2010 seems to be the year that everyone thinks corporations will advance beyond the often awkward and tumultuous stages of “Social Media Puberty” to the more focused stages of Social Media Maturity. Twitter is rife with tweets on the subject. Google’s delivering 700,000 pages on the search term. Social Media Blogging luminaries like Brian Solis, Marshall Sponder and MarketingProfs’ Paul Williams are cogitating and writing about it. In fact, MarketingSherpa recently made the 2010 Social Media Benchmark Report available, which detailed, among other things, the three phases of Social Media Maturity.
We felt that MarketingSherpa didn’t go quite far enough. In our recent post titled, ROI Measurement: The Fourth Phase of Social Media Maturity, we added a new Phase of development. This new Phase captured a company’s evolution beyond the often siloed and isolated, strategic Listening Phase to a phase of social media maturity characterized by quantifying and correlating the performance and activity in one communications channel to the performance and activity of other/all channels. This phase not only makes social media ROI calculation possible but also makes things like advanced trend analysis, multi-spectrum sentiment analysis, and predictive modeling possible.
The chart below features our interpretation and embellishment Marketing Sherpa’s three phases:

Why do we need this fourth phase? I’ll let a far wiser person than myself answer that question. Brian Solis, in his Social Marketing in Twenty Ten, posts states:
“Measuring sentiment analysis, would-be referrals, and increases in share of voice are entry-level techniques that do not necessarily capture the potential of socialized media channels.”
Brian, under the heading “From Information to Intelligence”, further describes how growing corporate social media sophistication will soon necessitate more advanced social media tools:
“Businesses that explored the social landscape in 2009 most likely employed one of the many listening tools available in order to monitor and document activity in popular social networks and blogs. Forrester believes that we will move from an era of listening to one of data mining, trend analysis, and ultimately action. Listening and observation will impact other departments including customer service, PR, among others. . .”
Enter the Social Media Intelligence Engine. This new type of software-as-a-service application will:
- Perform an in-depth analysis of the individual communication channels that form the totality of a brand’s customer outreach, including: SEO, Offline, Email, Blogosphere, Social Networks, CRM, Website, traditional PR/news media and multimedia initiative.
- Measure each channel’s ability to reach your targeted audience according to the quantified performance of its inbound and outbound attributes.
- Map channel attribute performance to intelligence gathered from the social web creating a rich repository of “social intelligence”.
- Diagnose the underlying factors driving over and under performance and provide insight into key performance indicators to optimize channel activity.
- Score individual channel attributes and aggregate them into a channel score, then aggregate individual channel scores into a brand’s score
- A brand’s will quantify a brand’s holistic level of engagement, which is defined as a brand’s ability to attract, engage and retain customers.
Scores, however, can be very tricky things. In fact, Brian Solis warns his legions of readers:
“Take caution however, when determining if out-of-the-box formulas or “scores” will help measure success or progress.”
Many scores can be absolutely meaningless when taken in isolation. Who’s to say that a score of 46 is better than a grade of E? The true value of our social intelligence engine’s scoring methodology is derived from studying the delta or degree of change over time and having sufficient data to correlate that change to specific events, initiatives, campaigns or efforts. True score validity comes from studying and scoring a company and its competitive set to establish a benchmark and then re-scoring periodically to reveal performance fluctuations.
We’re currently executing our methodology manually for our enterprise-level Alpha testers. Through manual computation applied to real-world scenarios, we’re developing the knowledge necessary to fine-tune our automated software solution into the ideal social intelligence engine. Although our bandwidth is limited to a select few Alpha Testers during this phase of our offering’s development, please let us know if you think your enterprise-level organization could benefit from this emerging analysis methodology and that you’d like to be considered for one of our final AlphaTester slots.
Photo by Fractal Artist
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