Digital agencies find themselves in a strange situation today. After convincing marketers that the Web is a better advertising medium than print because it can be measured, they are now trying to argue that Web 2.0 is even better than Web 1.0, even though it can’t be measured.
Web 2.0, or social media, can be measured, as long as we aren’t led astray by three narratives, which are only partially true.
Dealing With Flawed Measurement Shibboleths
According to the first business-as-usual narrative, the metrics we measure on social media should be the same business metrics we measure offline. The metrics might include lead conversions for the sales function, brand loyalty for the marketing function and customer satisfaction for the customer support function. The decision on whether to invest in social media programs should be taken based on the effectiveness of these programs relative to other (offline) programs in achieving business objectives.
According to the second ad-value-equivalence narrative, buzz is the single most important metric to track on social media. The decision on whether to invest in social media programs should be taken based on whether the value of the buzz created by these programs is higher than the visibility generated by spending the same money on advertising.
According to the third markets-are-conversations narrative, social media is about engaging in conversations and building relationships and businesses shouldn’t even be trying to measure it. A version of this narrative argues that social media is a fundamental game changer and businesses that do not adapt to it will risk being left behind. So, not engaging with social media isn’t a viable alternative for businesses anymore.
All three narratives have some truth in them, but they are flawed because they fail to factor in the multilayered nature of social media. As a result, most of the discussion revolves around the relative merits of focusing on return on investment (ROI) or engagement, while no one really agrees on what these terms mean.
What to Measure: Content, Collaboration, Community and Collective Intelligence Metrics
If you cut through the tools and the terminologies, social media is about four underlying themes: Content, Collaboration, Community and Collective Intelligence. Taken together, these four C,s constitute the value system of social media.
The First C: Content
The first C refers to the idea that social media tools allow everyone to become a creator, by making the publishing and distribution of multimedia content both free and easy, even for amateurs.
At the content level, the design challenge is to factor in the 1:9:90 rule, which says that 90% of all users are consumers, 9% of all users are curators and only 1% of the users are creators.
Content that is easy to find and easy to spread becomes popular, so the key content metrics are popularity, virality and findability. Popularity metrics include pageviews, clicks and time spent. Virality metrics include comments, trackbacks, bookmarks, votes and retweets. Findabilty metrics include the entire range of search engine marketing (SEM) and search engine optimization (SEO) metrics.
The Second C: Collaboration
The second C refers to the idea that social media facilitates the aggregation of small individual actions into meaningful collective results.
At the collaboration level, the design challenge is to raise the game from conversations to co-creation and collective action.
The key collaboration metrics are conversations, contributions and transactions. Conversation metrics are similar to the quantitative virality metrics, but are more qualitative in nature and factor in context, influence and sentiment. Contribution metrics include the quantity and quality of user submitted content, including feedback on current products/ processes and ideas for new products/ processes. Transaction metrics are primarily business metrics and include lead conversions, complaint closures and customer recommendations.
The Third C: Community
The third C refers to the idea that social media facilitates sustained collaboration around a shared idea, over time and often across space.
At the community level, the design challenge is to identify a relevant social object and build a large and vibrant community around it.
The key community metrics are membership, relationships and interactions. Membership metrics include the number and profile of the community members. Relationship metrics include the number and nature of connections between community members. Interaction metrics include the frequency and nature of interactions between community members.
The Fourth C: Collective Intelligence
The fourth C refers to the idea that the social Web enables us to not only aggregate individual actions, but also run sophisticated algorithms on them and extract meaning from them.
At the collective intelligence level, the design challenge is to aggregate our individual and collective actions in databases, and run sophisticated algorithms on them to build reputation and recommendation systems.
The key collective intelligence metrics are sentiment, authority and predictability. Sentiment metrics include the strength and nature of positive and negative reactions, in a given context. Authority metrics include the influence of an individual or a group, within a given context. Predictability metrics include the precision and accuracy of the forecasts about the market or the company based on social media data mining.
The 4C’s form a hierarchy of what is possible with social media. Each layer is often a prerequisite for the next layer, and, as we move from content to collaboration to community to collective intelligence, it becomes increasingly difficult to both observe these layers and activate them. Also, the nature of the metrics changes from simple to complex and the role of human analysis increases, as machine analysis reaches its limits.
How to Measure: Web Analytics, Network Analysis and Text Analysis
It’s impossible to measure all these metrics by any one tool or approach, so social media analytics needs to incorporate three different elements: onsite/offsite Web analytics, network/influence analysis, and semantic/content analysis.
Popularity, findability and transaction metrics are in the domain of Web analytics. Membership metrics are in the domain of network analysis. Sentiment metrics are in the domain of content analysis. Virality metrics are at the intersection of Web analytics and network analysis. Contribution metrics are at the intersection of Web analytics and content analysis. Relationship metrics are at the intersection of content analytics and network analysis. Authority, conversation, interaction and predictability metrics need a combination of all three types of analysis.
Google Analytics, WebTrends and Omniture are some popular Web analytics tools. Linkfluence, Buzzlogic and MorningSide Analytics offer popular network analysis tools, while Radian6, Techrigy and Collective Intellect are some popular content analysis tools.
All Metrics Must Link Back to Business Objectives
While transaction metrics are most directly linked to business objectives, in the end, all social metrics must link back to business objectives. One way to ensure this is to follow the five step of a social media program—planning, listening, understanding, engaging and monitoring—and ensure that social media metrics are tied in to each of these five phases.
This is excerpted from PR News Guide to Best Practices in PR Measurement, Volume 4. It was written by Gaurav Mishra, the co-founder of social media research and analytics company 20:20 WebTech. To order the guidebook or find out more information about it, go to www.prnewsonline.com/store.