Calculating engagement, part III … social engagement and relative content grouping
Curse Clint Ivy, curse him for being right some of the time! I mean, of course, Clint’s diatribe about my engagement calculation and it’s lack of social (media) value. In his post, Clint gives me credit for at least trying to work out how we can measure engagement, then proceeds to chop to pieces for forgetting about the everyday blogger in my calculations.
Maybe he wasn’t that mean, but it’s late and I’m cranky … and he makes a good point. In my previous posts, I have been assigning some value in my engagement calculation directly to the viewing of specific content on my web site. But, given my respect for Mr. Ivy, and the fact that others have commented about this, I took out the high- and moderate-value content scoring and have substituted (experimentally) a Social Media Index. After looking at my site, I am now scoring the following “social media” activities one can engage in at Analytics Demystified:
- Reading my weblog
- Reading other user generated content on the web site
- Participating in a truly social activity facilitated by my site
- Joining a social network of web analytics people
- Contributing content directly to the web site
- Submitting a comment to my weblog
- Emailing me directly
Yeah, I know that my site is no Digg.com, nor is it Friendster or YouTube, but hopefully you get the gist. The measurement works pretty much the same, regardless of the volume of traffic. The net effect is to, at least in my mind, remove some of the content-specificity from the calculation while improving the metrics ability to help sites understand visitor attraction to activities designed to draw the visitor in.
This list could just as easily include providing a rating, tagging, Digging, etc. Depending on the technology you use, the measurements don’t even need to be direct. Think of my list as a strawman, one that can be brutally beaten into better shape (but, unlike almost everything else I’ve seen so far, one that actually functions now …)
One thing that Clint and I talked about off-line that wasn’t represented in his post (or maybe it was, it is getting later by the minute) was whether the engagement calculation would provide any additional value, relative to “corporate” measurements like conversion rate. I took a look at that, mapping my buyer conversion rate and engagement against the visitor’s session number. I got this:
While it clearly looks like if I don’t get ’em to buy one of my books pretty quickly after they first come to the site my opportunity to convert goes down pretty fast, the opposite is true for engagement. It actually appears that, at least on my site, there is a sweet spot for visitor engagement between about 40 and 50 sessions … heck, I even sold a few books to folks well after their initial visit once their engagement ran up to over 55 percent!
The nice thing about the engagement metric is that it helps resolve the problem that Gary describes in his recent post on visitor classification. Gary, in talking about the need to capture and visualize both absolute and relative content usage on a site says this:
The problem is that heavily engaged users of your site will show up (and often drive the statistics for) virtually every area of your site. For publishing clients, a small segment of heavily engaged users inevitably show up in every single content area. And the smaller the overall usage of that area, the more the heavily engaged component influences the results.
Yep, so wouldn’t it be nice if you could not only create on-the-fly visitor segments that are inclusive of any different number of content areas and pages on your site plus easily determine how much of an influence highly engaged visitors are on your absolute content usage measurements? If you could do that, it would probably look something like this:
I know it’s hard to see, but I simply dragged a bunch of pages, groups of pages, and content groups onto the page visualization map and told Visual Site to color the nodes by visitor engagement (the height of the bars represents the relative number of sessions to each node.) I could then select-in or select-out visitors based on their relative level of engagement to identify the special kinds of customers Gary refers to.
Anyway, I’m going to have beers with the good Mr. Ivy next week and I didn’t want that whole “social media” thing hanging over my head. And while I recognize that this metric (which I still have yet to share the calculation) doesn’t capture fully the elaborate needs of the really smart folks working to pound out Social Media Measurement, I heartily agree with Clint’s friend Jeremiah Owyang when he says that “Social Media is about people. People connecting to other people to build better relationships, fostering communities and increasing collective knowledge” and “Measurement and Metrics are one way to help to tell the story of Social Media.”
Measurement and Metrics, indeed.