Choice Set Heterogeneity and the Role of Advertising: An Analysis with Micro and Macro Data

Draganska, Michaela; Klapper, Daniel

doi:10.1509/jmkr.48.4.653

Cited by 79 publications

(23 citation statements)

References 64 publications

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“…That consumers are often heterogeneously informed about available o↵ers has long been recognized as an important driver of market power by firms, and has been widely documented empirically (see, for example, Chandra and Tappata (2011) and the references therein). Its importance for empirical work studying consumer demand and industry conduct is now increasingly recognized (see, for instance, Sovinsky Goeree (2008) and Draganska and Klapper (2011)). Theoretical work on competitive price discrimination with heterogeneously informed consumers, however, has to date been missing.…”

Section: Introductionmentioning

confidence: 99%

Competitive Screening Under Heterogeneous Information

Garrett

Gomes

Maestri³

2018

The Review of Economic Studies

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We study the interplay between informational frictions and second-degree price discrimination. Our theory recognizes that consumers differ in their tastes for quality as well as in the information they possess about available offers, which leads to dispersion over price–quality menus in equilibrium. While firms are ex ante identical, we show that their menus are ordered so that more generous menus leave more surplus to consumers of all valuations. We explore the cross-section of equilibrium menus and variations in market conditions to generate empirical predictions on prices, qualities, and markups across firms, and within a firm’s product line. For instance, more competition may raise prices for low-quality goods; yet, consumers are better off, as the qualities they receive also increase. The predictions of our model illuminate empirical findings in many markets, such as those for cell phone plans, yellow-pages advertising, cable TV, and air travel.

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Section: Introductionmentioning

confidence: 99%

Competitive Screening Under Heterogeneous Information

Garrett

Gomes

Maestri³

2018

The Review of Economic Studies

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“…A further source of data on consumer search behavior / considerations is survey information directly levied from consumers Draganska and Klapper (2011). andHonka (2014) use this kind of data.2 Apart from RFID, other technology such as video capture (seeJain, Misra, and Rudi (2014) or Hui, Huang, Suher, and Inman (2013)) or smart-phone wi-fi signals might also be used to measure search time in a similar fashion.…”

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confidence: 99%

Estimating Search Benefits from Path-Tracking Data: Measurement and Determinants

Seiler

Pinna²

2017

Marketing Science

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We study consumer search behavior in a brick-and-mortar store environment, using a unique data set obtained from radio frequency identification tags, which are attached to supermarket shopping carts. This technology allows us to record consumers' purchases as well as the time they spent in front of the shelf when contemplating which product to buy, giving us a direct measure of search effort. We estimate a linear regression of price paid on search duration in which search duration is instrumented with a search cost shifter. We show that this regression allows us to recover the marginal return from search in terms of price at the optimal stopping point for the average consumer. Our identification strategy as well as coefficient interpretation are valid for a broad class of search models and we are hence able to remain agnostic about the details of search process, such as search order and search protocol. We estimate an average return from search of $2.10 per minute and explore heterogeneity across consumer types, product categories and category location in the store. We find little difference in the returns from search across product categories, but large differences across consumer types and locations. Our findings suggest that situational factors such as the location of the category or the timing of the search within the shopping trip are more important determinants of search behavior than category characteristics like the number of UPCs.

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“…A more modern literature empirically teases apart lack of attention (from e.g., large thinking costs) from lack of preference, in several ways. One way is to obtain a direct measure of attention: for example, Roberts and Lattin (1991) and Draganska and Klapper (2011) utilize survey data and directly elicit consideration decisions. Another way is to provide exclusion restrictions that only enter consideration but not purchase.…”

Section: Related Literaturementioning

confidence: 99%

Pennies for Your Thoughts: Costly Product Consideration and Purchase Quantity Thresholds

Huang

Bronnenberg

2018

Marketing Science

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We show that individual consumer demand for packaged goods has discrete jumps between zero and large quantities, under a marginal change in price. Ruling out alternative explanations, we propose that consideration of each brand is costly independent of the purchase quantity. Such costs limit the amount of varieties chosen by each consumer, who uses prices to screen products in and out of the consideration set. In turn, the scarcity of attention at the shelf forces firms to compete more fiercely for customers. We structurally characterize rational decisions on consideration and purchase quantity, and estimate the model on micro data from the yogurt category. We find that consideration costs $1.25 for a brand not on feature, and $0.93 for a brand on feature. In a world with more feature ads, firms enjoy higher equilibrium markups because competition is softened by the abundance of attention.

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Choice Set Heterogeneity and the Role of Advertising: An Analysis with Micro and Macro Data

Cited by 79 publications

References 64 publications

Competitive Screening Under Heterogeneous Information

Competitive Screening Under Heterogeneous Information

Estimating Search Benefits from Path-Tracking Data: Measurement and Determinants

Pennies for Your Thoughts: Costly Product Consideration and Purchase Quantity Thresholds

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