Saturday, July 31, 2010

How to enable social discovery of content

Consumers spend a significant amount of time online building digital profiles and feeding preference engines on web sites that promise an intelligent algorithm to understand their tastes and likely behaviors. While consumer volunteered preferences may seem highly relevant and useful as a means to offer content recommendations on digital experiences, it also seems like an overly complex way to address this simple need.

Another solution to address the consumer need of useful and relevant content lies in the old metaphor of the “office water cooler.” As a contemporary metaphor, the water cooler represents a consumer’s social network across multiple digital channels. A consumer’s social network, like the water cooler, is an oracle (rather, it is a gathering place for oracles). This metaphor, in its original meaning, suggested that a group of colleagues gather around the water cooler on Monday morning to discuss the TV shows they watched on Sunday evening. The modern translation of this concept is that a consumer’s social graph is his/her most relevant source for entertainment, recommendations, and useful information. While the water cooler scenario described a sphere of influence that was confined to the office, the success of social networks, such as Facebook, proves that a potential sphere of influence for today’s consumer includes his/her family, friends, peers, colleagues, and former classmates.

Considering the tremendous success of Facebook, it is obvious that there is great potential in leveraging a consumer’s social graph to serve relevant content and recommendations. The best application of this strategy on a digital experience is to build a platform for highly connected social networks to generate recommendations to members of the same social graph for the purpose of driving engagement. The tactic works especially well with brands that already have credibility and presence within a particular industry. Stated another way, social discovery of content is most successful on digital platforms that already have large audiences. To further drive engagement on such a platform, it is a matter of “re-tooling” it to support secondary social tasks, such as the ability to create lists and recommendations that can be shared and used by members of the same social graph, to discover new and useful content.

Peer-influenced recommendations have the potential to be more effective than recommendations generated from an algorithm based on a consumer’s own behavior. It is typically assumed that the success of consumer targeted tactics is based on relevancy to the consumer. For example, a web site may offer consumer ratings and reviews. These content recommendations may be more relevant to a consumer visiting a web site than editorially-driven ratings or reviews (given the potential for bias, based on advertising agendas). The tier of relevance described in the above scenario includes all web site visitors, and while that level of relevance may be more influential to consumers than an editor’s perspective, greater relevance may be achieved by utilizing a consumer’s own behavior (viewing content, bookmarking content, shopping, etc.) to generate recommendations. There are, however, challenges associated with the creation of a technology-driven solution to collect, understand, and utilize a consumer’s own behavior to provide more relevant content:

1. It's complicated. A significant investment is needed to build a database and relevancy algorithm that improves over time (based on the collection of consumer behavior over time).

2. It eliminates the element of surprise. For example, on an ecommerce web site, recommendations based on a consumer’s previous purchases will likely result in products that the consumer would normally seek out, via the traditional modes of search and discovery (i.e. - web site search and navigation). Relying on previous purchases and content consumed eliminates surprising discoveries that the consumer’s social sphere of influence might surface based on each peer’s unique preferences. It is a simple principle. People are friends with other people that are similar to them, but not identical. There are always members of a social sphere of influence that are taste-makers to other individuals within the same social network. That’s the primary reason a technology-driven solution that focuses on an audience of one will fail to provide the surprise and “true discovery” that a social network can provide to a consumer.

The mechanics of implementing social discovery of content on a particular digital experience may not be easy; however, there are platforms and design patterns in existence that may be leveraged. Given its popularity, Facebook connections to allow consumers to view their friends' activity relevant to the digital experience, should be considered a primary tactic. If a digital platform already has its own community, it may be a matter of extending this community to include Facebook connections, then adding social functionality to enable the larger community to create shareable lists from the content ("top 10 videos to watch on a Sunday evening," etc.).

As always, understanding what is relevant to consumers requires an in-depth understanding of their likely behaviors, motivations, and preferences. This level of understanding is obtained through customer research, and should be used as a foundation to determine what tactics will likely be successful.