Estimating Network Effects in Two-Sided Markets

ABSTRACT

The proliferation of the Internet has enabled platform intermediaries to create two-sided markets in many industries. Time-series data on the number of customers on both sides of the markets allow platform intermediaries for estimating the direction and magnitude of network effects, which can then support growth predictions and subsequent information technology (IT) or marketing investment decisions. This article investigates the conditions under which this estimation of same-side and cross-side network effects should distinguish between its impact on the number of new customers (i.e., acquisition) and existing customers (i.e., their activity). The authors propose an influx-outflow model for doing so and conduct a simulation study to benchmark the new model against the traditional model. Further they compare the models in an illustrative empirical study in which they study the growth of an Internet auction platform. The results show that this separation of effects is beneficial because the existing customers on both sides of the market can influence the acquisition and dropout of other customers asymmetrically. The paper thus makes an important contribution that should impact the way how researchers and business practitioners measure network effects in two-sided markets.

KEYWORDS:

• Two-sided markets
• electronic commerce
• online intermediaries
• customer churn
• customer acquisition
• platform economy

Acknowledgements

We also thank Tim Kraemer for helping us to start this project and for his support throughout the earlier phases of this project.

Notes

1. In the context of this paper, “asymmetric network effects” mean that the installed base of customers makes it easier to acquire new customers (inflow), but harder to keep existing customers (outflow) and vice versa.

2. In principle, such models could also make use of other signals such as messages that buyers share in forums or online social networks [20] or user-generated content in general. Plattform.com did not allow such interactions to circumvent the bypassing of the platform. Purchases are however in all cases the strongest and most credible signal that can be used to infer the installed bases for market participants. Therefore, the majority of models in the area of “customer base analysis” use this information.

3. A platform operator could also use other signals of activity as an input for sellers’ churn model, such as the creation of offers or activities in the back end of the system. Unfortunately, we do not have access to such data, which prevents us from using this promising alternative approach to model the number of sellers.

4. We assume constant network effects over time, which is a commonly made assumption.

Additional information

Funding

This work has been [co-]funded by the DFG as part of the CRC 1053 MAKI and by the efl – the Data Science Institute at Goethe University Frankfurt.

Notes on contributors

Oliver Hinz

Oliver Hinz ([email protected]; corresponding author) is Professor of Information Systems and Information Management at Goethe University Frankfurt, Germany. He is interested in research at the intersection of technology and markets. His work has been published in such journals as Information Systems Research, Journal of Management Information Systems, MIS Quarterly, Journal of Marketing, and Decision Support Systems, and in a number of proceedings the leading IS conferences.

Thomas Otter

Thomas Otter ([email protected]) is Professor of Marketing at Goethe University. His research focuses on Bayesian modeling with application to marketing. He has worked in the areas of conjoint measurement, choice modeling, and assessing the effectiveness of marketing actions when the actions are endogenous to the system. Dr. Otter’s papers have been published in Journal of Marketing Research, Marketing Science, Quantitative Marketing and Economics, Journal of Business & Economic Statistics, and other journals. He is co-editor of Quantitative Marketing and Economics and member of the editorial review boards of Marketing Science other journals.

Bernd Skiera

Bernd Skiera ([email protected]) holds the Chair of Electronic Commerce at Goethe University and is also Professorial Fellow at Deakin University. Australia. His interests include e-commerce, marketing analytics, online marketing, customer management, and integration platforms as a service (iPaas). Dr. Skiera has published in such journals such as Management Science, Journal of Management Information Systems. Marketing Science, Journal of Marketing Research, and others. He was a recipient of an ERC Advanced Grant in 2019.