Grubhub is attempting to increase network effects and increase barriers to multi-homing for customers on both sides of their platform
What's so special about this Boston-based alcohol delivery company?
Craigslist grew rapidly by leveraging hyper-local network effects – but now faces market share erosion as other platforms enter niche segments. Who will win, and how?
This post discusses Netflix’s reliance on network effects, and risks and benefits of their current model to attract users and content owners to their platform.
Google Maps has evolved into a platform boasting powerful direct and indirect network effects.
Introduced as a messaging app in 2011 by Tencent, WeChat has evolved into lifestyle platform for users in China. With ~850 million monthly active users, it now offers to its users what Facebook, WhatsApp, Messenger, Venmo, Grubhub, Amazon, Uber, Apple Pay, etc. together offer in the West. The blog discusses how WeChat has used the strong network effects to emerge as the one app that rules them all.
OfferUp is transforming how we sell used goods online and is termed a “Craiglist killer”. This post discusses how they ended up creating and capturing this value
Waze, an app that tracks real time traffic and road conditions from drivers’ phones, uses huge amounts of data to better route people to their destinations.
Sift Science uses machine learning and pattern recognition to detect and prevent fraud. The company has strong network effects and has managed to create and capture value by improving its product over time.
With the help of the crowd, Waze is rapidly enhancing the way we navigate.