July 2012 Archives

CrowdFrom the first time I described the three buckets of social media data, I knew that one category was different. Content and activity analysis are built on the lessons from established schools of measurement, and while we argue about the specifics, the objectives aren't so alien. The last category—people data—seems more exotic, and it's the least discussed area of measurement. What do we do with data about people, then?

What are people data?
Social media data provide information about both individuals and groups of people: who they are, who they know, what they care about, what they have to say, where they go… Have you noticed just how much information people are sharing about themselves, both intentionally and unintentionally? Collect it from various sources, and you're looking at people data.

As I mentioned in the introduction post, the boundaries between categories aren't absolute, so you could look at much of the data that does into an analysis of people as either content or activity data. The difference comes about when we start thinking about the people as individuals or as identified groups—the focus is on the people, which is why it's useful to look at the data differently.

Analyzing data about individuals
When using the data to consider an individual, you have several basic options on how to approach the analysis. Remember to think and, not or; there's no value in deciding which approach is the right one until you have a specific objective.

  • Profiling
    Compile a detailed personal profile from multiple sources, merging multiple social account profiles with customer data and content analysis of the person's online activity. The resulting information could provide context to customer service agents or sales reps as they interact with the person.

  • Scoring
    Apply a model to rate someone's influence, authority, or relevance, which might help you prioritize efforts in blogger outreach. You might also view someone as a customer, scoring credit, lead strength, customer value, or loyalty.

  • Predicting
    Activity data linked to an individual might be useful for predicting future behavior. How good is your crystal ball?
Working with data about individuals always runs the risk of turning creepy. I'll get into the balance between privacy and the value of data another time, but be sensitive to the risks as you decide how to use information about individuals.

Analyzing data about groups
Zoom out from the individual view to think about the what the data can tell us about groups of people. First, we might identify different types of groups, and then we can develop profiles that communicate why we're interested in particular groups.

  • Identifying
    Groups come in various forms, both formal and informal. The easiest to profile are organizations with formal membership (which includes employers). More casual groups might form through social network sites, discussion forums, or meetup groups. Finally, we have the extended networks of indirect connections, some of which are conveniently entered into online social networks.

    We might also find value in virtual communities implied by some characteristic, from interest in a common topic to locations, both real and virtual. How information travels in such a community could be useful to understand.

    I've had some interesting conversations on the subject of social network analysis, and how its use in social media isn't necessarily in sync with the science on social networks (in the original, not online, sense). If you understanding that you're mapping something other than social relationships, though, I think there's underdeveloped value in applying network analysis to more data points.

  • Profiling
    Profiling a group is less likely to turn creepy than individual profiling, but there's still a right way to do it. First, describe how the group was identified; for some uses, that may be all the information you need—if you're developing a targeted marketing promotion, for example. Going deeper, think about what the group is interested in and where they go (online and in the real world). Who are their leaders—and what is leadership within the group? What's important to them, and what's their history?

    Before you interact with a group, make an effort to understand their norms. The unwritten rules vary by community, and what works in one setting can be precisely wrong in another. As you work to understand and interact with groups, you're dabbling in anthropology, so you might consider its methods.

Our society is producing an astounding amount of data about people, both as individuals and in groups. It's easy to cross the line into overly intrusive use of the data, but it's hard to find a common definition of where that line is. That's a topic I plan to explore in depth in the coming months.

Photo by James Cridland.

DashMonitoring social media. Measuring social media. Social media analytics. All of these treat social media as data, but social media generate at least three types of data: content, activity, and people. In the last post, I wrote about content data, which is the starting point for listening. This time, let's talk about activity. What are people doing that we can analyze?

What is activity data?
Activity data is just what it sounds like: data about the behaviors of people as they use social media. When we're tweeting, pinning, tagging, posting, commenting, sharing, and liking, the systems we watch are watching us back. It's like web analytics, except that social media support many more activities than most web pages, and the activity takes place on social media sites instead of companies' own web sites.

Analyzing activity data
If you're used to measurement conversations with an unstated assumption that you're talking about content data, you probably talk a lot about sentiment and topics. If you listen to web analytics folks talk about social media for a few minutes, you hear about entirely different metrics: friends, followers, fans, likes, shares, retweets, and more.

Compared to content data, activity data presents a set of harder metrics, meaning there's not much doubt about the actual numbers. They're based on observing the use of features built into the software, rather than an interpretation of someone's writing. There's little ambiguity in clicking on a Like button, for example. It's either been clicked or not. The real question is what that means.

An embarrassment of metrics
The challenge in using activity data is less about the underlying technologies and more about tying them to business objectives. We have a lot of available metrics to choose from, and to complicate things, similar-sounding metrics from different social media sites can't always be compared. Always start with the most important question ("what are you trying to accomplish?"), and be sure you understand what the metrics really represent.

With activity data, the web analytics folks have an advantage, because their existing metrics tend to be closely tied to business performance. They already measure how well their web properties generate interest, leads, and sales. It's not too much of a stretch to extend the marketing funnel to include social media properties, too.

Besides its effectiveness in leading customers directly to the e-commerce store, you might measure social media activity as evidence of customer or community connections (engagement), or think of users as an audience for your messages (reach). Some metrics may have value with minimal interpretation, such as product ratings scores. Any tactic you employ that is designed to lead to an action has the potential to be evaluated with activity data, so—again—what are you trying to do?

Lines that go up and to the right make for successful presentations, if you understand what the line represents and how it relates to the business. Activity data can give you those charts; all you have to do is pick the right metrics. And as you're considering metrics, remember the three types of social media data.

Next: Working with Social Media Data: People and Groups

Screen capture by Darren Krape.

About Nathan Gilliatt

  • ng.jpg
  • Voracious learner and explorer. Analyst tracking technologies and markets in intelligence, analytics and social media. Studying complexity and futures.
  • Principal, Social Target

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