The author systematically examines one of the important issues of information — establishing the market price. He introduces the concept of «search» — where a buyer wanting to get a better price, is forced to question sellers. The article deals with various aspects of finding the necessary information.

Economics of Information

IEEE Transactions on Pattern Analysis and Machine Intelligence

The nature of marketing science is changing in a systematic, predictable, and irrevocable way. As information technology enables ubiquitous customer communication and big customer data, the fundamental nature of the firm's connection to the customer changes: better, more personalized service can be offered, from which service relationships are deepened, and consequently, more profitable customers grow the influence of service within the goods sector and expand the service sector in the economy. Marketing is becoming more personalized, and marketing science techniques that exploit customer heterogeneity are becoming more important. Information technology improvements also guarantee the increasing importance and usage of computationally intensive data processing and “big data.” Most importantly, these trends have already lasted for more than a century, and they will become even more pronounced in the coming years as a result of the monotonic nature of technology improvement. These changes imply a transformation of marketing science in both the topics to be emphasized and the methods to be employed. Increasingly, and inevitably, all of marketing will come to resemble to a greater degree the formerly specialized area of service marketing, only with an increased emphasis on marketing analytics.

The Service Revolution and the Transformation of Marketing Science

The Role of Big Data and Predictive Analytics in Retailing

The authors develop a three-stage framework for strategic marketing planning, incorporating multiple artificial intelligence (AI) benefits: mechanical AI for automating repetitive marketing functions and activities, thinking AI for processing data to arrive at decisions, and feeling AI for analyzing interactions and human emotions. This framework lays out the ways that AI can be used for marketing research, strategy (segmentation, targeting, and positioning, STP), and actions. At the marketing research stage, mechanical AI can be used for data collection, thinking AI for market analysis, and feeling AI for customer understanding. At the marketing strategy (STP) stage, mechanical AI can be used for segmentation (segment recognition), thinking AI for targeting (segment recommendation), and feeling AI for positioning (segment resonance). At the marketing action stage, mechanical AI can be used for standardization, thinking AI for personalization, and feeling AI for relationalization. We apply this framework to various areas of marketing, organized by marketing 4Ps/4Cs, to illustrate the strategic use of AI.

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A strategic framework for artificial intelligence in marketing

The authors test methods, based on cognitively simple decision rules, that predict which products consumers select for their consideration sets. Drawing on qualitative research, the authors propose disjunctions-of-conjunctions (DOC) decision rules that generalize well-studied decision models, such as disjunctive, conjunctive, lexicographic, and subset conjunctive rules. They propose two machine-learning methods to estimate cognitively simple DOC rules. They observe consumers' consideration sets for global positioning systems for both calibration and validation data. They compare the proposed methods with both machine-learning and hierarchical Bayes methods, each based on five extant compensatory and noncompensatory rules. For the validation data, the cognitively simple DOC-based methods predict better than the ten benchmark methods on an information theoretic measure and on hit rates. The results are robust with respect to format by which consideration is measured, sample, and presentation of pro...

/pdf/disjunctions-of-conjunctions-cognitive-simplicity-and-17j4rha4yu.pdf

Disjunctions of Conjunctions, Cognitive Simplicity and Consideration Sets

A new approach for measuring consumer brand perceptions from consumer-created brand imagery via deep learning.

Visual Listening In: Extracting Brand Image Portrayed on Social Media

We develop and test an active-machine-learning method to select questions adaptively when consumers use heuristic decision rules. The method tailors priors to each consumer based on a “configurator.” Subsequent questions maximize information about the decision heuristics (minimize expected posterior entropy). To update posteriors after each question, we approximate the posterior with a variational distribution and use belief propagation (iterative loops of Bayes updating). The method runs sufficiently fast to select new queries in under a second and provides significantly and substantially more information per question than existing methods based on random, market-based, or orthogonal-design questions.

Synthetic data experiments demonstrate that adaptive questions provide close-to-optimal information and outperform existing methods even when there are response errors or “bad” priors. The basic algorithm focuses on conjunctive or disjunctive rules, but we demonstrate generalizations to more complex heuristics and to the use of previous-respondent data to improve consumer-specific priors. We illustrate the algorithm empirically in a Web-based survey conducted by an American automotive manufacturer to study vehicle consideration (872 respondents, 53 feature levels). Adaptive questions outperform market-based questions when estimating heuristic decision rules. Heuristic decision rules predict validation decisions better than compensatory rules.

http://web.mit.edu/hauser/www/Hauser%20PDFs/Dzyabura-Hauser%20Active%20Machine%20Learning%20for%20Consideration%20Heuristics%2004-21-11.pdf

Active Machine Learning for Consideration Heuristics

Firms are increasingly selling through both offline and online channels, allowing customers to experience the touch and feel of product attributes before purchasing those products. Consequently, the selection of products offered offline affects the demand in both channels. We address how firms should select an optimal offline assortment to maximize profits across both channels; we call this the showcase decision problem. We incorporate the impact of physical evaluation on preferences into the consumer demand model. Under this model, we show that the decision problem is NP-hard. Analytically, we derive optimal results for special cases and near-optimal approximations for general cases. Empirically, we use conjoint analysis to identify changes in consumer preferences resulting from physically evaluating products. For this application, we demonstrate gains in expected revenue of up to 40% due to accounting for the impact of offline assortment on the online sales. The online appendix is available at https://d...

http://people.stern.nyu.edu/ddzyabur/index_files/OfflineAssortment.pdf

Offline Assortment Optimization in the Presence of an Online Channel

Consumers often learn the weights they ascribe to product attributes (“preference weights”) as they search. For example, after test driving cars, a consumer might find that he or she undervalued tr...

/pdf/recommending-products-when-consumers-learn-their-preference-50tnwth5cm.pdf

Daria Dzyabura

Papers

Disjunctions of Conjunctions, Cognitive Simplicity and Consideration Sets

Visual Listening In: Extracting Brand Image Portrayed on Social Media

Active Machine Learning for Consideration Heuristics

Offline Assortment Optimization in the Presence of an Online Channel

Recommending Products When Consumers Learn Their Preference Weights