Marketing and Data Science: Together the Future is Ours

The synergistic use of computer science and marketing science techniques offers the best avenue for knowledge development and improved applications. A broad area of complementarity between the typical focus in statistics and computer science and that in marketing offers great potential. The former fields tend to focus on pattern recognition, control and prediction. Many marketing analyses embrace these directions, but also contribute by modeling structure and exploring causal relationships. Marketing has successfully combined foci from management science with foci from psychology and economics. These fields complement each other because they enable a broad spectrum of scientific approaches. Combined, they provide both understanding and practical solutions to important and relevant managerial marketing problems, and marketing science is already very successful at obtaining unique insights from big data

A marketing science perspective on recognition-based heuristics (and the fast-and-frugal paradigm)

Marketing science seeks to prescribe better marketing strategies (advertising, product development, pricing, etc.). To do so we rely on models of consumer decisions grounded in empirical observations. Field experience suggests that recognition-based heuristics help consumers to choose which brands to consider and purchase in frequently-purchased categories, but other heuristics are more relevant in durable-goods categories. Screening with recognition is a rational screening rule when advertising is a signal of product quality, when observing other consumers makes it easy to learn decision rules, and when firms react to engineering-design constraints by offering brands such that a high-level on one product feature implies a low level on another product feature. Experience with applications and field experiments suggests four fruitful research topics: deciding how to decide (endogeneity), learning decision rules by self-reflection, risk reduction, and the difference between utility functions and decision rules. These challenges also pose methodological cautions.Sloan School of Managemen

Learning from Experience, Simply

There is substantial academic interest in modeling consumer experiential learning. However, (approximately) optimal solutions to forward-looking experiential learning problems are complex, limiting their behavioral plausibility and empirical feasibility. We propose that consumers use cognitively simple heuristic strategies. We explore one viable heuristic—index strategies—and demonstrate that they are intuitive, tractable, and plausible. Index strategies are much simpler for consumers to use but provide close-to-optimal utility. They also avoid exponential growth in computational complexity, enabling researchers to study learning models in more complex situations. Well-defined index strategies depend on a structural property called indexability. We prove the indexability of a canonical forward-looking experiential learning model in which consumers learn brand quality while facing random utility shocks. Following an index strategy, consumers develop an index for each brand separately and choose the brand with the highest index. Using synthetic data, we demonstrate that an index strategy achieves nearly optimal utility at substantially lower computational costs. Using IRI data for diapers, we find that an index strategy performs as well as an approximately optimal solution and better than myopic learning. We extend the analysis to incorporate risk aversion, other cognitively simple heuristics, heterogeneous foresight, and an alternative specification of brands

Unstructured Direct Elicitation of Decision Rules

We investigate the feasibility of unstructured direct-elicitation (UDE) of decision rules consumers use to form consideration sets. With incentives to think hard and answer truthfully, tested formats ask respondents to state non-compensatory, compensatory, or mixed rules for agents who will select a product for the respondents. In a mobile-phone study two validation tasks (one delayed 3 weeks) ask respondents to indicate which of 32 mobile phones they would consider from a fractional 4[superscript 5]x2[superscript 2] design of features and levels. UDE predicts consideration sets better, across profiles and across respondents, than a structured direct-elicitation method (SDE). It predicts comparably to established incentive-aligned compensatory, non-compensatory, and mixed decompositional methods. In a more-complex (20x7x5[superscript 2]x4x3[superscript 4]x2[superscript 2]) automobile study, non-compensatory decomposition is not feasible and additive-utility decomposition is strained, but UDE scales well. Incentives are aligned for all methods using prize indemnity insurance to award a chance at $40,000 for an automobile plus cash. UDE predicts consideration sets better than either additive decomposition or an established SDE method (Casemap). We discuss the strengths and weaknesses of UDE relative to established methods.Research Grants Council (Hong Kong, China) (SAR (9041182, CityU 1454/06H))Pennsylvania State University (Smeal Small Research Grant

Crowdsourced earthquake early warning

Earthquake early warning (EEW) can reduce harm to people and infrastructure from earthquakes and tsunamis, but it has not been implemented in most high earthquake-risk regions because of prohibitive cost. Common consumer devices such as smartphones contain low-cost versions of the sensors used in EEW. Although less accurate than scientific-grade instruments, these sensors are globally ubiquitous. Through controlled tests of consumer devices, simulation of an M_w (moment magnitude) 7 earthquake on California’s Hayward fault, and real data from the M_w 9 Tohoku-oki earthquake, we demonstrate that EEW could be achieved via crowdsourcing

A Measurement Error Approach for Modeling Consumer Risk Preference

Von Neumann-Morgenstern (vN-M) utility theory is the dominant theoretical model of risk preference. Recently, market researchers have adapted vN-M theory to model consumer risk preference. But, most applications assess utility functions by asking just n questions to specify n parameters. However, any questioning format, especially under market research conditions, introduces measurement error. This paper explores the implications of measurement error on the estimation of the unknown parameters in vN-M utility functions and provides procedures to deal with measurement error. We assume that the functional form of the utility function, but not its parameters, can be determined a priori through qualitative questioning. We then model measurement error as if question format and other influences cause the consumer to choose the unknown “risk parameter” from a probability distribution and to make his decisions accordingly. We provide procedures to estimate the unknown parameters when the measurement error is either (a) Normal or (b) Exponential. Uncertainty in risk parameters induces uncertainty in utility and expected utility, and hence uncertainty in choice outcomes. Thus, we derive the induced probability distributions of the consumer\u27s utility and the estimators for the implied probability that an alternative is chosen. Results are obtained for both the standard decision analysis “preference indifference” question format and for a “revealed preference” format in which the consumer is asked simply to choose between two risky alternatives. Since uniattribute functions illustrate the essential risk preference properties of vN-M functions, we emphasize uniattribute results. We also provide multiattribute estimation procedures. Numerical examples illustrate the analytical results

Beluga whales in the western Beaufort Sea : current state of knowledge on timing, distribution, habitat use and environmental drivers

ECG was supported by a National Research Council-National Academy of Sciences Postdoctoral Fellowship.The seasonal and geographic patterns in the distribution, residency, and density of two populations (Chukchi and Beaufort) of beluga whales (Delphinapterus leucas) were examined using data from aerial surveys, passive acoustic recordings, and satellite telemetry to better understand this arctic species in the oceanographically complex and changing western Beaufort Sea. An aerial survey data-based model of beluga density highlights the Beaufort Sea slope as important habitat for belugas, with westerly regions becoming more important as summer progresses into fall. The Barrow Canyon region always had the highest relative densities of belugas from July-October. Passive acoustic data showed that beluga whales occupied the Beaufort slope and Beaufort Sea from early April until early November and passed each hydrophone location in three broad pulses during this time. These pulses likely represent the migrations of the two beluga populations: the first pulse in spring being from Beaufort animals, the second spring pulse Chukchi belugas, with the third, fall pulse a combination of both populations. Core-use and home range analyses of satellite-tagged belugas showed similar use of habitats as the aerial survey data, but also showed that it is predominantly the Chukchi population of belugas that uses the western Beaufort, with the exception of September when both populations overlap. Finally, an examination of these beluga datasets in the context of wind-driven changes in the local currents and water masses suggests that belugas are highly capable of adapting to oceanographic changes that may drive the distribution of their prey.PostprintPeer reviewe

Design and Evaluation of Product Aesthetics: A Human-Machine Hybrid Approach

Aesthetics are critically important to market acceptance in many product categories. In the automotive industry in particular, an improved aesthetic design can boost sales by 30% or more. Firms invest heavily in designing and testing new product aesthetics. A single automotive "theme clinic" costs between \$100,000 and \$1,000,000, and hundreds are conducted annually. We use machine learning to augment human judgment when designing and testing new product aesthetics. The model combines a probabilistic variational autoencoder (VAE) and adversarial components from generative adversarial networks (GAN), along with modeling assumptions that address managerial requirements for firm adoption. We train our model with data from an automotive partner-7,000 images evaluated by targeted consumers and 180,000 high-quality unrated images. Our model predicts well the appeal of new aesthetic designs-38% improvement relative to a baseline and substantial improvement over both conventional machine learning models and pretrained deep learning models. New automotive designs are generated in a controllable manner for the design team to consider, which we also empirically verify are appealing to consumers. These results, combining human and machine inputs for practical managerial usage, suggest that machine learning offers significant opportunity to augment aesthetic design

The Strategic Implications of Scale in Choice-Based Conjoint Analysis

Choice-based conjoint (CBC) studies have begun to rely on simulators to forecast equilibrium prices for pricing, strategic product positioning, and patent/copyright valuations. Whereas CBC research has long focused on the accuracy of estimated relative partworths of attribute levels, predicted equilibrium prices and strategic positioning are surprisingly and dramatically dependent on scale: the magnitude of the partworths (including the price coefficient) relative to the magnitude of the error term. Although the impact of scale on the ability to estimate heterogeneous partworths is well known, neither the literature nor current practice address the sensitivity of pricing and positioning to scale. This sensitivity is important because (estimated) scale depends on seemingly innocuous market-research decisions such as whether attributes are described by text or by realistic images. We demonstrate the strategic implications of scale using a stylized model in which heterogeneity is modeled explicitly. If a firm shirks on the quality of a CBC study and acts on incorrectly observed scale, a follower, but not an innovator, can make costly strategic errors. Externally valid estimates of scale are extremely important. We demonstrate empirically that image realism and incentive alignment affect scale sufficiently to change strategic decisions and affect patent/copyright valuations by hundreds of millions of dollars.</p