What is Crowdsourcing?

Crowdsourcing involves gathering work, information, or opinions from a large group of people who submit their data via the Internet, social media, and smartphone apps.

People involved in crowdsourcing sometimes work as paid freelancers, while others perform small tasks voluntarily. This is becoming a popular method to raise capital for special projects. As an alternative to traditional financing options, crowdsourcing taps into the shared interest of a group, bypassing the conventional gatekeepers and intermediaries required to raise capital.

The advantages of crowdsourcing include cost savings, speed, and the ability to work with people with skills that an in-house team may not have. If a task typically takes one employee a week to perform, a business can cut the turnaround time to a matter of hours by breaking the job into many smaller parts and giving those segments to a crowd of workers.

What Are the Main Types of Crowdsourcing?

Wisdom: Wisdom of crowds, a concept popularized by James Surowiecki, is the idea that large groups of people are collectively smarter than individual experts when it comes to problem-solving or identifying values (like the weight of a cow or number of jelly beans in a jar).

Creation: Crowd creation is a collaborative effort to design or build something. Wikipedia and other wikis are examples of this. Open-source software is another good example.

Voting: Crowd voting uses the democratic principle to choose a particular policy or course of action by “polling the audience.”

Funding: Crowdfunding involves raising money for various purposes by soliciting relatively small amounts from a large number of funders.

The Bottom Line – Does Netflix Use Crowdsourcing?

Yes. Netflix uses crowdsourcing to help improve its entertainment platform. Most notably, in 2006, it launched the Netflix Prize competition to see who could improve Netflix’s algorithm to predict user viewing recommendations and offered the winner $1 million.

By sharing a dataset of 100 million anonymous movie ratings, Netflix encouraged participants to develop algorithms that could predict movie ratings more accurately. The winning team achieved a 10.06% improvement over Netflix’s original recommendation engine.