particularly pronounced for less commonly purchased items. Even among the top 100 categories in a supermarket, the baseline probability of purchasing an item in the category is very low on any particular trip, and there are thousands of categories in a typical store.
^{3}^{3}3In the sample we use in our empirical exercise, among the top 123 categories the average category is only purchased on 3.7% of shopping trips. Only Milk, Lunch Bread, and Tomatoes are purchased on more than 15% of trips. Things are even sparser at the individual UPC level. The average purchase rate is 0.36% and only one, Avocados, is purchased in more than 2% of the trips in our TuesdayWednesday sample. For ecommerce, there may be millions of products, most of which are rarely or never purchased by any particular consumer. But by pooling data across categories, it is possible to make personalized predictions about purchasing, even for categories in which the consumer has not purchased in the past.A second advantage of analyzing many categories at once is that from the perspective of marketing, it is crucial for retailers to understand their consumers in terms of what drives their overall demand at the store, not just for individual products. For example, there may be products that are very important to highvolume shoppers, but where they are priceelastic; avoiding stockouts on those products and offering competitive prices may be very important in storetostore competition. Although this paper does not offer a complete model of consumers’ choice across stores, we view the demand model we introduce as an important building block for such a model.
Our model makes use of recent advances in machine learning and scalable Bayesian modeling to generate a model of consumer demand. Our approach learns a concise representation of consumer preferences across multiple product categories that allows for rich (latent, i.e. unobservable) heterogeneity in products as well as preferences across consumers. Our model assumes that consumers select a single item from a given category (a strong form of substitution, where in the empirical analysis we drop categories that have large violations of this assumption), and further assumes that purchases are independent across categories (thus ignoring budget constraints, which we argue are less likely to bind at the level of an individual shopping trip). From a machine learning perspective, we extend matrix factorization techniques developed by Gopalan et al. [2013] to focus on the case of shopping, which requires incorporating timevarying prices and demand shifts as well as an appropriate functional form. We introduce “sessions,” where prices and the availability of products are constant within a session; but these elements may change across sessions. It is common in stores for products to go in and out of stock, or to be promoted in various ways; accounting for these factors is helpful in allowing the model to estimate the parameters that are most useful for counterfactual inference. Finally, relative to the machine learning approach, we tune our model hyperparameters on the basis of performance on counterfactual estimates, and we show that this makes a difference relative to focusing on the typical machine learning objective, prediction quality. We are able to do this because our data contains a large number of distinct price changes and examples of products going out of stock, and thus we can hold out data related to some of these changes and evaluate performance of the model in predicting the impact of those changes.
The primitives of our model include the latent characteristics of products (a vector, whose dimension is tuned in the process of estimation on the basis of goodness of fit), as well as each consumer’s latent preferences for each dimension. These latent characteristics and preferences are constant over time. In addition, we do not assume that the consumer’s price sensitivity is constant across products; instead, each product has a vector of latent characteristics that relate to consumers’ price sensitivity toward the product, and each consumer’s price sensitivity is the inner product of a consumerspecific latent vector and the product’s latent characteristics that relate to price sensitivity. Thus, both the consumer mean utility and the consumer price sensitivity varies at the individual consumer level, as well as across products within a consumer.
^{4}^{4}4We also ran alternative specifications with the per consumer price coefficients restricted to be the same across all products, however this lead to a substantial reduction along both the predictive and counterfactual fit measures of performance. In addition, the model includes controls for weekspecific demand for products. We use a Bayesian approach, so our model produces a posterior distribution over each latent factor.We apply our model to data from a single supermarket over a period of 23 months, where we observe the same consumers shopping over time. The data originate from shopper loyalty cards. Unlike many panels collected by third parties, the data is available at the level of the trip rather than aggregated to the weekly level, and we see shopping at a high enough frequency to identify the timing of price changes and stockouts. In particular, we observe many weeks where prices change at midnight on Tuesday night; and otherwise, behavior is very similar between Tuesday and Wednesday. This allows us to identify the effects of price changes and be able to make counterfactual predictions. We conduct a variety of tests that assess our identification strategy, and in a departure from the machine learning literature on which we build, we evaluate model fit on the basis of the model’s ability to predict how behavior changes when prices change. We compare our model to a variety of commonlyused categorybycategory models, including nested logit and mixed logit, showing that our model performs better both in terms of overall ability to fit on a representative test set, but also in terms of the model’s ability to predict responses to price changes. We examine both ownprice and crossprice effects. We also examine whether the heterogeneity incorporated in our model is spurious or predictive by showing that our model tends to produce more heterogeneity across groups in terms of ownprice and crossprice elasticities, and using heldout data, we show that this heterogeneity predicts heterogeneity in consumer response to price changes. We also show that our model has key advantages in terms of being able to predict the behavior of consumers who have rarely or never purchased in the training data.
2 Related Work
In the traditional discrete choice literature, it has become common to include many latent variables describing user preferences; for example, in a mixed logit model, it is common to include a useritem random effect, as well as individualspecific preference parameters for price and other observed item attributes [e.g. Berry et al., 2004, Train, 2009]. However, it is less common to model latent item attributes, other than perhaps a single dimension (quality). There are, however, several lines of work that estimate richer models that incorporate latent user characteristics, exploiting panel data.^{5}^{5}5In data from a single crosssection of consumers, Athey and Imbens [2007] show that only a single latent variable can be identified (or two if utility is restricted to be monotone in each) without functional form restrictions, arguing that panel data is critical to uncover common latent characteristics of products.
An early example is the “market mapping” literature, where each product is described as a vector of latent attributes. A market map can be used for a variety of exercises; for example, one can consider the entry of a new product into a position in the product space and forecast which consumers are likely to buy it. Empirical applications have also typically focused on a single category, such as laundry detergent (Elrod and Keane [1995] and Chintagunta [1994]).^{6}^{6}6Elrod and Keane
use a factor analytic probit model with normally distributed preferences, whereas
Chintagunta uses a logit model with discrete segments of consumer types.Elrod [1988a, b] use logit models to estimate up to two latent attributes and the distribution of consumer preferences. The former study uses a linear utility specification, and the latter uses an idealpoint model. Outside of shopping, there are several other social science applications making use of panel data to estimate latent item attributes and individual preferences. Goettler and Shachar [2001] study television viewing for a panel of users, and attempt to estimate latent attributes of television shows based on this panel. Another application area is political science, where panel data on legislators’ voting decisions is used to uncover their preferences and the latent characteristics of legislation. Poole and Rosenthal [1985] use a transformed logit model to estimate both the locations of legislators’ ideal points and the locations of legislative bills in a unidimensional attribute space.There has been some progress on estimating multiplediscrete choice models in which consumers choose more than one of a single item [e.g. Hendel, 1999, Kim et al., 2002, Dubé, 2004], however a substantial portion of the literature continues to focus on categories in which the unitdemand assumption plausibly holds.
Despite the extensive literature making use of consumer panel data, very little literature in economics and marketing attempts to consider multiple categories simultaneously. A few papers study demand for bundles of products, where the products may be substitutes or complements, and where the models attempt to estimate the nature of interaction effects. These models are limited by the curse of dimensionality and generally have difficulty incorporating more than two or three categories (e.g.
Athey and Stern [1998]; see Berry et al. [2014] for a review). The only paper we are aware of that estimates interaction effects across many categories is Ruiz et al. [2017] which uses a similar approach to this paper, but focuses on estimating interactions rather than exploiting available information about the category structure. We discuss this in more detail below.Our model focuses on sharing information about consumer preferences for item attributes across categories where consumer preferences are additively separable across categories. Our model differs from the past literature in social sciences in the techniques used and in the scale and complexity of the model. In order to flexibly estimate consumer heterogeneity across multiple product categories, this paper builds on the Bayesian Hierarchical Poisson Factorization (HPF) model proposed in Gopalan et al. [2013]. The HPF model predicts the preferences each user (decision maker) has for each item (product) based on a sum of the product of a latent vector of item characteristics and a latent vector of consumer preferences for each of those item characteristics.^{7}^{7}7This approach has similarities to the econometrics literature on “interactive fixed effects models” although that literature has focused primarily on decomposing common trends across individuals over time rather than identifying common preferences for products across individuals [Moon and Weidner, 2015, Moon et al., 2014, Bai, 2009]
Gopalan et al. [2013] demonstrate that the HPF model can make accurate predictions^{8}^{8}8As is standard in the machine learning literature, the accuracy is estimated on a “held out” or “test” data set that is not used during the training of the model. This is a more accurate way to evaluate how well a model will be able to make predictions on new data that has not yet been observed. across a wide variety of contexts, including Netflix movies, New York Times articles, and scientific articles in a researcher’s Mendeley account. Despite the reputation that Bayesian methods have for being slow computationally, this model is scalable across large data set sizes^{9}^{9}9For example, when trained on Netflix data with 480,000 users, 17,700 movies, and 100 million observations, they report the model took 13 hours to converge on a single CPU. due to its use of meanfield variational inference to approximate the computationally intractable exact posterior.
The HPF model is related to the extensive recommender systems literature that uses matrixfactorizationbased techniques to predict what items (movies, links, articles, search results, etc.) that a user will enjoy based on their previous choice behavior [Koren et al., 2009, Bobadilla et al., 2013]. A core insight of this literature is that it is often very effective to predict a user’s interests based on the preferences of other users who have similar tastes. These approaches try to find a lower dimensional approximation of the full matrix of user and item preferences.^{10}^{10}10The matrix has one row for each user and one column for each item. The entry corresponds to how much user “likes” item . We often only observe some of the entries of this large matrix, and would like to make predictions for the unobserved entries. e.g. predict how much a user will like a movie that they haven’t watched or rated yet. The resulting factorization often is able to make accurate predictions and can also provide an interpretable representation of the user preferences in the data. Jacobs et al. [2016] applies a related approach known as latent Dirichlet allocation (LDA) towards the task of predicting consumer purchases in online markets with large product assortments. However, their approach stays closer to the existing recommender systems literature. They focus on predicting which new products a consumer will buy, rather than focusing on predicting responses to price changes or patterns of substitution between similar products as we do in this paper.
In our empirical exercise using data from a supermarket loyalty program, we show that simultaneously modeling consumers’ decisions across multiple product categories helps improve our ability to characterize individual level preferences relative to estimating preferences in each category independently. This has similarities to the growing area of transfer learning in machine learning
[Pan and Yang, 2010, Oquab et al., 2014] in which training a model on one domain (e.g. one for which large amounts of data are available) can help improve the model’s ability to make predictions in a different domain (potentially one for which less data is available). This insight may have applications in other economics and marketing contexts, e.g. data on consumers’ purchasing decisions in one domain in which purchases are frequent (grocery stores) may be able to improve our ability to estimate consumer demand in a seemingly unrelated domain in which purchases are much less frequent (cars).A closely related paper to this one is Ruiz et al. [2017], which uses a similar approach in terms of matrix factorization, but focuses on estimating whether items are substitutes or complements, rather than exploiting observed information about the product hierarchy. This paper differs in that it fits more directly into the existing literature on choice in marketing and economics. This paper also systematically evaluates the assumptions required to identify price elasticities, and conducts a comparison of alternative category by category demand models.
Another closely related paper is Wan et al. [2017]
which uses a latent factorization model with three stages. In the first stage, the consumer makes a binary choice for each category about whether or not to purchase something from the category. Next the consumer makes a multinomial choice of which item from the category to purchase. Finally the consumer makes a choice of how many of this item to purchase, drawn from a Poisson distribution. Latent factorization is carried out independently for each stage. Our paper differs in the linkage we propose between the utilities of the items in a category and the decision of whether or not to purchase something from the category. Our paper is also more systematic in the evaluation of the ability of the model to make counterfactual predictions about changes in individual level and aggregate demand in response to own price and cross price changes as well as to changes in item availability caused by stock outs.
3 The Model
3.1 Random Utility Models and Independence of Irrelevant Alternatives
In this section we introduce the canonical Random Utility Model (RUM) that we will use in this paper. Consider shopper on a shopping trip at time . In each product category (e.g. bananas, laundry detergent, yogurt) there are products to choose between. Within each category, the shopper has unit demand and will purchase at most one item. To simplify the model, we assume that the product categories are disjoint and that there is no substitution or complementarity between products in separate categories. The shopper purchases the item that provides her with the highest utility among the options in the category.
If we assume that the are drawn i.i.d. from an Extreme Value Type 1 distribution^{11}^{11}11Also known as the Gumbel distribution., then
(1) 
The ratio of purchase probabilities between any two items and in the same category depend only on the ratio of their values.
(2) 
Similarly if we consider some subset of the products in a category , then conditional on the purchase of an item from the subset, the relative purchase probability for item is given by
(3) 
This property is known as “Independence of Irrelevant Alternatives” (IIA). IIA is clearly inappropriate in many cases. However, much of the problematic implications of IIA are caused by insufficient allowance for heterogeneity in preferences across the decision makers. If it were feasible to estimate each independently, the proportional substitution implied by IIA is much less restrictive when applied at the individual level.^{12}^{12}12Steenburgh and Ainslie [2013] provides a nice overview of the degree to which allowing heterogeneity in preferences reduces (but does not eliminate) the problems of the homogeneous logit model.
The IIA property however has the advantage of providing substantial leeway to the econometrician in how to approach the estimation. As Train [2009, p. 53] points out, it is possible to generate consistent estimates of the model parameters using any subset of the alternatives. For example, it is possible to estimate the ratio of the for any two products based on data for the purchases of those two items regardless of whatever other items might have been available in the category. In particular, after normalizing the utility of one product to be , then can be recovered from . This approach has been used for estimating multinomial logit models with individual level fixed effects, where the analyst estimates a series of binary logit models using only data where one of two items is purchased, and then parameter estimates are averaged to form a final estimate.
3.2 Estimation of the RUM using Nested Hierarchical Factorization Model
3.2.1 Nested Factorization Model
The Nested Factorization model builds on the Hierarchical Poisson Factorization (HPF) model proposed by Gopalan et al. [2013], adding a number of additional features important for capturing shopping behavior. It also extends the Time Travel Factorization Model (TTFM) introduced in Athey et al. [2018]. A core difference is that while the TTFM model predicted choice of restaurant conditional on the choice to go out to eat, whereas in this paper we wish to predict the unconditional purchase probabilities for each product, since this is critical for the stores ability to make pricing decisions.^{13}^{13}13We predict whether or not the shopper will make a purchase from each product category and if so, which product will she choose. These predictions are conditional on the shopper’s decision to visit the store, which we do not model.
In the grocery context, the typical purchase rates for any particular product are almost always less than 1%, which makes the assumption of IIA problematic with respect to the outside good, especially if we are interested in cross price elasticities. If consumer purchases nothing from a category on 90% of trips, then assuming IIA implies that any decrease in sales caused by a price increase will almost never cause a consumer to purchase a different product from the same category. Under this assumption, because granulated sugar is purchased relatively infrequently, a price increase for one brand of sugar will mostly cause consumers to buy no sugar at all, with very few consumers to substitute to a different brand. To address this concern, we introduce a structure similar to a nested logistic regression. This gives the model flexibility to fit the degree to which consumers substitute between different products within a product category rather than deciding to purchase nothing instead. In this particular application, we use a simple nesting structure with all of the products in a category in one nest and then a second nest with the outside good for the category. However the model can allow for other richer nesting structures, since it can be implemented via repeated runs of the same code used in the TTFM model. First a model is fit predicting choice of UPC conditional on the purchase of a product from the UPC’s category. The results of this model are aggregated to calculate a usercategory specific “inclusive value” term, which is used as an input into a second run that predicts from which categories each user will make purchases.
3.2.2 UPC Choice
To estimate the Nested Factorization model we first train a model to predict the consumer’s choice of which products they would purchase conditional on the decision to purchase one item from the corresponding product category. For example if the consumer has decided to purchase yogurt, which brand and size of yogurt will she select. The mapping from utility values to conditional choice probabilities follows the standard multinomial logit form that arises from assuming a Extreme Value Type 1 distribution for . Our model differs from the standard multinomial logit in that it allows for rich heterogeneity in preferences and price responsiveness across consumers and across items.
Similar to the HPF model, the Nested Factorization model incorporates latent item characteristics () as well as latent user preferences for the latent item characteristics (). The Nested Factorization model differs in that the HPF model did not incorporate any consumer or item level covariates, nor did it allow for any time varying characteristics such as price or product availability (e.g. a product being out of stock). These extensions allow for predictions at the level of individual shopping trips and for predictions of the patterns of substitution between similar products caused by price changes and changes to product availability. We assume consumers have latent preferences for observable item characteristics (), while observable user characteristics affect user preferences differentially for each product (). We allow for heterogeneity in price elasticities across users and items that depends on latent item characteristics () and latent user characteristics (). In addition, we allow for certain items to be “out of stock” or “unavailable” on a particular shopping trip ( if the item is out of stock, while otherwise).
(4) 
(5) 
3.2.3 Category Choice
We model the consumer’s choice of whether or not to purchase something from each category of goods as a series of binary choices that depends on the utility values of items in the category, through their “inclusive value” . One interpretation of the inclusive value is to notice that the expectation (over the realizations of the ) of the utility of the best option in the category is equal to the inclusive value for the category.^{14}^{14}14If the are a standard Extreme Value Type 1 distribution, then there will be an extra term added which in practice does not matter since it can be absorbed into the constant term. Alternatively we can define to get rid of the extra term without affecting any of the choice probabilities.
(6) 
The coefficient on is allowed to vary across users and categories by means of a latent factorization. ^{1}^{1}todo: 1Is it worth discussing the interpretation of the IV coef? e.g. when it is 1, then we have a standard unnested logit, when it is 0 changes in the price of a product will never cause you to leave the nest. Observable user characterstics, category characterstics, and a latent factorization linearly enter the utility function for the choice to make a purchase from each category. In addition we control for seasonal trends and day of week effects in order to separately control for time trends in each category.^{15}^{15}15In order to reuse the variational inference code from the UPC level model, we fit the category choice model as if there were two products for each category: the “inside good” with calculated based on the UPCs in the category, and “outside good” with . We then can transform the estimated parameters by subtracting the outside good’s parameters from both, to be equivalent with a model with the utility of the outside good equal to 0.
(7) 
(8) 
3.2.4 Estimation with Variational Bayes and Stochastic Gradient Descent
To estimate the Nested Factorization model, we build on the approach described in Ruiz et al. [2017] and Athey et al. [2018] to fit a hierarchical Bayesian model to the structure described in Section 3.2.1. As is common in many Bayesian models, the exact posterior distribution over the latent variables does not have a closedform solution. We instead approximate the posterior distribution using variational inference, which is typically substantially faster to compute on large scale Bayesian problems than classical methods such as Markov Monte Carlo sampling [Blei et al., 2017]. In variational inference, we posit a parameterized family of distributions over the latent variables of the model and then use stochastic optimization to find a value of such that
is “close” to the true posterior as measured by the KullbackLeibler divergence. See
Blei et al. [2017], Ruiz et al. [2017], Athey et al. [2018] for a more detailed explanation.The core difference in the model in this paper is the nested decision making process across multiple distinct product categories. In order to fit the Nested Factorization using essentially the same computational code as in Athey et al. [2018], we manipulate the data input to the model in a two stage process. First, we fit the factorized model described in Section 3.2.2 to learn a UPC level model using only the data from product categories the household actually made a purchase from on each particular shopping trip. Conditional on purchasing something from a product category, the model predicts which particular UPC the household will choose. If a household purchases from 5 categories on a particular shopping trip, only the UPC choices from those 5 categories enter the likelihood. We then create a second data set that contains each household’s decisions of whether or not to buy something from each product category on each trip they make to the store. This second data set depends on estimated parameters from the UPC through the “inclusive value” term as described in equation 6 which depends on the household’s predicted utilities for each of the UPCs in the product category.
4 Supermarket Application
4.1 Data
We apply the Nested Factorization model to scanner panel data from one store in a large national grocery store chain, using a data set originally assembled by Che et al. [2012]. This store is located in an isolated mountain region and has no other large grocery competitors within a 5 mile radius. For each transaction that a loyaltycard household makes between May 2005 and March 2007, we observe the price and quantity of each product purchased. In addition we incorporate several household demographic variables that the store has compiled from a variety of sources, including estimated age, gender, income, and household size (there are additional demographics in our data set but we restricted attention to a subset). We restrict our analysis to a sample of 2068 households who make between 20 and 300 shopping trips. These households collectively make 1,551,213 purchases during 333,585 shopping trips.^{16}^{16}16We define a shopping trip as a set of all purchases a household makes on a calendar day. Of these, 455,445 purchases and 100,504 trips occur on a Tuesday or Wednesday. We use only the data from Tuesday and Wednesday and exclude weeks with major US Holidays^{17}^{17}17We exclude data from the week prior to Halloween, Thanksgiving, Christmas, 4th of July, and Labor Day. in our estimation approach due to concerns about the potential for price endogeneity as discussed in Section 5.
The data includes a product hierarchy for each product, with the smallest unit of analysis (the unit at which prices are set) being the universal product code (UPC). From examining the data, it is not a priori perfectly clear which level of the hierarchy best matches our desiderata for a “category,” which would be for the consumers to buy at most one item from each category, while purchasing decisions are not correlated across separate categories.^{18}^{18}18At higher levels of aggregation, it was much more common to see multiple purchases in the same grouping on a single trip. At lower levels of aggregation, many categories were split into classes that contained products that seemed likely to be substitutes. For example, the category Apples is split into classes such as Fuji and Gala apples. Sharp Cheddar is in a separate class (but same category) as Mild Cheddar. To ensure a good match between the model and the application, we use the “category” level of the UPC hierarchy, and we focus on categories and items that pass certain filters, reducing the number of product categories from 235 to 123. The filters are reviewed in appendix Section 8.1, but important restrictions include eliminating highly seasonal categories, as well as categories without sufficient price variation, or where withincategory price changes are highly correlated across products.
Figure 1 illustrates summary statistics on household shopping frequency and basket size in our restricted data set.
4.2 Models
4.2.1 Nested Factorization
Our primary model is the Nested Factorization model, as outlined in Section 3.2.1. The key hyperparameters of the model are the dimensionality of the latent factorizations of the user preferences and elasticities. Allowing for a higher dimensional factorization allows for more flexibility in the shopping patterns the model is able to fit, at the expense of slower estimation speeds and larger potential for overfitting the data. In order to choose the values for these hyperparameters we follow the standard practice in the computer science literature of selecting based on performance on “validation” subset of the data that is distinct from the subset used to train the models (and distinct from the “test” subset that is “held out” and not used until the final comparison between models). We discuss the model selection criteria in more depth in Section 6. We compare the performance of the Nested Factorization model against several alternative approaches described in the following sections.
4.2.2 Multinomial Logit
The simplest and most commonly used discrete choice model is the multinomial logit [Train, 2016], which has a long history in economics tracing back to Luce [1959] and McFadden [1974].
We focus on a baseline specification that controls for household demographics (gender, age, marital status, and income^{19}^{19}19We divide age into buckets {Under 45, 4555, Over 55}. We split income at $100k, which is roughly the median for this store.) and include the weekly mean category purchase rates as pseudofixed effects for each calendar week, which helps control for seasonal trends that shift the demand for the product category and which may be correlated with the product prices. We have also tried alternative specifications that add behavioral controls based on splitting the population into 20% buckets based on total spending in the store and a model without demographic controls all of which had similar predictive performance. Because of its poor predictive performance across all of the measures we focus on in this paper, we have omitted the multinomial logit results from some of the results charts and tables, when the additional entries detract from clarity.
4.2.3 Mixed Logit
The mixed, or random coefficients, logit is one approach for increasing the flexibility of the multinomial logit. By allowing the coefficients of the model to vary across the population, the mixed logit allows for correlation in unobserved factors over time and for more flexible patterns of substitution between products. McFadden and Train [2000] show that any choice probabilities derived from random utility maximization can be approximated arbitrarily well by a appropriately chosen mixed logit model. As Steenburgh and Ainslie [2013] point out, the mixed logit still constrains demand at the individual level to satisfy IIA, so it “improves upon, but does not completely solve the problems of the homogeneous logit model.”
4.2.4 Nested Logit
Another method for relaxing the homogeneous logit model to allow for more flexible patterns of substitution is the nested logit model. In the nested logit model, the decisions a user faces are partitioned into “nests.” One interpretation of the nested logit structure is that a user first chooses which nest to purchase from, and then which product to choose from within the nest. ^{2}^{2}todo: 2Do we need to explain the alternative interpretation of the NL in which there are correlated error terms within each nest? Within each nest, the choices satisfy the IIA substitution pattern, but the substitution between products in different nests is able to vary more flexibly. We choose a very simple nesting structure. We put the outside good in its own nest, and the remaining products in each category are in a single shared nest. An additional term called the “nesting coefficient” or “inclusive value term” controls how the decision of which nest to choose depends on the utilities of the items within the nest. This nesting coefficient is analogous to the term in the Nested Factorization (equation 8) with the restriction that the value be homogeneous across households. Within the product nest, choice follows the same functional form as used in the homogeneous logit model with demographic controls. In essence, the Nested Factorization can be thought of as an extension to a nested logit that allows for rich heterogeneity in consumer preferences and price sensitivities by means of a latent factorization approach.
4.2.5 Discrete Choice Models with HPF Controls
One disadvantage of the Nested Factorization functional form relative to the HPF form used in Gopalan et al. [2013]
is that leads to substantially slower estimation of the approximate posterior. The choice of functional form and priors in the original HPF form allows for a closed form for the gradient of the variational Bayes objective function. With the Nested Factorization model, we have to perform stochastic gradient descent using a noisy estimate of the gradient. In practice this leads the model to require substantially more time and iterations before convergence.
Hierarchical Poisson Factorization (HPF)^{20}^{20}20We have extended the original HPF model to allow for observed user and item characteristics (including time varying characteristics), however on this dataset we found little or no improvement for out of sample predictive fit relative to a purely latent factorization.:
This model predicts user will purchase item at a mean rate of , so we can analogize it to a discrete choice model within a category with utility taking the form , which will generate the same choice probabilities for each item in the category (with an appropriate choice of utility for the outside good^{21}^{21}21In this case, ).
This motivates an alternative approach that approximates the full Nested Factorization model with a two step approach. First, estimate each shopper’s preferences over items using the HPF model without controlling for prices. Second, take the estimated utility values for each shopper and item and plug these values in as covariates into standard discrete choice models. This “HPF controls” approach can be thought of as an approximation to the generally infeasible approach of having separate fixed effects for each household item pair (i.e. separate parameters).
This two step procedure may also proved helpful in contexts where it is important to incorporate additional complications that are difficult to directly embed into the full NF Bayesian model. For example it may be effective to include these HPF controls into models of dynamic discrete choice such as those that arise from storable goods [Hendel and Nevo, 2006] or from consumer learning [Ackerberg, 2003] as a simple way to allow richer heterogeneity in consumer preferences (at the cost of lower statistical efficiency and potentially bias from estimating as a two step procedure rather than simultaneously).
5 Identification and Placebo Tests
In our data, almost all price changes occur on Tuesday nights, at a time (midnight) when very few customers are shopping. Thus, we can think of the price change as separating Tuesday and Wednesday. This motivates an empirical specification in which we use only the data from Tuesday and Wednesday in order to focus narrowly to the days immediately before and after price changes. We then include controls at the category level for each week and a dummy variable for Wednesday. The “identifying assumption” for learning price elasticities from this specification is that any differences in a particular consumer’s preferences for items between Tuesday and Wednesday are constant across weeks; in other words, weeks may differ from one another, but the Tuesday to Wednesday trend is constant over time. We also exclude the data from weeks immediately prior to major US Holidays out of a concern that this assumption is less likely to hold in these weeks e.g. the difference between the shopping patterns on the Tuesday and Wednesday before Thanksgiving may systematically differ from pattern that holds during more typical weeks.
In order to assess the validity of our assumptions, we present some supplementary analysis in the spirit of the literature on treatment effects [Athey and Imbens, 2017]. In particular, we test for the presence of certain types of price endogeneity by taking the price coefficients we get from the actual price data and comparing to the price coefficients we would get from a model fit on a data with the prices shifted forwards or backwards across time. We do this in two ways, first by shifting the price of a single UPC in each category (and giving that UPC a separate price coefficient), and second by simultaneously shifting the prices of all items in the category. To create the forward shifted price series for a product, we move each week with a price change forward to the first week that had no price changes in the real data.^{22}^{22}22Thus in the shifted data all price changes occur on weeks without price changes in the real data and all weeks with price changes in the real data have no price changes in the shifted data. A naive shift of all prices by exactly 1 week fails to break the correlation between the price changes in the shifted and real price data, due to the frequency of weeklong temporary price changes.
We repeat this process for each of the 123 product categories and plot the resulting distribution of price coefficients and pvalues for the price coefficients. The desired result is that the shifted price series result in an approximately uniform distribution of pvalues, since the artificial price changes should have no effect on consumer purchase behavior. The following results are based on the basic multinomial logit specification (with the pseudo week effects, which are calculated as the category level mean purchase rate in the week).
^{23}^{23}23We focus on the basic multinomial logit specification due to it’s computational speed and relative simplicity. Running similar tests for the other specifications including the Nested Factorization is possible in theory, but requires a larger computational cost.Out of the 123 categories, 13 fail one of the four placebo tests at the 1% level. If we only considered unconfoundedness checks that shift prices backwards, only 4 categories fail one of the two backward shifting tests. Backwards shifts would fail in the presence of consumers who are aware of future price changes. Forward shifts can fail for goods for which stockpiling is possible. Some of the categories that fail with the forward shifted price are durable/storable (e.g. Baking Mixes, Ketchup, and Bag Frozen Vegetables), but other categories seem more likely be failing for other reasons (e.g. Refrigerated Turkey and Tomatoes).
6 Assessing Model Performance and Fit
In the machine learning literature, it is typical to split data into three nonoverlapping parts: a training set, a validation set, and a test set. The training data is used to fit the parameters of the model. To the extent the model has hyperparameters
^{24}^{24}24For example the regularization coefficientin a LASSO regression, or in the case of Nested Factorization, the number of latent factors of each type to include.
that must be set prior to estimation, the model estimation can be repeated under different values of the hyperparameters. The validation set is used to select a model (i.e. to make a choice of hyperparameters) based on each model’s predictive performance on the validation set. Finally, the predictive performance on held out test data is used to evaluate the performance of the chosen model. Under the assumption that all observations are drawn from the same data generating process, then the predictive performance on the test set is an unbiased estimate of the model’s ability to make predictions on new data. In Section
6.1, we compare Nested Factorization and the set of alternative models in terms of their predictive fit on the held out test sample of data.However, this notion of predictive performance on held out data does not evaluate the ability of a model to make causal predictions of what “would” happen if we took actions that changed the distribution of the data. For example, a model trained to predict the demand for hotel rooms, might correctly identify that hotels are often full when prices are high and have many empty rooms when prices are low. This, however, may be due to hotels setting prices in expectation of demand, rather than because consumers prefer to pay high prices. Such a model could be highly predictive of hotel demand based on a randomly selected held out test set (which is drawn from data generated under the existing data generating process), however such a model would perform poorly at predicting what prices a hotel ought to charge (since changing prices will change the data generating process). It is concerns about such endogeneity of prices^{25}^{25}25As discussed in Rossi [2014], with consumer level data, our biggest concern for the identification of price effects, is that the store may be setting prices in response to variations in expected demand caused by seasonal trends or advertising. For example, there is more demand for fresh berries when they are in season or for turkeys immediately before Thanksgiving. It is not always clear which direction such price endogeneity will bias our estimates. The retailer may decide to take advantage of high demand by raising prices, but in other cases we prices reduced during high demand periods e.g. bags of candy going on sale before Halloween. that motivates our identification approach that relies on focusing on data immediately before and after price changes (Tuesdays and Wednesdays), including weekly time controls (which can absorb any seasonal/holiday trends), including a dummy variable for Wednesdays (to absorb any consistent differences in Tuesday vs Wednesday demand), and excluding data from the weeks of major US Holidays. Since all models include the weekly controls at the category level, they all have the ability to predict average demand in a week at that level. However, in weeks with price changes, only a model that has well estimated consumer preferences about price can account for which day within the week is expected to have more purchases.
To validate the ability to make predictions about counterfactuals, we focus on three types of changes that can occur during a week for a particular UPC: (a) change in the price of the UPC (b) a change in the price of a different product in the same category and (c) another item in the category going into or out of stock. If the identifying assumptions^{26}^{26}26i.e. that controlling for week and day of week effects at the category level is sufficient to make potential demand orthogonal to price level and product availability. of our models hold, then we can think of each of these events as a small sources of quasiexperimental variation. In Section 6.2, we compare the log likelihoods of the individual household level predictions during weeks in which one of these “counterfactuals” occurs in order to evaluate how well each model is able to make predictions that capture the change in predicted demand before and after the change (relative to the weeklevel average captured by the weekly time controls). In addition, we also compare the ability of each model to make predictions about the change in aggregate demand from Tuesday to Wednesday during weeks in which one of these events occurs. Our test sets hold out data at the householdweek level; this allows us to estimate overall consumer preferences, and we test our ability to predict household purchases on trips outside the training data, and in particular in weeks where the weekcategory effect estimated using other consumers’ purchases in that week is insufficient to predict the average probability of consumers purchasing on a particular day of the week (since prices differ across days). We use select hyperparameters (tune the model) using only data from itemweeks with price changes, and we evaluate performance in the test set based on the three changes (a)(c) outlined above.
In Section 6.3, we further evaluate the performance of our model at making predictions in scenarios of interest for counterfactual inference. We compare models in terms of their ability to capture heterogeneity in preferences across the population of households and evaluate the degree to which the predicted heterogeneity is predictive of actual behavior in the held out test set. For example, we compare the predictions made for households who in the training data sample never purchased a particular UPC or have made no purchases at all from an entire product category. Among this group of “never buyers,” we show that the Nested Factorization model is able to correctly predict which of these households are relatively more or less likely to make a purchase in the held out test data.
Section 6.4 examines the estimated own price and cross price elasticities. Finally, Section 6.5 looks at the potential for targeted marketing efforts that are personalized based on the rich heterogeneity estimated by the Nested Factorization model.
6.1 Predictive Fit
Mean Log Likelihood  Mean Squared Error  

Model  Train  Test  Train  Test 
Nested Factorization  4.2271  4.9096  0.8981  0.9268 
Mixed Logit with Random Price and HPF Controls  4.9233  5.3125  0.9473  0.9660 
Nested Logit with HPF Controls  5.2345  5.4230  0.9583  0.9650 
Multinomial Logit with HPF Controls  5.2307  5.4248  0.9583  0.9651 
Mixed Logit with Random Price and Demographics  5.2976  5.5690  0.9780  0.9898 
Mixed Logit with Random Price and Random Intercepts  5.3956  5.5827  0.9785  0.9849 
Nested Logit with Demographic Controls  5.6080  5.6779  0.9788  0.9801 
Multinomial Logit with Demographic Controls  5.6142  5.6791  0.9791  0.9803 
In Table 1, we compare the predictive fits of each of the models.^{27}^{27}27Mean Log Likelihood and Mean Squared Error are calculated by dividing by the total number of purchases in order to make the values comparable between the test and training sets. Comparing the overall predictive accuracy across all models, Nested Factorization Model has the highest likelihood and the lowest sum of squared errors among all models on both the training data and the held out test sample. In addition, each of the models that include HPF controls^{28}^{28}28i.e. useritem specific covariates that are estimated from the HPF model run on all categories simultaneously as described in Section 4.2.5 perform better than the models that use control only for demographics.^{29}^{29}29These trends also hold in additional specifications of the alternative logit models that included controls for shopping frequency and previous purchase behavior.
6.1.1 Comparison of Predictive Fit by Category
We can also compare how each model performs relative to the NF model at the level of individual categories. In Table 2, we calculate the relative rank of each model’s performance in the test set separately for each category. The Nested Factorization model has the highest log likelihood in 86% of categories and the lowest squared error in 96%. This demonstrates the effectiveness of learning preferences simultaneously across many product categories. Even if we were only interested in understanding consumer preferences in one particular category, e.g. yogurt, it can be effective to train a model using the data from other categories as well, either using the full Nested Factorization model or by using the HPF controls, which also consistently improve predictive performance on the held out test data in most categories.
Mean Rank  % Best Performance  

Model  Log L  SE  Log L  SE 
Nested Factorization  1.53  1.10  86.2%  95.9% 
Mixed Logit with Random Price and HPF Controls  2.40  5.33  9.8%  0.8% 
Nested Logit with HPF Controls  3.87  3.38  0.0%  0.8% 
Multinomial Logit with HPF Controls  4.21  3.63  0.8%  0.8% 
Mixed Logit with Random Price and Random Intercepts  4.69  5.65  0.8%  1.6% 
Mixed Logit with Random Price and Demographics  5.21  7.07  2.4%  0.0% 
Nested Logit with Demographic Controls  6.96  4.93  0.0%  0.0% 
Multinomial Logit with Demographic Controls  7.13  4.91  0.0%  0.0% 
6.1.2 Comparison of Fits by Household and UPC
To analyze which households or products for which we are gaining the most improvement to test set predictive fit. In figure 3, we can see that all models are more accurate in their predictions for the more commonly purchased UPCs (percentile 100) than for the less common items. Across all percentiles, the Nested Factorization model does consistently better than the alternative models. The models that use HPF effects (solid lines) have much smaller, but consistent gains over the nested and mixed logit models that use demographic or behavioral controls. Similar trends can be seen when we divide the results based on the number of purchases each household made in the training data in figure 3. ^{3}^{3}todo: 3If we want to give these less emphasis, I can make a smaller sidebyside version with a single legend
6.2 Price Change and Availability Counterfactuals
In the economics and marketing literatures, models of consumer demand are used to make inferences about what “would” happen if change were made to a market. For example, such models have been used to predict what would happen if prices were changed, if products are added or removed from a market, or if competing firms in the market were to merge. As discussed in Section 6, evaluating the predictive fit of a model, even when done on a held out test sample, does not reliably determine whether a model can be used to make predictions under counterfactual states of the world such as these. To evaluate the ability to make predictions under changes to prices or product availability, we focus on each model’s predictions on held out test set data immediately before and after such changes occur. Under the assumption that these changes are exogenous conditional on our week and weekday controls, we can think of each of these changes as a miniature experiment. By pooling across many such small noisy experiments, we can increase our precision in detecting differences in performance. We focus on three types of changes that can occur between Tuesday and Wednesday for a particular UPC. First, we look at weeks in which the focal product’s price changes, which we can think of as evaluating the accuracy of the model’s ownprice elasticity estimates. Second, we look at weeks in which some other product in the focal product’s category has a price change, in order to evaluate the predicted crossprice elasticities. Finally, we look at weeks in which some other product in the focal product’s category goes into or out of stock, which is another measure of the patterns of substitution between products.^{30}^{30}30In all cases we exclude weeks in which the focal product is out of stock on either day. For the cross price and out of stock counterfactuals, we exclude weeks in which the focal product has a price change. For the price change counterfactuals, we exclude weeks in which the magnitude of the price change is less than We asses fit on the test data using three measures. The first measure is the mean log likelihood of the individual household level predictions for product weeks that experienced the corresponding counterfactual event. The second and third measure compare the actual aggregate demand to the predicted aggregate demand across all households in the test set who shopped during the corresponding weeks. For products that are purchased at least 2.5 times on average per day, we calculate the likelihood of the Tuesday to Wednesday change in aggregate demand as approximated by a Skellam distribution.^{31}^{31}31If the individual purchasing decisions are distributed as independent Bernoulli variables, then their sum, the aggregate demand has a Poisson distribution. Then the TuesdayWednesday change in aggregate demand has a Skellam distribution, which is the difference between two independent Poisson distributions
For less popular products, we calculate the likelihood of observing aggregate demand greater than zero, which we approximate with a Bernoulli distribution whose mean is the sum of the household level predictions.
Individual  Aggregate  
Model  Popular  Less Common  Popular  Less Common 
All Weeks  
Nested Factorization  0.1070 (0.0004)  0.0173 (0.0001)  2.5356 (0.0146)  1.3072 (0.0044) 
Mixed Logit with Random Price and HPF Controls  0.1156 (0.0004)  0.0188 (0.0001)  2.5562 (0.0145)  1.3365 (0.0037) 
Nested Logit with HPF Controls  0.1194 (0.0005)  0.0191 (0.0001)  2.5568 (0.0154)  1.3105 (0.0040) 
Multinomial Logit with HPF Controls  0.1194 (0.0005)  0.0191 (0.0001)  2.5554 (0.0154)  1.3086 (0.0040) 
Mixed Logit with Random Price and Random Intercepts  0.1263 (0.0005)  0.0194 (0.0001)  2.5712 (0.0146)  1.3316 (0.0037) 
Mixed Logit with Random Price and Demographics  0.1240 (0.0004)  0.0195 (0.0001)  2.5934 (0.0153)  1.3543 (0.0038) 
Multinomial Logit with Demographic Controls  0.1290 (0.0005)  0.0197 (0.0001)  2.5722 (0.0157)  1.3105 (0.0040) 
Nested Logit with Demographic Controls  0.1289 (0.0005)  0.0197 (0.0001)  2.5740 (0.0157)  1.3113 (0.0041) 
Cross Price Weeks  
Nested Factorization  0.0925 (0.0008)  0.0149 (0.0001)  2.4527 (0.0262)  1.2017 (0.0084) 
Mixed Logit with Random Price and HPF Controls  0.1006 (0.0008)  0.0163 (0.0001)  2.4846 (0.0258)  1.2584 (0.0069) 
Nested Logit with HPF Controls  0.1041 (0.0009)  0.0164 (0.0001)  2.4844 (0.0277)  1.2177 (0.0076) 
Multinomial Logit with HPF Controls  0.1041 (0.0009)  0.0164 (0.0001)  2.4836 (0.0276)  1.2165 (0.0076) 
Mixed Logit with Random Price and Random Intercepts  0.1139 (0.0009)  0.0168 (0.0001)  2.5002 (0.0260)  1.2527 (0.0069) 
Mixed Logit with Random Price and Demographics  0.1111 (0.0009)  0.0170 (0.0001)  2.5132 (0.0280)  1.2752 (0.0071) 
Multinomial Logit with Demographic Controls  0.1162 (0.0010)  0.0170 (0.0001)  2.4966 (0.0279)  1.2182 (0.0077) 
Nested Logit with Demographic Controls  0.1161 (0.0010)  0.0170 (0.0001)  2.5066 (0.0292)  1.2194 (0.0078) 
Own Price Weeks  
Nested Factorization  0.1374 (0.0009)  0.0229 (0.0001)  2.7871 (0.0360)  1.5544 (0.0104) 
Mixed Logit with Random Price and HPF Controls  0.1465 (0.0009)  0.0243 (0.0001)  2.8004 (0.0357)  1.5475 (0.0086) 
Nested Logit with HPF Controls  0.1493 (0.0009)  0.0249 (0.0001)  2.7986 (0.0371)  1.5356 (0.0092) 
Multinomial Logit with HPF Controls  0.1493 (0.0009)  0.0249 (0.0001)  2.7949 (0.0373)  1.5321 (0.0092) 
Mixed Logit with Random Price and Random Intercepts  0.1530 (0.0009)  0.0251 (0.0001)  2.8003 (0.0348)  1.5408 (0.0085) 
Mixed Logit with Random Price and Demographics  0.1525 (0.0009)  0.0251 (0.0001)  2.8544 (0.0374)  1.5709 (0.0090) 
Multinomial Logit with Demographic Controls  0.1557 (0.0010)  0.0256 (0.0002)  2.8097 (0.0369)  1.5344 (0.0092) 
Nested Logit with Demographic Controls  0.1555 (0.0010)  0.0256 (0.0002)  2.8186 (0.0372)  1.5377 (0.0093) 
Out of Stock Weeks  
Nested Factorization  0.0924 (0.0040)  0.0159 (0.0002)  2.3349 (0.1114)  1.2746 (0.0129) 
Mixed Logit with Random Price and HPF Controls  0.1033 (0.0044)  0.0173 (0.0002)  2.3679 (0.1225)  1.3064 (0.0111) 
Nested Logit with HPF Controls  0.1068 (0.0046)  0.0176 (0.0002)  2.3427 (0.1243)  1.2817 (0.0122) 
Multinomial Logit with HPF Controls  0.1068 (0.0046)  0.0176 (0.0002)  2.3446 (0.1259)  1.2774 (0.0121) 
Mixed Logit with Random Price and Demographics  0.1091 (0.0045)  0.0180 (0.0002)  2.3669 (0.1132)  1.3249 (0.0118) 
Mixed Logit with Random Price and Random Intercepts  0.1125 (0.0046)  0.0181 (0.0002)  2.3403 (0.1156)  1.3068 (0.0113) 
Multinomial Logit with Demographic Controls  0.1146 (0.0048)  0.0183 (0.0002)  2.3261 (0.1189)  1.2800 (0.0122) 
Nested Logit with Demographic Controls  0.1145 (0.0047)  0.0183 (0.0002)  2.2974 (0.0990)  1.2840 (0.0123) 
6.3 Comparison of Degree of Personalization across Households
To examine the extent to which each of these models is able to flexibly model the differences in preferences between households, we calculate two measures of the degree of “personalization” of the predicted purchase rates that each model predicts for each household. First, we compare the coefficient of variation of a models predictions at the UPC level and the category level.^{32}^{32}32Coefficient of variation is defined as As a second measure, we regress the predicted purchase rate on the actual purchase rate in the in the held out test sample.^{33}^{33}33The coefficients are from a regression of actual purchase rate on the predicted purchase rate (both calculated on the test set) with item/category specific fixed effects to absorb heterogeneity in the mean purchase rates across items/categories.
Table 4 shows that the Nested Factorization model has the largest variation in the predictions across households and that this variation is strongly correlated with variation in the actual purchase rates in the held out test data. For each increase in the Nested Factorization model’s prediction of a household’s purchase for a UPC, the household’s actual purchase rate in the test set increases by .
Coef of Variation  Regression Coef  

Model  UPC  Category  UPC  Category 
Nested Factorization  3.2546  1.7756  0.9955  1.0023 
Mixed Logit with Random Price and HPF Controls  2.0747  1.6085  0.6861  0.7007 
Mixed Logit with Random Price and Demographics  1.3869  1.5724  0.4718  0.5968 
Multinomial Logit with HPF Controls  1.2590  0.7276  0.8402  0.8893 
Nested Logit with HPF Controls  1.2368  0.7520  0.8417  0.8725 
Mixed Logit with Random Price and Random Intercepts  1.0834  1.0446  0.4666  0.6959 
Nested Logit with Demographic Controls  0.4465  0.2967  0.8947  0.9314 
Multinomial Logit with Demographic Controls  0.4337  0.2756  0.9077  0.9411 
6.3.1 Predicting Preference for Products a Household has Not Yet Purchased
The Nested Factorization model is also able to make predictions about the strength of a households preferences for a given UPC, even if the household has never purchased that particular item before. To demonstrate this, we look at the set of all households who have made 0 purchases of a particular UPC in the training sample. For each UPC and each predictive model, we can rank these households based on their predicted purchase rate and group them into deciles. We carry out a similar analysis of households who made no purchases from an entire product category during the training sample. Figure
4 shows that the Nested Factorization model is able to correctly predict which households are relatively more or less likely to purchase a category or UPC that they never purchased during the training sample. The decile of households with the highest predicted likelihood to purchase a product category for the first time, purchases at roughly 3 times the frequency of the lowest decile. At the UPC level, this ratio of purchase rates in the held out test sample is more than 10 fold difference. The models with HPF controls (solid lines) are able to capture a smaller amount of this variation The models without either approach for latent factorization (dotted lines) have substantially less predictive power for these first time buyers. This may be useful to applied marketing practitioners who may be interested in targeting advertising or promotions towards new customers who might be interested in a product that they have not yet tried.6.4 Estimated Elasticities
6.4.1 Cross Price Elasticities Within and Between Products Subcategories
None of the models were given data from the product hierarchy data about the “class” or “subclass” each product is categorized under. These groupings are more fine grained than the “category” level that we focused on for modeling product substitution. Nevertheless, we show that the Nested Factorization model correctly infers that products that are in the same class or subclass are more similar to each other, and as a result these models predict higher levels of cross price elasticities between products that are in the same class or subclass than between items that are in different classes/subclasses. ^{inline}^{inline}todo: inlineThe Elasticities tables is fairly large/wide. We could split into one table for own price and one table for the cross price elasticities.
Own Price  Class Cross Price  Subclass Cross Price  

Model  Median  SD(Mean)  Mean(SD)  Inside  Outside  %  Inside  Outside  % 
Nested Factorization  1.7121  1.2008  1.7774  0.0186  0.0080  132%  0.0196  0.0181  8.4% 
Nested Logit with Demographic Controls  1.2976  1.0377  0.0532  0.0119  0.0086  37%  0.0125  0.0115  8.5% 
Mixed Logit with Random Price and Random Intercepts  2.2024  1.7822  0.8387  0.0062  0.0053  16%  0.0062  0.0062  0.7% 
Mixed Logit with Random Price and HPF Controls  2.7077  1.9084  1.1294  0.0063  0.0054  16%  0.0063  0.0065  3.9% 
Multinomial Logit with HPF Controls  1.1841  0.8904  0.0060  0.0035  0.0030  16%  0.0036  0.0035  0.8% 
Multinomial Logit with Demographic Controls  1.1813  0.8837  0.0017  0.0034  0.0029  16%  0.0034  0.0034  2.1% 
Mixed Logit with Random Price and Demographics  3.0897  2.4546  1.4785  0.0067  0.0060  13%  0.0066  0.0069  4.6% 
Nested Logit with HPF Controls  1.1017  0.8783  0.0182  0.0148  0.0158  7%  0.0140  0.0157  10.7% 
SD(Mean) is the standard deviation across the mean product level own price elasticities. This captures how much variability there is in elasticities across products. Mean(SD) is the mean of the standard deviation of elasticities across consumers within a specific product. This captures the amount of variability in elasticities across consumers for the same product.
We also compare the mean estimated elasticities for products that are/aren’t in the same product class or product subclass. We would expect the cross price elasticities inside of a class or subclass to be higher than those outside the class, since this implies substitution towards products that are more similar.
6.4.2 Aggregate Demand Curves
In figure 7, we validate whether the households with higher predicted elasticities do in fact respond more to price changes in heldout test data. To do this, for each UPC we split households into terciles based on their predicted elasticity. We then compare the TuesdayWednesday change in aggregate demand in the test set depending on the size of the price change during the week. The household’s with the higher predicted elasticities do in fact appear to have aggregate demand that is more responsive to price changes. This establishes that the heterogeneity we estimate is useful for counterfactual predictions about heterogeneity in consumer response to price changes.
6.5 Target Marketing
In Table 6, we evaluate the ability of the store to target a coupons to its customers. For each product category, we use each model to select the 30% of households for whom it would be most profitable for the store to offer a 30% off coupon for the most popular UPC in the category.^{34}^{34}34Profits are calculated as price  marginal cost. Marginal costs come from the retailer’s records, which are available for most products. For items with no marginal cost data, we treat the minimum retail price in the data as the marginal cost. We then evaluate how profitable this coupon targeting would be using the Nested Factorization model as ground truth. For each model we evaluate three approaches to targeting the coupons. Under individualized targeting, the store is able to select individual households when choosing whom to target with the coupons. Under demographic targeting, the coupons must be allocated in a way that is uniform within demographic groups.^{35}^{35}35We define demographic groups in terms of marital status, income level, age, and number of children. Under behavioral targeting, the coupons must be allocated based on the number of times a household has made purchases in the product category. Under each scenario, we compare the predicted store profits to the profits that would have been earned if the store had allocated the coupons uniformly at random.
% Gains Relative to Uniform  

Model  Behavioral  Demographic  Individualized 
Nested Factorization (linear)  2.57%  4.55%  28.5% 
Mixed Logit with Random Price Effects and HPF Controls (linear)  1.51%  2.38%  5.7% 
Multinomial Logit with HPF Controls (linear)  1.37%  1.60%  5.7% 
Nested Logit with HPF Controls (linear)  1.41%  1.57%  5.4% 
Nested Logit with Demographic Controls (linear)  0.56%  3.03%  4.7% 
Multinomial Logit with Demographic Controls (linear)  0.77%  2.17%  3.4% 
Mixed Logit with Random Price Effects and Demographics (linear)  0.75%  2.53%  3.1% 
Mixed Logit with Random Price and Random Intercepts (linear)  0.86%  1.31%  2.5% 
Unfortunately, without the ability to run an experiment, it is difficult to validate our model’s predictions of the household specific profitability of pricing decisions. We can however approximate such an experiment by looking at consumers’ purchasing behavior under the various price regimes that happened to have occurred during each consumer’s test sample shopping trips. ^{inline}^{inline}todo: inlineNeed to articulate the implicit assumption here. Something about consumers not strategically choosing which days to shop. For each UPC, we identify the two most common prices^{36}^{36}36We exclude all prices that are less than the item’s marginal cost, since those prices would lead to negative profits, and thus would never be chosen as the more profitable price for any consumer., and for each consumer use our model to predict which of the prices will lead to higher store profits. We can then compare the average profit per shopping trip from the focal UPC under the two chosen prices. We aggregate these profits across the two groupings of households^{37}^{37}37i.e. the household’s who are predicted to have higher profits under price 1 and the households who are predicted to have higher profits under price 2. and calculate the increase in average profits per shopping trip from households shopping at their targeted price relative to the other price.^{38}^{38}38We restrict ourselves to the two most common prices in order to increase the frequency with which we observe shopping trips with the selected prices in the test sample. In figure 8, we can see that on average the store earns substantially more profit from households when they shop on days with the price that we predicted would lead to higher profits. We further decompose these results in figure 9 by splitting the results based on what fraction of households were assigned to each of the pricing groups. When 95100% of households were all predicted to yield more profit under the same price, then that price on average leads to more than double the profit of the alternative price. ^{inline}^{inline}todo: inlineThe mean is positive, but there is substantial mass around zero. Not sure what we want to say about that.
7 Conclusion
This paper proposes the Nested Factorization model for learning consumer preferences from panel data. This model allows rich heterogeneity in preferences and price responsiveness across consumers, and it gains efficiency and precision from simultaneously learning consumer preferences across many product categories. Using recent advances in variational Bayesian inference with stochastic gradient descent allows the model to remain tractable on the types of relatively large data sets that are increasingly becoming available as digitization progresses. We show that this approach can yield substantial improvements in out of sample predictive accuracy. This model is also able to predict price elasticities and patterns of substitution between products, which are often ignored or explicitly assumed away in most of the related recommender systems literature from computer science. Using the nested functional form, inspired by the nested logit model, allows our model to more efficiently learn these patterns of cross product substitution. We demonstrate an approach for validating a model’s ability to make predictions for counterfactual questions, by leveraging the large number of price changes and changes in product availability that occur in the data. Treating each such change as a “mini experiment,” we can evaluate a model’s predictions before and after the change on held out data that was not used to fit the model. Pooling across many such sources of variation in the data reduces the noise and allows us to compare models in terms of their ability to make counterfactual predictions. We evaluate the potential gains from using flexible personalized models such as the one we propose here for targeting marketing efforts such as personalized price discounts or for identifying new consumers who might be interested in trying a product. More generally, we believe that flexible models of consumer demand, such as the Nested Factorization model proposed here, can be a useful tool for guiding the marketing strategies of firms or as part of a larger model for understanding patterns of competition between firms.
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8 Appendix
8.1 Data Construction and Sample Selection
The filters we use to select categories for study are outlined as follows:

For many of the mixed and nested logit specifications, we encountered difficulty with convergence in some of the product categories. To reduce these issues we ran all of the logit specifications using the top 10 items in each category along with an eleventh “pooled” option that combined all of the less popular items in the category. The NF and HPF models were run without any pooling of items. To make for a fair comparison, we evaluate model fit using only the top 10 items in each category. The relative performance of the NF model improves further if we compare the sum of the predicted purchase probabilities for the pooled items to the pooled item prediction from the logit models.

We eliminate categories in which more than 15% of shopping trips contain multiple items from the category or more than 10% of trips contain multiple top 10 items. Since for these categories the assumption of unit demand was substantially violated. For any remaining shopping trips in which multiple items from the same category were purchased, we selected one item at random from among the purchased items (and treated the remaining items as unpurchased).

We eliminate categories where the average absolute withincategory correlation of the top 10 items’ prices is greater than .

We only include categories where at least 2 of the top 10 items have price variation from Tuesday to Wednesday in one of the sample weeks and at least 1 of them top 10 UPCs has price changes of at least 10 cents in a least 10% of the sample weeks.

We eliminate the bottom 15% of categories in terms of seasonality. For each UPC, we first calculate seasonality as the Herfindahl index of daily demands over the sample period. We then calculate the percentile of each UPCs Herfindahl index over all UPCs and define a category’s seasonality as the average of the category’s top 10 items’ percentiles.
We also need to specify prices as well as outofstock status for products. We consider an item unavailable to all shoppers on any days in which it is listed as outofstock during more than 75% of shopping trips on that day.
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