re-thinking reach

21 Dec, 2009 • posts i've written • 1 comment

If we stopped measuring reach solely by impressions and instead, factored in how content posted to a site is spread by its readers – our notions of the top sites on the web would dramatically change.

Fortunately, this is something we can actually do. Simple tools like Google Blog Search, Backtweets, and more sophisticated services like Scout Labs and Radian 6 make tracking how content spreads nearly automatic.

We can start by simply multiplying the usual reach estimate of a site by the % of the estimated readership that has spread content in the past (an estimation drawn from the number of links back, tweets, etc). We could go further by tacking on the estimated reach of the sites where the content was spread to – but we’d find that many of these sites are too small to have an accurate measure of readership.

What would we find? Well, 4chan would probably be the number one site or at least somewhere in the top 5. Mainstream media sites would drop way down in the rankings – these sites have simply not reinforced the sharing behavior with their users. Blame the years they spent fighting any kind of user interaction.

I hope we start to see lists like this in 2010. If anyone works at any of these measuring services, send them my way, I want to make this happen (or hit my head against the wall until it can’t).

how to define a marketing strategy

07 Dec, 2009 • posts i've written • 3 comments

Business Objectives – I want to be awesome, too, but… remember the difference between goals and objectives? Objectives should be measurable and objectives should be stated BEFORE anything is enacted. Your objectives define the desired future for your team, and create a vision everyone can work towards.

Business Constraints – I’d like to etch my likeness in the moon with a laser, too, but… Constraints can and should include budgets, timing, product availability, team availability, responsiveness, legal concerns, etc. Again, this should be understood, to your best ability, BEFORE anything is enacted.

Competitive Insights – I like to pretend I’m the only person in the world sometimes, too, but… The activities of your competitors matter; they’re seeking the same consideration, attention, and resources from your customers as you are. Look beyond numbers. Try to define their current strategy, its effectiveness, and how they’re accomplishing their objectives.

Consumer Insights – Sometimes I imagine myself as Kevin Costner in Field of Dreams, too, but… Just because you build it, create it, or advertise it, doesn’t mean they’ll come. Understanding where people are, how they spend their time, why they share information, and what motivates all of these behaviors is essential; and the more that this work is outsourced the more disservice is done to the organization.

I’m trying to convey something simple with this simple diagram – these are the ingredients you need before you can begin firing off missives to your internal and external teams. I wish this were more apparent to more companies.

There’s quite a bit that this diagram doesn’t communicate.

First, everything is interdependent. Objectives cannot be defined without an understanding of constraints, consumers, and your competitors, and your ability to dive deeply into insights is a factor of your time, money, and resources.

Second, this becomes a fluid process when you apply and enforce measurement. Strategies should only evolve once you have an understanding of their performance towards stated objectives.

Third and last of all, this kind of paradigm can produce a suite of strategies that can be tested, measured, refined, and eliminated as necessary.

the big money facebook 50

01 Dec, 2009 • posts i've written • 3 comments

Slate column, The Big Money, decided to rank the top 50 companies using Facebook.

How did we compile this list? First, we defined a universe: A company did not qualify for this list unless its Facebook page(s) had a minimum of 200,000 fans. Within that universe, we rated the companies using a variety of criteria: how often they update their Facebook offerings; the level of engagement demonstrated by their fans; how fast a company’s site has grown; and how creatively the companies are using their Facebook presence, as evaluated by a distinguished panel of outside judges.

I take issue with the list and the criteria used to create the list.

200,000 fans? I’ve been scribbling this phrase among the pages of my notebooks lately, ‘you suffer what you measure.’ Why is that we’ve decided the value of a Facebook fan page starts and ends with a ridiculous number of people that pressed ‘fan’ and then we ignore the question of whether they’re ignoring us? Why is it that in a digital environment, where almost everything is measurable, we fix ourselves to a single variable… a dangerously myopic vision of our online health? (our hearts cry out for an algorithm!)

How fast a company’s site has grown? Basically, this measures how many fans they’ve purchased through media buys.

I suppose the author of the article answered my next question in the first sentence of the post, why, why would they create such a silly list predicated on making the big brands (the ones that have been spending millions of dollars year on year for decades) look big on the internet… “Lists seem to have an irresistible lure for business publications.” Sure, can’t argue there.

Here are some more worthwhile FB fan page lists I’d like to see…

• FB fan pages with the highest level of activity (including traffic out of FB) per capita (so having a giant user base that mostly ignores you would be a bad thing)
• Brands with the highest level of earned & active fans
• Giant brands that use social platforms to leverage the tens, if not hundreds, of thousands of employees connected digitally
• The most active and interesting fan-created FB fan pages (I know, I know, Coke can take some credit here on having the biggest fan-created page, but baby, it ain’t just about size…)

social media practitioner survey

12 Aug, 2009 • posts i've written • 3 comments

We at Undercurrent have put together a survey for social media practitioners I’d like to share. (and hope you’ll share, too)

http://bit.ly/socialmediasurvey
* please use the bit.ly link when sharing
** and how about the hashtag #smsurvey when tweeting

The objective of this survey is to gain a greater understanding of how social media is put in practice by agencies and clients, including: how objectives are defined, how results are measured, who is doing the work, the level of compensation, and what resources are most popular among practitioners.

Who should take the survey? Anyone that handles social media strategy, manages a social media team (internal or external), or conducts social media outreach on behalf of or within a brand.

It’s important to get a wide variety of data, so please share and spread this survey. We’ll keep the survey open during the month of August.

The survey should only take about 10 minutes of your time.

What are we doing with this data? We plan on releasing a free report (probably slideshare) on our findings, along with a free download of the full dataset. We’re hoping that what we collect will be beneficial for the entire industry. When you’re done with the survey, you’ll see a link to follow to request the report and dataset.

You may remember an earlier post of mine where I solicited feedback for the survey. Thanks to everyone for chiming in. Also a big thanks to Heather LeFevre, author of the Planner Survey, of which I drew great inspiration.

I can’t stress enough how much I need your help in spreading the survey to social media groups you may be a member of, to your industry friends, to your clients (this could lead to some very compelling insights), and to your co-workers.

If you have any issues at all with the survey, I’m your man, so please leave a comment below.

Also, after taking the survey, I invite you to leave a comment back here to let me know any data in particular you’re interested in seeing charted. (like average salary, or seniority)

gini coefficient for online participation

22 Jul, 2009 • posts i've written • 4 comments

I actually wrote this article in August 2007 for my last blog, and I thought about it again today while taking a spin through a client’s new online community. So many sites are now driven by point systems (either visible or hidden from the user) and yet I have not encountered a metric like the one I proposed some time ago.

Here it is, the original post, reproduced:

Reaching into the worlds of economics and statistics, I’d like to share a way to measure the health of online communities.

This all started sometime around 2001 — I originally heard of the Gini coefficient freshman year in college during one of those massive lecture courses of Economics 101. From Wikipedia:

The Gini coefficient is a measure of inequality of income distribution or inequality of wealth distribution. It is defined as a ratio with values between 0 and 1: 0 corresponds to perfect equality (e.g. everyone has the same income) and 1 corresponds to perfect inequality (e.g. one person has all the income, while everyone else has zero income).

It bounced back in my brain one day a while back as I overheard someone lamenting the 90-9-1 rule of online participation: that 90% of your users will be “lurkers,” those who read but don’t contribute, 9% will contribute sporadically or only occasionally, and 1% of your entire user base will make up the bulk of the total participation in your community.

Some people like to use the 90-9-1 rule to boo-hoo any attempt at building an online community, some like to do a little math and say “hey, 1% of my total user base is still a big number if they really do become outspoken evangelists” — but everyone is always looking for a way to break the rule and encourage widespread participation.

But how do we create a metric that allows us to track the ROI of our efforts to increase participation? We can build our own Gini-like metric ….

WARNING: this is a long one but if you stick with me, I bet you’re going to start thinking about measuring online communities in a different way.

In most communities, I encourage point systems driven by participation — leave a comment, get a point, write a blog, get a point — sometimes certain activities are worth more points(be careful when doing this), and always, the community itself has an effect on the total score: for instance, write a defamatory comment, get negative points from other users and your total score drops. Another choice we often have to make is to decide whether or not to make the score visible to the community — it almost always encourages competition between users, which in some communities is perfect and in others, can lead to negative behaviors. Digg, for instance, used a visible participation score and it led to the top users wielding too much influence over the entire community — which fostered a drop in the quality of the content.

Regardless of how visible we make the score, we, as the community organizers, can use it in all manner of ways. In this example, we can use the score to compare the participation of users across the entire community to determine the distribution of participation and build a dynamic metric we can track over time — just like economists use the Gini coefficient to measure income distribution.

In statistics, what we’re looking for is called statistical dispersion — how far data elements fall from each other or a mean value. In our case, a perfectly distributed community would all have the same participation points, or each member would have the same number of points as the total community points divided by the number of members.

The perfectly distributed community would look like:

User1: 500 Points, User2: 500 points, User3: 500 points and so on… Everyone is participating equally.

But we know that’s not how it looks in real communities, we’re much more likely to see:

User1: 0 points, User2: 0 points, User 3: 5 points, User 4: 500 points… Participation is very unequally dispersed.

And we also know that as participation grows increasingly less equal, we see new entrants into the community drop-off more quickly and even older members fade away — as good community managers, we look out for this type of activity, but it would be extremely beneficial to have a dashboard of quantitative data to back up our qualitative assumptions.

To solve this, in short terms, I start by running a calculation on each user to find the average deviation, also known as the absolute deviation, from the mean (or ideal mean) of the community. Once I know this, I take the coefficient of the variance, which is the average deviation divided by the mean, times 100% which gives us the deviation as a percentage of the mean. Understand? Good, cause I just confused myself.

Ok, I’ll show my work!

Let’s start with a community:

community 1 points
User 1 50
User 2 4
User 3 6
User 4 18

Total Points: 78 Mean (or perfect score): 19.5 points

The average deviation of the group is 15.25. On average, each score is 15.25 units away from the mean.

Taking the coefficient of variance, 15.25/19.5 x 100% = 78.21% — which means, the average deviation is 78.21% of the mean — or, the participation in this community is largely unequal.

Unequal compared to what? I’m glad you asked!

Let’s look at another community:

community 2 points
User 1 8
User 2 7
User 3 9
User 4 10

Total Points: 34 Mean (or perfect score): 8.5 points

The average deviation of the group is 1. On average, each score is 1 point away from the mean.

Taking the coefficient of variance, 1/8.5 x 100% = 11.76% — which means, the average deviation is 11.76% of the mean — or the participation in this community is more equally distributed than community 1.

How can we use this?

Each period, we can track the change in our coefficient to see if the participation in the community has grown more or less equally distributed, and on what scale the change has occurred. We shouldn’t use this metric by itself, of course, it’s also necessary to see the overall growth of participation — by total number of points — which we can also segment by our user types or buying segments that we’ve already constructed beforehand.

Imagine now as you deploy an online community, you can track the distribution of participation from the very start — and as you see more users register on the site and as you attempt to push more of them to contribute more often — you now have a metric ready at your side to measure the effectiveness of each new campaign.

So, back here in 2009, you might be running a site where you track participation via a point system. How can I work with you to institute this metric? I’m on the look out for a group of sites I can track in this way in order to study the data (anonymously of course). Please help me out and spread this post to any site owners you know.

measuring the pace of spread

03 May, 2009 • posts i've written • No comments

Faris has written a great observation piece on the pace of cultural latency.

Diminished cultural latency means that the propagation of information is so fast that the spread itself becomes the defining aspect of the system: the rate-of-spread becomes as important as the information itself.

That rate of spread has consumed me for the better part of the last two years. I’m a measurement addict. I crave knowing the deep, dark secrets of the data universe. And what’s more, I crave every bit of competition, and knowing where you and your opponent stand (not too even mention how well I alone am doing). We often work with clients that release content into the web, and I have been tense with not knowing how well that information has spread.

So many factors go into releasing that content: what communities we choose to tap, how well they’re connected, how much social currency is baked into the content for that community, the novelty factor, which platforms we choose, what account we release from, how we title it, what tags we use, what time of day, what day of the week, what other information is spreading within the network, etc. etc. Ultimately though, for this mountain of criteria, our feedback mechanism is lousy. It’s like swimming in the Olympics and being given a time, without rank, without seeing the other swimmers, without the start gun, and without any knowledge of how to swim.

I’ve been obsessing about this, obviously. What I’ve been looking for is a way of understanding the ‘speed of light’ for YouTube, specifically. (admittedly, not the greatest metaphor) What’s the asymptotic curve of how quickly content is spreading in that platform alone, say for the last 2 years? What are we approaching in terms of time between views, or looking outside of that platform alone, time between sharing? Perhaps every bad decision we make in creating, courting, and releasing that information can be likened to friction, and we can begin to measure and better understand that effect in what we do.

If I could choose at random, 150 new videos per day, to track their pace from upload to six months in, what could we learn about the speed of information spread? This question is certainly more vexing when you open it up to events in the world (how quickly swine flu information spread online for example), as well. But YouTube provides a perfect petri dish for discovery (and YouTube study and findings could prove somewhat lucrative as well).

So, is anyone up for the challenge? And can I help?