Research

Influencers are now an integral part of marketing. However, influencer strategy is often not systematically integrated into the overall marketing strategy. This situation was confirmed by our in-depth, semi-structured interviews with 15 marketing managers engaged in influencer marketing. Research related to such integration is also scant. We bridge this gap by providing a conceptual framework that integrates influencer strategy into marketing strategy through the lens of customer journey—the flow of customers from brand unawareness all the way to brand advocacy and repurchase. We first develop a theory-based influencer typology that yields eight ideal influencer types. We then provide a theory-driven mapping of the ideal influencer types to the customer journey stages and support the mapping with real-world illustrations. From a managerial perspective, the framework can guide the optimal allocation of ideal influencer types and the design of influencer activities across the customer journey, yielding a more profitable and integrated influencer strategy. From a research perspective, the framework opens new interpretations and opportunities related to influencer classification, the creation, deployment, and coordination of optimal portfolios of influencers across the customer journey, and richer interpretations of the customer journey itself.

We study a seller’s optimal influencer strategy in markets where early adopters post imperfect positive product reviews, reflect their experiences with only some attributes while omitting others. A seller can use influencers—who care about sales-based commissions and consumer welfare—when launching the product. Using an information design approach, we jointly design the seller’s optimal influencer usage, influencer contracts, and product pricing. We characterize when the seller should engage influencers and, if so, whether to motivate them through sales-based commissions or non-performance-based contracts. From a managerial perspective, our results show that the seller should (a) use influencers when early adopters’ review omissions are low to moderate, and (b) rely solely on early adopters despite their high omissions. If influencers are seeded, the optimal contract depends on omission level: (a) non-performance-based contracts are optimal when omission levels are low, and influencers then provide consumer-welfare–maximizing reviews; and (b) sales-based commissions are optimal when omission levels are moderate, and influencers’ reviews are biased towards the seller. From a research perspective, we are the first to highlight influencers’ dual role as consumer-oriented content creators and information intermediaries.

This paper examines how consumer-centric online platforms can design recommendation algorithms that maximize consumer welfare. Motivated by practices on platforms such as Etsy, we analyze a setting where a monopolistic seller offers a product to consumers with heterogeneous valuations through a platform's recommendation. The platform commits to a personalized recommendation algorithm that incorporates both the seller's price and consumer valuations. We characterize the consumer-optimal algorithm and show that it adheres to a threshold rule, using recommendation visibility to incentivize lower prices. This framework gives rise to a novel pricing strategy, which we term Laissez-faire-Pooling-Regulated-Excluded (LaiPREx). In this strategy, low-cost sellers charge monopoly prices, medium-cost sellers pool at a common price, high-cost sellers face regulated prices below monopoly levels, and highest-cost sellers are excluded from the platform. Our findings highlight the trade-off between gains from trade and price discipline, and predict that pooling prices will emerge robustly when the platform cannot enforce price reductions through manipulation of recommendations. We offer testable implications for algorithmic pricing and platform design and extend our analysis to settings with consumer outside options. 

We study monopoly regulation under asymmetric information about costs when subsidies are prohibited.  A monopolist with privately known marginal cost serves a single product market and sets a price.  The regulator maximizes a weighted welfare function using sales taxes as the sole policy instrument.  We identify the sufficient and necessary conditions for regulatory intervention. When intervention is warranted, the optimal policy is a soft price cap: prices below a benchmark face no tax, while higher prices are taxed, potentially at prohibitive rates. This mechanism combines delegation at low prices with taxation at high prices, balancing access, affordability, and profitability. Depending on its cost, the firm either operates under delegation or under taxation. Our results suggest improvement from a pure price cap policy.