Taming Social Media Data


I’m a Business Intelligence Analyst. I’m also a data geek. So when a client recently asked me to oversee the development of a comprehensive Social Media report, for monthly presentation to their upper management, I was both excited and wary.

Excited Me: Cool! The latest thing! A vast untapped resource of dialog and opinion straight from customers!

Wary Me: This stuff is mostly free-form! How are we ever going to glean relevant, actionable data from these sources? It’s like the Wild West out there…

Some basic social metrics are easy to gather and interpret. How many Facebook likes did we add this month? How many unlikes? How many Tweets were made with a hashtag we might expect, like #ProductName? How many people liked our client’s latest Facebook post? It’s a good start, but our client’s management wanted more in-depth insight.

There are several tools available for social media analytics. Visible Intelligence, Radian6, Sysomos (MAP and Heartbeat), Nielsen’s BuzzMetrics, Crimson Hexagon, etc. They all offer valuable insight but go about it in very different ways. Instead of picking a tool and prying out what we could, we worked backward from our client’s requirements. We tried various tools, analyzed the results, and chose the solutions that provided the most relevant analyses for their needs. Here’s a partial list of some of the in-depth analyses we were able to generate:

Buzz Volume: Using keyword searches across Facebook, Twitter, and select blog, video, and photo sites, we came up with a “buzz” volume that we could track over time. This is good to watch for overall social interest—for example, to see the buzz from rolling out a redesigned website or a new product version.

Sentiment: Some tools are quite good at classifying posts by sentiment: positive, negative, or neutral. Tracking counts of posts by sentiment month-to-month proved to be very useful, particularly around new product version releases and press releases. For further insight we picked out specific sample posts, positive and negative, and provided links to the actual posts.

Topics by Media Type: This breaks out a percent of posts by where they occurred: Facebook, Twitter, blogs, etc. Watching shifts in these volumes proved valuable. For example, we knew that discussions in this client’s forums tended to be mostly about troubleshooting technical issues, so an increase in forum buzz proportionally to other sources could indicate an emerging technical issue.

Top Contributors: We identified the URLs of forums, URLs of blogs, and handles of Twitter accounts providing the highest number of posts in the month. This provides a nice short list for further investigation of who is “buzzing” about our client’s product the most and might be worth approaching for a more formal relationship. For example, a person who frequently blogs about usability of the product might be an excellent person to have as a beta tester.

Competitive Analysis: Why just track buzz volume for the client’s product? We track buzz volume for competitors’ products as well, then combine and track over time to see how our client stacks up. The percentage that our client’s buzz contributes to the overall buzz in their category could be termed “Share of Voice” and is a good metric to track. When our client releases a new version, does their Share of Voice go up? If not, do they need to retool their launch marketing efforts?

Website Visits by Referring Domain: Most common web traffic tracking tools can report the domain from which a visitor came. Because many of our client’s social media posts include a call-to-action to click on a link to our website, we can track referrals from twitter.com, linkedin.com, etc. to determine how much web traffic is being driven by their social campaigns.

Success Events by Referring Domain: Going one layer further with web analytics, we worked with our client to define specific “success events” we wanted to track. These were desirable actions taken once the visitor landed on the client’s website, such as watching a video, reading a whitepaper, or downloading a software trial. Analyzing these success events by referring domain allowed us to see the level of engagement of visitors from social sites. Combining Visits and Success Events, both by Referring Domain, we calculated a “Success per 100 Visits” metric-another good way to see how effective our client’s social media posts are at engaging customers.

Blog Traffic Analysis: We used standard web analysis tools to track interaction on our client’s blog. Initially, their blog showed entire articles on the main page. To enable more detailed analysis, we drove changes to their site structure so that the home page shows only the first several lines of each article with a “Read more” link. This way, each full article receives a unique URL, and we’re able to track how many in-depth reads each article is receiving.

Blog Traffic by Referring Domain: Again, it’s interesting to see how people are getting to our client’s blog. This analysis reveals some noteworthy sources of links to their blog articles, and it can also identify sites that could be approached for a more formal relationship.

In its infancy, the process of analyzing social media data can yield some very helpful insights for marketers and managers. It’ll be exciting to see how the tools evolve—no doubt providing richer and more accurate data in the future—and to discover how we can further leverage this rich source of customer feedback data to benefit our clients.

This entry was posted in Business Analytics, Marketing Musings, Social Media by Tom Pollock. Bookmark the permalink.

About Tom Pollock

Tom has been in a variety of marketing roles since 1989 for companies such as White Cap (a food packaging manufacturer), Eddie Bauer, and Microsoft. While working full-time (in the absence of a real life), Tom attended Northwestern University in Illinois, earning a BS degree and an MBA in Marketing from the Kellogg School. Having come to marketing from several IT roles, Tom brings a technical skill set not often seen in a marketer, making him as comfortable handling the details of data analysis, data mining, and database administration as he is driving marketing strategy and vision. He has been in on the ground floor of three major CRM system rollouts. Tom’s key strengths are bridging the gap between business and technical roles, and using quantitative and qualitative data analyses to drive business strategy and decision-making. It's not all work: Tom has produced several CD projects for local musicians. And as a native Chicagoan, Tom is an avid Cubs fan, but he thinks the Mariners are cool too.

One thought on “Taming Social Media Data

  1. Sounds like you did everything perfectly, Tom. I always tell people to figure out what their goals are and how you want to show that you;ve reached them and from there choose a tool that can help you show that data. Of course I always hope that they choose our tool, but if they don’t I understand because it’s their business and they need to do what’s right for it.
    Sounds like you have all your eggs in the right basket though.

    Sheldon, community manager for Sysomos

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