It’s relatively easy to consolidate monitoring data by time and to segment it by shared metrics. For example, conversions by country can be compared to historical performance within that country, and periods of poor performance can be aligned with bounce rates to see if there’s a correlation.
A far bigger challenge, however, is linking individual visits to individual outcomes. In essence, the web monitoring industry is moving from a focus on visits to a focus on visitors. The individual visitor—either named or anonymous—must be tracked across social networks and the conversion process in order to understand the return on any marketing investment. It’s a return to the days of database marketing.
Figure 18-1. Facebook’s business solutions include onsite advertising, the Lexicon analytics platform, paid pages for companies, and tools such as sharing and Connect to encourage users to share their actions
Identifying an individual visitor is critical. This happens in several ways:
- Visitor self-identification
Visitors sign up for a federated social model, such as Facebook Connect, Microsoft Passport, or OpenSocial, which shares their activities with others (Figure 18-1). The information may be anonymized by the owner of the social platform, but already, tools like Radian6 rely on OpenSocial to identify key influencers in a social network.
- Grammatical links
Text from comments and blog posts are analyzed using language-parsing algorithms that try to identify a single person’s online identity from the content she creates. This works for prolific web users for whom a considerable amount of content exists.
- Metadata and fuzzy association
Many social networks include basic information about users, such as their first names or the cities in which they live. By combining this information, analytics systems can make educated guesses about who’s who and try to join multiple online profiles into one metaprofile.
- Understanding the flow of messages and referrals
Most social conversations carry threads within them that can be tracked. On Twitter, words like “via” and special characters such as “RT” (reTweet) signify information that’s being passed down the long funnel. Tracking tools can follow these threads to examine the social spread of a message (Figure 18-2).
- Embedding tracers in online activity
For years, analytics companies have relied on cookies to persist information on browsers so they can track new versus returning users. As we’ve seen, with the emergence of short message interactions like Twitter messages and Facebook status updates, there’s nowhere to easily hide tracking information other than within the URL.
- Asking visitors to log in
Sometimes the simplest solutions are the best. Asking visitors to sign in so that they can be uniquely identified and tracked is the surest approach to tracking. It’s also less risky from a legal and ethical standpoint, since site operators have the visitor’s permission to know who they are.
Figure 18-2. Social messages, such as these ones on Twitter, contain clues about the spread of social conversations, in some cases even documenting when a social message changes platforms
Web analytics companies that focus on tracking visits are quickly retooling to track individual visitors, either anonymously or as known individuals, linking user logins to Twitter accounts and blogs. Of course, harvesting and mining every individual’s interaction across the Internet has serious privacy concerns, but it also steers web analytics toward the realm of CRM and a one-to-one relationship with a company’s customers.
A corollary to the move from visits to visitors is that personal identity becomes currency. Companies and individuals will start to manage—and defend—their online reputations as personal currency that gets them jobs and gets their voices heard. Online reputation is the twenty-first century’s reference letter.
Expect to see certified profiles and recommendations provided by the community. Already, highly ranked contributors on SAP’s community site use their community-granted awards on their Linkedin profiles, leveraging them as proof of their knowledge and ability to work with others when seeking new employment.
These reputations will have a strong influence over which links communities follow, and personal credibility scores will factor into community analytics. Metrics such as friend count, number of followers, and other metrics of popularity are already giving way to more sophisticated measurements on sites like Twitalyzer, such as the reach of a reTweet or the number of community members with whom a person converses regularly.