The 90-9-1 Principle in product development

In the world of digital communities, user engagement is a vital metric for measuring the success of a platform. The 90-9-1 principle offers a useful framework to understand the dynamics of user participation in these communities. This principle suggests that within an internet community such as a wiki, 90% of participants only consume content, 9% edit or modify content, and 1% of participants add content1.

Let's look at three examples of how the 90-9-1 principle applies to digital software products:

Social Media Platforms

On social media platforms like Facebook or Instagram, the vast majority of users primarily consume content created by others. They browse through their feed, read posts, and watch videos but rarely contribute. A smaller fraction of users will actively engage by liking, commenting, or sharing posts. The smallest group, however, are the content creators who regularly post original content and actively shape the community.

Online Forums and Discussion Boards

In online forums or discussion boards like Reddit, the 90-9-1 principle is very apparent. Most users browse threads and read discussions, a smaller percentage comment and engage in the discussion, and an even smaller group starts new threads or posts original content. Understanding this dynamic can help in designing features that encourage more active participation from users.

User-Generated Content Platforms

Platforms like Wikipedia or GitHub, where the content is mostly user-generated, also follow the 90-9-1 principle. Most users are there to use the information or code shared by others. A smaller group will make edits or modifications, and the smallest group consists of those who add new articles or repositories.

Conclusion

In conclusion, the 90-9-1 principle provides valuable insights into user behavior in digital communities. Recognizing these patterns can guide the design and development of digital software products, allowing creators to better cater to the needs of their user base, and potentially devise strategies to encourage more active participation. As we design the digital products of the future, understanding and leveraging these engagement dynamics will be key to creating vibrant, engaging online communities.

See also

The Pareto Principle (80/20 Rule)

Sources

1% Rule on Wikipedia

The 1% Rule in Four Digital Health Social Networks: An Observational Study

Background: In recent years, cyberculture has informally reported a phenomenon named the 1% rule, or 90-9-1 principle, which seeks to explain participatory patterns and network effects within Internet communities. The rule states that 90% of actors observe and do not participate, 9% contribute sparingly, and 1% of actors create the vast majority of new content. This 90%, 9%, and 1% are also known as Lurkers, Contributors, and Superusers, respectively. To date, very little empirical research has been conducted to verify the 1% rule. Objective: The 1% rule is widely accepted in digital marketing. Our goal was to determine if the 1% rule applies to moderated Digital Health Social Networks (DHSNs) designed to facilitate behavior change. Methods: To help gain insight into participatory patterns, descriptive data were extracted from four long-standing DHSNs: the AlcoholHelpCenter, DepressionCenter, PanicCenter, and StopSmokingCenter sites. Results: During the study period, 63,990 actors created 578,349 posts. Less than 25% of actors made one or more posts. The applicability of the 1% rule was confirmed as Lurkers, Contributors, and Superusers accounted for a weighted average of 1.3% (n=4668), 24.0% (n=88,732), and 74.7% (n=276,034) of content. Conclusions: The 1% rule was consistent across the four DHSNs. As social network sustainability requires fresh content and timely interactions, these results are important for organizations actively promoting and managing Internet communities. Superusers generate the vast majority of traffic and create value, so their recruitment and retention is imperative for long-term success. Although Lurkers may benefit from observing interactions between Superusers and Contributors, they generate limited or no network value. The results of this study indicate that DHSNs may be optimized to produce network effects, positive externalities, and bandwagon effects. Further research in the development and expansion of DHSNs is required.

www.jmir.org