The Two-Sided Market Network Analysis Based on Transfer Entropy & Labelr

TL;DR

This paper introduces a method using Transfer Entropy to identify key users in complex two-sided digital markets, aiding product managers in making data-driven decisions to enhance network effects.

Contribution

It applies Transfer Entropy to detect super users in two-sided markets, redefining decision criteria for product managers in complex digital platform environments.

Findings

Identifies super users who connect different networks.

Helps prioritize users with strong network effects.

Supports data-driven decision making for platform growth.

Abstract

This study more complex digital platforms in early stages in the two-sided market to produce powerful network effects. In this study, I use Transfer Entropy to look for super users who connect hominids in different networks to achieve higher network effects in the digital platform in the two-sided market, which has recently become more complex. And this study also aims to redefine the decision criteria of product managers by helping them define users with stronger network effects. With the development of technology, the structure of the industry is becoming more difficult to interpret and the complexity of business logic is increasing. This phenomenon is the biggest problem that makes it difficult for start-ups to challenge themselves. I hope this study will help product managers create new digital economic networks, enable them to make prioritized, data-driven decisions, and find users who can be the hub of the network even in small products.