Tag Archives: metrics

The curve of your #FACEbook; 4 common #facebook #insight curves demystified

By now, I’ve looked at many, many Facebook insights curves working with various brand pages and I’ve come to notice a pattern in what I see.  Here is an explanation of four common Facebook insight curves or charts.  Understanding these patterns will help with understanding the success of Facebook pages.  This is likely of most interest to community managers or analytic geeks.

First a quick recap of the main units of measure on these charts.

  • People Talking About This or “TAT” includes all engagement metrics that Facebook allows – liking a post, sharing it, commenting, presumably any liking per comment and *liking a page*.  I don’t think it is the best measurement of engagement but it is what Facebook allows us to see.
  • New likes per week – indicates any new liking activity for your page.   Why Facebook adds “per week” on this unit and not the other, I don’t know.

Consider that these units of measure are presented as though they are daily activity – but they are NOT.  Each point on the graph represents a rolling 7 day week of data.  So that means the Sept 20th data is actually 7 days of activity ending on Sept. 20th (so Sept. 13 – Sept 20th).  Then Sept 21th is represented by data from Sept 14 – 21.  This is frustrating because true daily activity is muted somewhat.  I’m not sure why Facebook does this nor can I find articles on why using 7 day rolling data would be advantageous over using daily data.  If you have an opinion – please enlighten me!

Also consider that the TAT number contains any liking of a page – which is what the ‘new likes per week’ is all about.  So we are comparing two curves, one containing the other curve within it so we have to deduce that the visual gap between the two curves represents the “success” of the content.

Of course, a liked post can be generated by cheap, low involvement engagement e.g. what is your favorite colour vs. answering a consumer inquiry.  I see tons of ‘cheating’ questions – to the tune of “like this if you put socks on in the morning” – and community managers report that as successful content because facebook monkeys click “like” on the post but it does nothing for building deeper connections with a brand page.  [rant]   So despite charts, there will always be a need for further analysis into the context of the engagement.

Onto the curves.

The Newbie Chart

This is the chart to an unidentified, brand new facebook page.  Our natural cues to its newbie status would be the start of content & number of facebook likes on the actual page (not depicted here).   In looking at this Facebook insights curve,  you see new like and ‘talking about this’ [TAT]  following together.  This is because for every new like, Facebook also records it within their  “TAT”number.  So the TAT number is driven almost entirely by new likes.    Then the community manager took a break – and with a young page, every thing – liking the page & engagement crashes.  But then content reappears which looks like it appeals to the existing fan base.   We see the curves diverge.

The Contest Driven Chart

Contests are the easiest way to ‘cheat’ at Facebook fan (like) growth.  So many many do it and wonder why their community isn’t reflecting the people brands care about.  At the same time, it is a tactic – a reasonable fast one to gain critical mass.  Critical mass allows brands to get into a decent social graph – reaching friends of friends – otherwise the brand is in a room talking to itself.   With a contest under belt,  its a crowded party but possibly in the wrong bar, with the wrong crowd.

So focus on the right half of this chart – we see two bumps with the gray ‘new likes’ line following the TAT line.  This is reflective of new people entering a contest.  It could also be a curve that reflects good content that encourages people to also like a page, but since we know the contest is going on, then know the curve.   At the same time  – there is a bit distance between the curves which indicates that the content either resonated with contest goers (it did, its the Swiffer ‘my man cleans’ t-shirt, Oct. 15) or that the contest asked visitors to do some kind of monkey task (it did, Swiffer’s advertising ‘show us the love or at least like us’).

The ad supported chart

Up, down, up, down… likely in concert with two waves of facebook advertising spend.   To me, the first bump suggests the first ad worked harder or was supported by some kind of like incentive.  The curve drops when the stimulus is removed, and re-appears with ad support (confirmed as I’ve seen the ads).    I don’t see this as a healthy facebook curve at all.  It is artificial – because it is not sustainable without advertising.

The ‘my content went viral’ chart

This is Red Bull’s facebook page – after its sponsorship of Felix Baumgartner’s jump from the stratosphere.   A brilliant capture of content from a brand, in my opinion.  Here we see the facebook curve coming off an ad spend, then the big bump on the right is the release of photos related to Felix.  The content is heavily shared – and although this is a rolling seven day picture, it seems to have been shared over time – given the evidence of new little spikes in the curve top.   As for new likes per week – there is traction.

What is interesting is that this chart represents a community of 32 million.  So the “new likes” are not what is driving the content here – it is the content being shared among the existing fan base – or so I expect.

Another ‘my content went viral’ chart

UK Bodyform’s very interesting video response to a disillusioned man was well received.  For a little community – the content got a lot of eyeballs.  It did not translate well into likes – although there are a few new likes at the right.  It may be that this community did not have a lot of content prior to the video – so the community saw this as a one time contribution, albeit awesome, but not enough to like the page.  That said – this is a page for feminine protection which wouldn’t get much public liking anyways.

I hope this helps with your perspectives on facebook insight charts and your measurement of facebook pages.  Drop me a comment if there is a curve pattern that I could include.

Advertisements

Klout – emergence of social influence metrics & interview with founder Joe Fernandez

Klout rates the social influence of individuals in social media.   oooooooo.   This is an inflection point in social media (metrics anyways).  The founders of Klout are working on the first application that measures social influence and so introduces an algorithm for determining which social media bloggers/tweeters/fans, etc are creating the strongest influence.  For brands, politicians, media – whatever your poison – can now understand those who influence the crowd.

Importantly, Klout has only integrated twitter and facebook so other social medias, like Linkedin or blogs, are yet to come.   AND… Klout has an API – which means that developers can integrate the Klout score of individuals into their services.   Hootsuite, the popular twitter client for instance, has klout scores integrated into the profiles of individuals.  There is rumour that Google will also be factoring in Klout in its own algorithms – which raises the voice of the influential.

Understanding klout is worthwhile and although I would hesitate to run a Klout perk program without ample investigation or verifying of influencers – unlike how some in the industry are starting to (and hats off pending your investment in this new area) – I do watch the growth – 1500 companies are using its API, people are adding klout profiles to their linkedin profiles (I did!) and young americans are putting scores on resumes.

I emailed Klout founder Joe Fernandez some questions following a tweet chat that he attended:

LDS:  How will Klout account for national or regional differences in social media usage? For example, Canadians just do not use twitter lists as much as Americans do – and while we might get there, it is hard to rate a Canadian using American metrics to determine influence.  Of course, Canadians are heavily into Facebook more than most nations – and so this too requires balancing.  I think, over time, Justin Bieber would become less of a poster child – assuming he does not leverage across multiple social networks.

JF:  This is a big challenge.  It’s gets even harder when you look at how people using twitter in indonesia or brazil.  We use a model based approach so that we can have lots of features and have the weights between them be dynamic based on different factors of the account.  This is something we’ll need to get really good at. 

LDS: I also notice a lot of follower gamers. @SiDawson from Australia has a neat little program called Twitcleaner which highlights the potential garbage in someone’s following.  I could see Klout offer something like this – to not only measure klout but manage klout.

JF:  Yeah, there is tons of follower gamers.  We actually don’t look at followers in the calculation at all.

LDS:  Last question – how do you isolate influencers for a particular brand?  I can look up individuals but if I wanted to know the most influencial on coffee, for instance – would I do that thru social media monitoring then take those with authority and cross reference into Klout?  It seems arduous.

JF:  We do this behind the scenes and use it to target our campaigns.  There really isn’t a great way to do it on the site right now.  We have a new version of the site coming though and this will be a big part of it.

=============

I’ve been tweeting a *lot* about Klout – and at the request of a number of folks – I will summarize some of excellent discussions, blogs and information that I’ve found on Klout:

  1. Oliver Blanchard’s piece on Understanding Klout’s measurement spectrum
  2. Jason Baker’s piece on Klout..become standard online influence measurement tool  , interview with Klout marketing director.

My past tweets:

Measuring for social media

Saw a very interesting discussion on linkedin today.     A project manager asks:  “What are the top 10 metrics for a social media site? I am the project mgr for a company’s new social media platform.”

Great question!  And indicative that social media metrics still need some defining and that common web based metrics (which are still indicators of site success) do not report on the social aspect of a site.  

Within the linkedin responses, Clay Gordon nicely describes the age old need to first define the objectives of the social site – though the goals he described were not ‘social’ in nature, but certainly any site has some kind of end business goal related to revenue and loyalty.

Social goals in my mind can vary – such as improved customer communication, improved customer experience/satisfaction, increased engagement, increased community participation, buzz.    And this can be captured in many ways pending on what type of social media is being used – be that blogs, videocasting, podcasting, uploads, communities, etc.

One metric that would be really neat to track would be the reduced costs by reducing or eliminating high volume, low value customer support [ or conversely – increased customer communication – which could be measured in satisfaction around key moments of truth (mot)].   [a mot is a customer interaction that is very important to a customer] 

This would be very cool to do in the housing /home builder business since there is a very long time between when a house or condo is purchased and when it is delivered.   Most home builders provide ‘legal’ customer communication between purchase, design selection and final occupation.  But these customers are *so* excited to have a new purchase – the opportunity to connect and create an emotional, word of mouth, loyalty is HUGE.    Using social media in the housing section is a great opportunity to continue the emotional bond and excitment from first purchase past the buyers’ remorse stage and into occupation.

So.. here was my response in linkedin:  

“When you say a social media site – I’m assuming there is some kind of community component. Is it internal or external? Blogs? Wikis? Is it a portal with video or podcast downloads? Any uploading? All this would affect which metrics are most important.

Standard web stats – still good for ‘social’ sites:
– unique visitors and watch growth rate over time
– type of visitor (usually limited to new vs. returning)
– source of traffic (direct, referral, paid)
– no. of pages per visit (how much is being consumed)
– time on site (mildly indicative of interaction on site)
– hot pages (top content)
– conversion goals (which can be shopping cart or registration, or something else).
– pathing.

*Social side* Measuring for interaction.
Here, I would be looking for metrics to cover the interaction.
We’ve seen a lot about twitter and its active vs. inactive audiences. [40% who sign on to twitter actually continue]  [okay… add some % for tweetdeck, etc]
I’d want to watch how active the audience is. You define what active is – e.g. return following month after initial month of participation or ‘active in last six months’ vs. total membership. Naturally, the growth in active audience will be important in the success of your platform.
– how much is downloaded and from where if its an internal international social site.
– how much is being uploaded?
– how much is being shared? (noit sure how to track that if not covered in your analytics package).
– for an internal blogs – I’d track bloggers vs. total employee audience e.g. at IBM in 2006, 1% of the company was active bloggers – but that was 3000 people.

Importantly, I’d be interesting in how the business is supporting the success of the platform. In other words, if its an internal tool, how will the business be supporting the adoption and growth of the social platform? Will there be any personal development goals for employees related to the social site? Any mandatory onboarding lessons, etc.

From an international release standpoint (e.g. international social platforms), successful adoption is a bit tricker. The operating systems are different, connection speeds challenged, etc. So your roll-out has to be well planned and social tools robust for multi-language support.

Anyhow – good luck Martha. Sounds very exciting.
Laurie.
laurie@socialwisdom.ca