Consumer inattention, uncertainty, and marketing strategy

This dissertation investigates the implications of consumer inattention and uncertainty for firms' advertising and pricing decisions. The first chapter is an overview of the problems addressed in the dissertation and the main findings. The second chapter develops a theory-based, cost-effective method to estimate the demand for new products using choice experiments. The premise is that consumers are uncertain about their valuation of a new product and need to spend costly effort to learn their valuation. The effort consumers spend is affected by the probability of their choice being realized, and as a result will change the manifested demand curve derived from choice experiments. We run a large-scale choice experiment on a mobile game platform, where we randomize the price and realization probability of a new product. Data support our theoretical hypothesis. We then estimate a structural model of consumer decisions. The structural estimates allow us to accurately infer actual demand based on choice experiments of small to moderate realization probabilities. The third chapter examines firms' advertising strategy on social media under consumers' limited attention. Advertising on social media faces a new challenge as consumers can actively select which advertisers to follow. A Bayesian learning model suggests that consumers with limited attention may rationally choose to unfollow a firm. This happens if consumers already know about the firm's value well and if the firm advertises too intensely. However, we find that intensive advertising may still be the optimal strategy for firms. If a firm is perceived as providing low value, it will want to advertise aggressively to change consumers' mind; if a firm is perceived as providing higher value, it will also want to advertise intensively, but in an effort to crowd-out advertising messages from its competitors. Tracking company accounts of 49 TV shows on the most popular tweeting website in China provides empirical evidence that both popular and non-popular firms advertise intensively, although the number of followers does go down when a firm advertises too intensively. The fourth chapter investigates channel coordination in search advertising. Given that consumers have limited attention, there are only a limited number of advertising slots on search engine platforms that can attract positive number of clicks. A manufacturer can sponsor retailers to advertise its products while at the same time compete with them in a position auction with limited number of slots. We prescribe the optimal cooperative search advertising strategies for the manufacturer. We find that it may not be optimal for a manufacturer to cooperate with all of its retailers, even when these retailers are ex ante the same. This finding reflects the manufacturer's tradeoff between higher demand and higher bidding cost caused by more intensified competition. With two asymmetric retailers, the manufacturer should support the retailer with the higher channel profit per click to get a higher position than the other retailer. The manufacturer should take a higher position than a retailer when its profit per click via direct sales exceeds the channel profit per click of the retailer. We also investigate how a manufacturer uses both wholesale and advertising contracts to coordinate channels with endogenous retail prices.

Department dc:contributor.department

Sloan School of Management.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2018

Author dc:creator

• Cao, Xinyu

Advisor dc:contributor.advisor

• Juanjuan Zhang.

• Sloan School of Management.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/118018

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/118018