Pros vs Joes: Agent Pricing Behavior in the Sharing Economy

Jun  Li; Antonio  Moreno; Dennis J. Zhang

Working Paper | 2016

Pros vs Joes: Agent Pricing Behavior in the Sharing Economy

by Jun Li, Antonio Moreno and Dennis J. Zhang

Abstract

One of the major differences between markets that follow a “sharing economy” paradigm and traditional two-sided markets is that the supply side in the sharing economy often includes individual nonprofessional decision makers, in addition to firms and professional agents. Using a data set of prices and availability of listings on Airbnb, we find that there exist substantial differences in the operational and financial performance of professional and nonprofessional hosts. In particular, properties managed by professional hosts earn 16.9% more in daily revenue, have 15.5% higher occupancy rates, and are 13.6% less likely to exit the market compared with properties owned by nonprofessional hosts, while controlling for property and market characteristics. We demonstrate that these performance differences between professionals and nonprofessionals can be partly explained by pricing inefficiencies. Specifically, we provide empirical evidence that nonprofessional hosts are less likely to offer different rates across stay dates based on the underlying demand patterns, such as those created by major holidays and conventions. We develop a parsimonious model to analyze the implications of having two such different host groups for a profit-maximizing platform operator and for a social planner. While a profit-maximizing platform operator should charge lower prices to nonprofessional hosts, a social planner would charge the same prices to professionals and nonprofessionals.

Keywords: two-sided market; sharing economy; Behavioral economics; revenue management; hospitality; Two-Sided Platforms; Price; Behavior; Experience and Expertise;

Language: English

Format: Print

SSRN

Citation:

Li, Jun, Antonio Moreno, and Dennis J. Zhang. "Pros vs Joes: Agent Pricing Behavior in the Sharing Economy." Michigan Ross School of Business Working Paper, No. 1298, August 2016.

The first two cases in this series are set in the financial services industry, and explore whether it is better for back-office workers to be generalists who provide the flexibility of being able to handle the complete range of transactions that the company faces or specialists who focus on a subset of the overall transaction mix. The objective is to introduce the advantages of demand pooling in reducing the variability associated with the forecasting of highly variable workloads, while also demonstrating the benefits to worker learning and efficiency improvements that can come from specialization.

The case is set at the Retail Investor Group Operations of the Vanguard Group, the mutual funds and ETF giant. Karin Risi, the managing director, is faced with a back-office operation that hasn’t kept up with massive transaction growth, one that is struggling under the weight of mounting client phone call and transaction volumes, client complaints, and buckling employee morale. And worse, the press was taking notice. The (A) case details the complexity in the growing backlog and frames the question of whether they should pursue a specialist model. The (B) case, for distribution in class, details the impact of an initial segmentation of workload into eight categories and poses additional questions for class discussion and analysis.

The case opens in August 2020 as Ozgur Tort and Mustafa Bartin, CEO and chief large-format and online retail officer of Migros Ticaret A.S. (Migros), Turkey’s oldest and one of its largest supermarket chains, are navigating Migros through COVID-19 and the unprecedented surge in demand in online groceries. Between the first official case in Turkey in March and August, customers have flocked to online shopping and Migros’ teams have been busy trying to solve the picking, fulfillment, and logistics bottlenecks. In the six months, the company recruited and trained new pickers, expanded its delivery fleet, and converted less busy stores into dark stores. Quick to react, Migros was able to add new customers to its base and was proud of its accomplishments.
Now, unable to forecast how much of the surge in demand for online was here to stay, how should Migros plan for the future of Sanal Market and Hemen? Was there anything the company could do to sustain the number of hybrid shoppers it acquired during the past few months?

Case | HBS Case Collection | August 2020 (Revised September 2020)

Migros Turkey: Scaling Online Operations (A)

Antonio Moreno and Gamze Yucaoglu

The case opens in November 2019 as Ozgur Tort and Mustafa Bartin, CEO and chief large-format and online retail officer of Migros Ticaret A.S. (Migros), Turkey’s oldest and one of its largest supermarket chains, are contemplating what the best fulfillment format and delivery model for the company’s growing online arm, Sanal Market, and its under-30-minutes grocery service arm, Hemen, are. Migros’s online operations had grown over 50% year-on-year in the previous three years, and the target for 2020 was to grow 100%. With all of these considerations in mind, Bartin and Tort needed to decide which levers to pull for the last mile and fulfillment to best serve the future of Migros.

The case chronicles the founding and growth of Migros as well as Sanal Market and lays the ground for food and grocery retail in Turkey complete with the competitive outlook. The case then provides a detailed overview of how Migros built online channels, Sanal Market and its recently introduced Hemen, and how Tort and Bartin have thought about fulfillment and delivery as well as omnichannel mentality. The case goes into detail about the three different fulfillment models, store pick micro-fulfillment center, and dark store, that Migros is piloting in 2019 as well as providing an understanding of what the company is thinking about logistics and last mile delivery as its online sales grow.

While globally, online shopping and consumer preferences were changing fast, particularly in terms of how and with what frequency customers shopped online and how fast they wanted their groceries delivered, Migros was trying to find the optimum model for fulfillment and last mile delivery. What was the best way to optimize last mile with regards to pooling store catchment areas and pooling stores, and which of the fulfillment models would yield the best results in terms of picking speed and accuracy and customer expectations? Additionally, the company needed to figure out how to position Hemen; facing fierce competition from fast-delivery grocery startups, for whom burning cash to achieve growth was not a concern, should Hemen remain as a stand-alone offering or should it be integrated within the Sanal Market app?

Keywords: retail; grocery; Business Model; Emerging Markets; For-Profit Firms; Strategy; Technology Platform; Information Technology; Technology Adoption; Value Creation; Globalization; Competition; Expansion; Logistics; Profit; Resource Allocation; Corporate Strategy; Turkey;

Citation:

Moreno, Antonio, and Gamze Yucaoglu. "Migros Turkey: Scaling Online Operations (A)." Harvard Business School Case 621-026, August 2020. (Revised September 2020.) View Details

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Pros vs Joes: Agent Pricing Behavior in the Sharing Economy

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