The Social Media Intelligence Framework

I’m finishing up a presentation on Social Media Intelligence with my co-author, Mark Eduljee of Microsoft, for our upcoming keynote at the Text Analytics Summit in San Fransisco. I thought I’d give you a little sneak peak at what we’re working on. Also, check out a recent post on social media intelligence to get more info on constructing a social media intelligence system.

The social media intelligence framework

Notice the wheel shape for the social media intelligence framework. The shape shows that analytics is a system whereby you listen to increase ROI, then use insights to do more to optimize your ROI.

So, let’s take a look at the elements comprising this framework.

Purpose

Too much data describes the biggest problem in social media analytics. Every day, millions of pieces of data about your brand show up in blogs, news sites, social media platforms, and your own website. Managing this mountain of data requires a purpose — or goal. Maybe you want to improve customer satisfaction; maybe you want to explore shopping cart abandonment. Think KPIs (Key Performance Indicators) and monitor those KPI metrics likely to make the biggest impact on your market performance.

Set goals for these KPIs and monitor your success. Explore WHY you’re not reaching your goals.

Balance qualitative data with quantitative data

Because it’s easy to monitor quantitative data with tools like Google Analytics, businesses may ignore qualitative data. Yet, according to an IBM study, 80% of data is unstructured — qualitative. You need to strike a balance between qualitative and quantitative data to ensure your gain insights on your customers that help build better products and services to satisfy their changing needs.

Create a structured process involving people and tools

Remember, tools aren’t solutions and they aren’t processes. Tools are things that help you create solutions. Use a dashboard system, like IBM Cognos to bring insights from different platforms together into easily understandable insights. Establish criteria for which data is collected for each level of reporting, how data is best visualized to highlight issues requiring attention, and how often reports are created.

Take action

It’s not enough to just collect data and display it visually. You need to analyze the data to provide interpretations that guide action.

For instance, it’s nice to understand how many visitors came to your website, where they came from, and what they did on your site. That’s descriptive data. Nice, but not insightful and doesn’t really guide actions.

What you need is predictive data — data that draws relationships between events to guide actions. Let’s assume you track site performance with standard metrics like # of visits, # of visitors, bounce rate, time on site, etc. Knowing that 234,043 folks visited your site on a particular day might give you a psychological boost (or depression — depending on “normal” site statistics), but it’s not really actionable. Instead, investigate WHY something happened so you can repeat good performance and reduce bad performance.

Let me give you a short example. About a month ago, I published a post that brought about 3X as many visitors to my site as normal for a single day. Cool. I felt really good.

But, why? Now, I know, you don’t want to look a gift horse in the mouth, but I really wanted to know what happened so I could do it again. I snooped around my stats to find that an abnormally high number of visits came from Business2Community, which re-purposes most of my content on their curation site. I went to my recent posts on B2C and found an abnormally high number of folks shared one particular post in their own social networks (of course, I knew which post was likely the source from my Google Analytics page content report), which amplified the content and drove site visits from a number of platforms. Now, I have information that helps me create more content like this post.

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