In this study, we attempt to understand how household budget allocations across various expenditure categories change when the economy is in recession or expansion. The common assumption is that a household’s tastes would not change as a function of economic conditions and therefore any adjustments in expenditure patterns during economic contractions/expansions would simply be due to changes in the consumption budget. Standard economic models translate these budgetary effects into lateral movements along a set of fixed Engel curves, which relate category expenditure shares to total expenditures. We propose and test a conceptual framework based on the notion of relative consumption, which prescribes that, for any given total consumption budget, expenditure shares for positional goods/services will decrease during a recession, while shares for nonpositional goods/services will increase (i.e., shifting the entire Engel curve upward or downward, depending on the nature of the expenditure category and the economic conditions).

Many companies collect substantial information about their interactions with their customers. Yet information about their customers' transactions with competing firms is often sparse or nonexistent. As a result, firms are often compelled to manage customer relationships from an inward view of their customers. However, the empirical analysis in this study indicates that (1) the volume of customers' transactions within a firm has little correlation with the volume of their transactions with the firm's competitors and (2) a small percentage of customers account for a large portion of all the external transactions, suggesting the considerable potential to increase sales if these customers can be correctly identified and incentivized to switch. Thus, the authors argue for a more outward view in customer relationship management and develop a list augmentation-based approach to augment firms' internal records with insights into their customers' relationships with competing firms, including the size of each customer's wallet and the firm's share of it.

Household life cycle has been widely used as a determinant of consumer behavior and a basis for market segmentation. However, there is considerable disagreement about how life stages should be defined and how households progress through these stages. Existing studies use a priori definitions, which are tested on a cross-sectional survey of households collected at a single point in time and thus cannot reveal the real dynamics of the household life cycle. The Panel Study of Income Dynamics provides longitudinal data on household composition in the United States for a period of 34 years; the authors use this to identify empirically the most typical stages and paths that U.S. households have followed since 1968. They develop a hidden Markov model in which the stages of the household life cycle are taken as latent, unobservable states that are uncovered from the manifest household demographic profiles over the 34 years, assuming that households evolve through these latent stages following a first-order Markov process. The authors apply their results to classify members of another panel (Consumer Expenditure Survey) into life stages, which enables them to study the impact of the household life cycle on households' budgetary allocations, providing a comprehensive analysis of lifestyles (through expenditure patterns) over the household life cycle.

All types of consumer expenditures ultimately vie for the same pool of limited resources—the consumer's discretionary income. Consequently, consumers’ spending in a particular industry can be better understood in relation to their expenditures in others. Although marketers may believe that they are operating in distinct and unrelated industries, it is important to understand how consumers, with a given budget, make trade-offs between meeting different consumption needs. For example, how much would escalating gas prices affect consumer spending on food and apparel? Which industries would gain most in terms of extra consumer spending as a result of a tax rebate? Answers to these questions are also important from a public policy standpoint because they provide insights into how consumer welfare would be affected as consumers reallocate their consumption budget in response to environmental changes. This study proposes a structural demand model to approximate the household budget allocation decision, in which consumers are assumed to allocate a given budget across a full spectrum of consumption categories to maximize an underlying utility function. The authors illustrate the model using Consumer Expenditure Survey data from the United States, covering 31 consumption categories over 22 years. The calibrated model makes it possible to draw direct inferences about the trade-offs individual households make when they face budget constraints and how their relative preferences for different consumption categories vary across life stages and income levels. The study also demonstrates how the proposed model can be used in policy simulations to quantify the potential impacts on consumption patterns due to shifts in prices or discretionary income.

Trendspotting has become an important marketing intelligence tool for identifying and tracking general tendencies in consumer interest and behavior. Currently, trendspotting is done either qualitatively by trend hunters, who comb through everyday life in search of signs indicating major shifts in consumer needs and wants, or quantitatively by analysts, who monitor individual indicators, such as how many times a keyword has been searched, blogged, or tweeted online. In this study, the authors demonstrate how the latter can be improved by uncovering common trajectories hidden behind the coevolution of a large array of indicators. The authors propose a structural dynamic factor-analytic model that can be applied for simultaneously analyzing tens or even hundreds of time series, distilling them into a few key latent dynamic factors that isolate seasonal cyclic movements from nonseasonal, nonstationary trend lines. The authors demonstrate this novel multivariate approach to quantitative trendspotting in one application involving a promising new source of marketing intelligence—online keyword search data from Google Insights for Search—in which they analyze search volume patterns across 38 major makes of light vehicles over an 81-month period to uncover key common trends in consumer vehicle shopping interest.