Using analytics – Page 4

Using stats from Facebook’s self-serve advertising program

Even Facebook doesn't have demographics and user info. on all of its users, as this ReadWriteWeb story illustrates. In particular, look at the comments which point out all of the problems with using an incomplete set of data.

Facebook doesn't require users to provide age, geographic location and other basic demographic data when they sign up. Thus, the user data you get when you go through the self-serve advertising program doesn't include all of those people who didn't submit data. And you don't know how many, or how these missing people differ.

Also, Facebook doesn't require people to update their data, which causes other problems. One of the ReadWriteWeb story commenters noted that students who graduate from a high school or college may or may not be counted as students.

Facebook data should not be used for planning your social media strategies and services. You're better off guessing than using this data. Bad data may

Continue reading “Using stats from Facebook’s self-serve advertising program”

The money’s with the audiences, not the content

For those of you who were wondering what the difference is between audience-based sites and content-based sites, read this (rather bracing) iMedia Connection blog entry by John Nardone.

Online advertisers buy audiences, wherever they are. "Let’s say you’re Coach. How many contextually relevant sites can you be
on, once you’ve hit the major fashion sites? What’s far more important
than getting your ad on, say Vogue.com, is getting it in front of
fashion-minded women who have the means to buy expensive leather goods
and accessories….

….you can buy audience without being tethered to editorial….The bottom line is that editorial matters only if you're reaching your target audience at a price that makes sense."

In online now, there are three things that matter: audience, audience, audience.

Age – or geography – is not enough

After burning through $9 million, TeeBeeDee.com, a social network for baby boomers, is closing because “baby boomers apparently did not want to be categorized away by their age,” Joseph Tartakoff of PaidContent.org reports.

Uh, duh? Audiences have never been able to be truly understood by category, and often resent it. From @deanpeters on Twitter: “I logged-in once [to TeeBeeDee.com], it gave me the creeps.”

Caution: Time on site

I'm getting Twit-fatigued from all of the phenom Tw-stats, but I can't resist pointing out how Nielsen's May 2009 report on Twitter usage illustrates the problems with using average time on site as a rough gauge of engagement.

Nielsen reported that the average time per person on Twitter in May 2009 was a little over 17 minutes, an increase from about six minutes in May 2008.

This is an average. This means you don't know how many people spent 20 hours a day on Twitter, and how many spent zero.

We do know, from a recent Harvard Business Review report, that:

the top 10 percent of "prolific" Twitter users produce over 90 percent of all Tweets, and that
the median number of lifetime Tweets per user is only one!

My advice: Don't use time on site as an indicator of success unless you're willing to really dig into your data and segment out heavy users vs. light users.

Here are two more observations – bashes, really – on using time-on-site.

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Rhyme of the ancient web analytics analyst

Here's a riff on a famous poem from Rishad Tobaccowalla, CEO of marketing agency Denuo, speaking at OMMA Metrics and Measurement in New York today:

Data, data everywhere
I think I could sink
Data, data everywhere
Will someone please help me think.

James Robinson, director of web analytics at the New York Times, offered complementary thoughts about how it's a "fallacy" that more complex data is more valuable, and that "it's not about the data, it's about the insights….it's not about the page views or click-throughs – it's about making New York Times customers happy."

For those of you who want to play web analytics games, make a bingo card out of Jodi McDermott's list of web analytics buzz words. Jodi, also known as Widget Girl, chaired the day-long seminar and is director of data strategy at Clearspring Technologies.

Who said web analytics wasn't fun?

Comparing unique visitors in political blog sites

David Kaplan of PaidContent.org compared the number of unique visitors in April in political blog sites such as Huffington Post and The Drudge Report and found that “left-leaning” sites had 6.4 million; “right-leaning,” 4.8 million; and “neutral/non-partisan,” 1.3 million.

This is a fun comparison, but here are a few web-analytics-nerd thoughts for newsrooms who are competing for these audiences.

The left didn’t necessarily “win.” To really gauge the relative strength or engagement of the audiences, you should look at ratios like number of visits per UV, number of page views per visit, and bounce rate.

The left’s 6.4 million UVs is dominated by HuffPo’s 5.6 million. The right’s 4.8 million was more distributed among The Drudge Report, Free Republic, World Net Daily and others. I’d like to know how many UVs the sites shared – and how many went to only left sites, only right sites, and only neutral or nonpartisan sites.
Also, how many went to both left and right, or to all three? How many who categorized themselves as left-leaning went to right sites? Right-leaning to left, and so on? (Note: A lot of this data will send you into analysis paralysis, but there could be some actionable info here.)
In the minds of your audiences, is your site categorized as conservative/right, liberal/left or neutral/nonpartisan? Ideally, you should measure the differences in perception between news stories and editorials.
Are your pages coded and/or is your site set up to track all “political” content, whether it’s on the home page or the officially named “Politics” section?