It doesn’t matter what event I am speaking at, or who the participants are in the room, I can’t think of a single time I’ve spoken about nonprofit technology topics and not had at least one hand raised in the audience to ask about the number of Facebook fans an organization “should” have, or the “best” time of day to tweet, or even the frequency of posting videos on YouTube. In the book, Beth and Katie identify a number of themes. One that really speaks to me is the point that “measurement helps nonprofits understand and improve their social networks.” Often, nonprofit leaders and staff think of data as something that helps us decide on something new (a new program, a new service, a new engagement opportunity); unfortunately, we don’t always remember that data also helps us make decisions about what we are already doing. Here’s a case study of my own!

To Link or Not to Link

Before I worked at NTEN, I managed the NetSquared program at TechSoup Global. As part of that role, I was in charge of our various social media channels, including Twitter, Facebook, and LinkedIn. At that time, most of NetSquared’s content came from the community blog, where anyone working at the intersection of technology and social impact could create a profile and contribute to the blog with stories, case studies, and more. The content strategy was to reflect the same community focus on the blog onto our other channels, both highlighting community members’ content as well as creating a space where people could continue sharing on their platform of choice. All of that translated to tweeting out links to blog posts all the time.

Watching the metrics every week, though, it was clear we weren’t looking just for traffic to the blog. We were looking to build the same kind of group sharing on social platforms that we were seeing on the blog. We also saw that retweets as a total or an average really varied. So, we dug deeper. In an attempt to ensure that we connected the “why” of whether something was successful or not to the “what” of the content itself, I started splitting apart our metrics. Instead of watching the average or total retweets for the week, I was looking at the number of tweets that went out with a link and the number that went out without a link, and the corresponding rewteets.

Wow, that’s when things got interesting! I started to see that the more I posted without a link or retweeted someone else’s link, the more response and engagement I built within the channel (in this case, Twitter). But when I tried to push people off of Twitter and over to the blog posts, there was much less of a response. Having a better understanding of what was going on, I could make a decision about our current strategy (not just a new one for later). To ensure that our content stayed balanced, I made sure that our metrics tracking documents separated % of posts with a link from those without a link and set goals for the weekly post balance.

Get your copy!

I am giving away a copy of Beth and Katie’s book to be sure that I do my part to spread the knowledge and share the insights. But, I don’t just want to mail it someone, I want to use this as an opportunity to catalyze some peer sharing! Please leave a comment below about how you’ve used data to help make a decision about the social channels you were already using. Just getting started? Share what you’d like to try! I’ll select a comment at random to win the book and we can all win by learning from each other. (I’ll pick someone on Friday, December 7th – so hurry!)

Why the dog photo? Well, that’s my dog and he’s pretty cute, but Beth and Katie are also running a dog vs cat photo contest.

Fenton just released a terrific guide book focused on actionable metrics: “See, Say, Feel, Do: Social Media Metrics that Matter”.

It is tempting to imagine social media as its own communications island where Twitter Follower counts are valuable currency that can be exchanged for internal high-fives. In this scenario there is no way to exchange social media coins for broader communication or marketing dollars. But who cares when you can boast 50,000 likes on your Facebook Page.

This is the wrong approach for three reasons: 1) it doesn’t acknowledge the complete media consumption behavior of your target audiences; 2) it cuts off feedback loops that can be mined for insights that will improve engagement and returns and 3) it limits the reach and impact of your broader communications.

Fenton’s guide divides up the kinds of metrics you can focus on under the headings See, Say, Feel and Do. As I suggest in my Community Mapping approach, the actions and metrics should be mapped against the segments of the community doing them. I really appreciate that Fenton has included a similar recommendation. I also love that they include both a space for identifying and tracking the data, as well as a place to include the insights and actions associated with it. The best of the guide book, though, is the inclusion of an example reporting form and a template to use in your own organization.

Download “See, Say, Feel, Do: Social Media Metrics that Matter” here. 

What do you think? If you downloaded the report, did you find the template useful? What aspects of the do/see/say/feel metrics approach were you already using or do you plan to try out?