WordPress’ kind badge on my seven years in blogging!
This blog replaced the one I left behind at IBM Canada back in 2006. IBM had a magnificent internal blogging tool – and about 1% of IBM employees were blogging. When I left IBM in 2006, I feel the pain of leaving a blog behind.
I always feel like my blog is like a neglected child – in that I think of so many posts but life prevent too many words from reaching my blog.. I guess that is what twitter is for..
Anyone can upload a video. But few do it well it seems. It is a pet peeve of mine to see a beautiful video just dumped on YouTube..
Beyond creating a video for online versus other media (which is pretty big) – one has to remember that YouTube is a search engine. The second largest. Entertainment, How Tos, anything with Cats will do well – but attention is needed on five core things..
Finally – don’t wait for views to come. Seed your video – if it is original content worth for eyeballs – submit it to Buzzfeed, to stumble upon, to many of the dozens of sites that aggregate content.
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.
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.
When I was a junior consultant at IBM – working with an ex-Kraft marketing VP and ex-Campbell’s brand director – I learned the ‘Bullshit, Prove It, So What’ design for strategy presentations. I’ve never forgotten it. BULLSHIT – is the hypothesis line. Prove it – is the chart, research, etc that proves or supports the bullshit. Then ‘so what’ is the implications for the brand. Sounds casual but it is actually a fine model for strategy presentations.
Today, I work with many individuals who help make sense of big data for agency clients.
Social listening – which is truly about finding patterns among copious amounts of data – is something that I rely on as one key input to digital strategy. In doing so, I find myself training many individuals – not how to gather social listening, for we have tools that do that, but to become suspicious of what is being offered and then package insights.
“Big data is more than simply a matter of size; it is an opportunity to find insights in new and emerging types of data and content, to make your business more agile, and to answer questions that were previously considered beyond your reach.” – IBM website.
Caption: An interesting look at the usage of ‘big data’ in google searches. We can see it emerging in the last two years. Image taken from Stephane Hamel’s blog post explaining big data.
First, I truly encourage everyone to understand how data is collected. Its a little bit like understanding the Google algorithyms.
For instance, let’s consider the key sources of mentions in the forums category for social listening platforms. If a niche Canadian forum does not pass country information in its API to the social listening platform, is it still considered a Canadian forum? The answer is no. It is considered to be a US forum. In which case, you can have gross misrepresentation when doing some forum analysis. This is the case with the automotive sector – which is host to many niche forums down to the nameplate or model of a car.
The same goes for automated sentiment which is, for many unknown reasons, accepted and presented as defacto accurate by many.
But beyond questioning where the data comes from or how it is collected, I insist that folks demonstrate more than just ‘fact gathering’ (however qualitative this ‘fact gathering’ actually is).
Many people who do social listening just regurgitate what a tool presents to you. So much so that reports become just a presentation of the what is seen in the social web. What I am demanding is that the research first consider the issues and form a hypothesis. What are you trying to demonstrate? This is fundamental to “issues based consulting” – something that I attended in IBM University in NYC.
After hypotheses are formulated, we collect data that may prove *or* disprove the hypothesis. For instance – perhaps you consider Canadians to be well informed about a major retailing event called Black Friday. But in gathering SEO activity and social listening – you can see that Canadians are not knowledgeable about a retailing holiday that is based on an American holiday.
With issues, hypothesis and ‘facts’ (or I prefer to call them findings) – we can move to the ‘so what’ stage. It sounds easier than it is – while doing social listening, you might go back & forth testing hypotheses three to four to five times.
The holy grail then is coming up with the brand implications from the data found. That is the ‘so what’ fun part. For instance, if Canadians do not understand black friday – when do they start looking for their answers compared to when retailers start offering answered. There is a gap.
It sounds so incredibly simple.. and yet, few use it.
