‘The Big Bang Theory’ of Content Marketing

Physics can teach us a thing or two about what matters in Content Marketing

big-bang-top-the-big-bang-theory-who-s-set-to-have-the-first-baby

Source: Moviepilot.com

Genesis

Two things collided to create this post.

First, I’m a huge ‘The Big Bang Theory’ fan. For anyone who doesn’t watch the show its central characters are two roommates – Sheldon Cooper and Leonard Hofstadter – both physicists at CalTech, and Penny, an aspiring actress and “temporary” waitress who moves in across the hall from them.

Penny lacks the guys intellect, often struggling to understand (or care) what they’re talking about, but she knows a lot more about life and relating to people than the two geeky scientists.

Second, I showed a very clever real life physicist, Stephen Baldwin* a graph of retweet activity over time for one of the tweets identified by our Tweets Distilled experiment. Tweets Distilled seeks to identify interesting tweets early in their lifecycle.

Tweets distilled

Stephen suggested there could be parallels between what makes content successful and the physical properties of heat capacity and phase transition.

*Stephen specialises in acoustics and sound processing and is currently looking for a new challenge.

Our Theory

Here’s a summary of what we produced:

Content heat capacity

Content Heat Capacity equations

Content phase transition 

Content Phase Transition changes

Sheldon Cooper will explain the physics behind these properties later in the post. First here’s the story of the tweet that got me and Stephen talking….

Giving it 110 per cent

On the day of the Scottish Independence Referendum, CNN’s graphic department had clearly been listening to too many footballers’ post match analysis as they put this graphic up on screen.

CNN reports 110  turnout in Scottish independence vote   Daily Mail Online

A Twitter user called Brady, who goes by the screenname of @BurningGoats picked up on the error and tweeted:

This was just after 8pm. The graph below shows the average retweets per minute in the 8 hours after the tweet was posted. Burning goats retweets You can see that in the first hour and a half there was a limited amount of activity. and by 21:31 the tweet had been retweeted 62 times. This is exceptional for Brady as none of his tweets in the subsequent 2 months have had more than 2 retweets, but it’s nothing compared to what happens in the next few minutes. Betfair Exchange   BetfairSports    Twitter At 21:31 the tweet is retweeted by @BefairSports, an account that doesn’t follow @BurningGoats. @BetFairSports has over 80,000 followers, one of whom is the ex Liverpool and Germany footballer Dietmar Hamann who has over 600,000 followers. Didi Hamann   DietmarHamann    Twitter He also retweets the story at 21:33 and immediately following this combination we see a massive spike in retweet activity. Burninggoats retweets after influencers From then on the tweet never looks back maintaining a rate of 40-80 retweets per minute for the next couple of hours.

Finally at 02.26 the story is picked up by the mainstream media and appears on the Daily Mail, whose article generates nearly 1,500 comments on Facebook. Daily Mail CNN 110 per cent The Science

So how can physics help to explain what happened? Over to the awesome Sheldon Cooper to explain.

Big-bang-theory-penny-sheldon-photo1

Source: bigbangtheory.wikia.com/

Sheldon: Two physical properties are relevant here. Heat capacity and phase transition.

Heat capacity

Heat capacity is the amount of energy that is required to increase the temperature of a material. It can be expressed as an equation, thus: Heat Capacity Equation So the higher the heat capacity, the more energy it’s going to need to get hot. Different materials have different heat capacities.

Leonard explanation for Penny: It’s why they don’t make hair straightener plates out of rubber!

ghd Phase transition

Sheldon: As the temperature of a material rises it will change state. These changes are called phase transitions and the most well-known are from solid to liquid and liquid to gas.Phase Transition There is also a fourth state that matter can take. When a gas is heated sufficiently it will ionise and form plasma (the most abundant material in the universe). Different materials go through phase changes at different temperatures.

Applying physics theory to Content Marketing

Content heat capacity

Sheldon: If we relate:

– heat capacity as a measure of the quality and likely potential interest a piece of content possesses within an online community (where high quality content is equivalent to material with a low heat capacity);

– energy as the tweets, retweets, favorites, likes, shares and other forms of engagement with the content it receives; and

– temperature as the level of interest it’s achieving within the relevant online community;

we get: Content Heat Capacity This predicts that low quality content will need high engagement to raise the level of interest being shown in it.

Leonard to Penny:  “You might read an article on toilet brushes, but only if Gerard Butler tweeted it!”

Gerard_Butler_My_Morning_Man (2) Content phase transition

Sheldon:  If we see the different states content can exist in as: Content Phase Transition changesSolid phase = low quality content that even your followers find uninteresting or decent content that receives very little engagement.

Liquid phase = great content that’s got some engagement and started reaching followers of your followers, or not so great content that’s lucky enough to get engagement from some influential sources.

Gas phase = content that’s reached the wider community, either because a) it’s awesome and quickly got the attention of people dotted throughout that community, b) it’s pretty great content that’s getting lots of engagement generally or c) decent content that’s had the full star power treatment to force it out into the wider community.

Ionised = Amazing content that’s so “hot” you’d actually talk to someone about it in the real world and/or it’s appearing in media outside of the online community concerned.

The key is that the more widespread the appeal of the content among the community the lower the temperature (interest level) it will have to reach to change its state.

So awesome content will take a lot less energy to go through these phases and start reaching the wider community .

Leonard to Penny: “It’s why the biggest secrets make the best gossip.”

Sheldon-Leonard-and-Penny-leonard-hofstadter-16257789-721-400

Source: fanpop.com

How the theory fits the CNN Story

Sheldon: The CNN graphic is great material. It’s funny, has immediate visual impact and it’s got numbers, and everyone loves numbers. It therefore has an inherently low heat capacity i.e. very high content quality rating.

The tweet by @burninggoats improves on this by bringing in the “giving it 110 per cent” phrase that is often used by sports people. This meant the tweet itself had an even lower heat capacity and so raised its inherent quality further.

Implication: The tweet only needed a relatively small amount of energy (engagement) to raise its temperature (interest level). 

At the same time the content also had the potential for widespread appeal. Whether you were interested in the Scots referendum, appreciated the sporting reference or simply wanted to have a laugh at CNN’s expense, many people were likely to find this content interesting.

Implication: The tweet only needed a relatively low increase in temperature to change state and reach the wider Twitter community.

Combine these two and you had content that only needed a relatively small amount of energy (engagement) and it was going to reach far and wide. This is what happened when it received the retweets from @BetfairSports and @DidiHamann. Liquid to Gas And the content subsequently “ionised” when it was published by the Daily Mail.

Practical application of the Theory

To be successful you need to recognise three key implications:

1. Content creation and design is crucial

If your content isn’t all the things you know it should be – well designed, eye catching, exciting, thought-provoking, surprising, timely, appropriate format etc – then its going to need a lot of energy from the community to raise the interest level.

If you don’t possess this yourself e.g. a brand with a huge organic following like Apple, or you can’t buy it (celebrity endorsement for example), then content like this is going to really struggle to reach beyond a small proportion of those who are closest to you.

2. Listen to understand what will have widespread appeal

If you want to reach the parts of a community you don’t already know then you need to understand what is likely to engage those people, as well as those close to you, and design your content accordingly.

This will mean that your content has the potential to “change state” at much lower levels of interest. Again this means the energy (engagement) requirements to achieve this are lower.

3. Influencer engagement (and potentially paid promotion) will often still be necessary for success

In almost every case the quality and appeal of the content will only get you so far. They will reduce the energy requirements, but they won’t eliminate them.

As we saw with the CNN example, the innate quality and widespread appeal of the tweet meant it turned into a liquid (reached the followers of Brady’s followers) quite quickly. Even still it took the input of energy from Betfair and Didi Hamann’s engagement to make it change state to a gas and start reaching the wider community.

This demonstrates one of the potential benefits of an influencer strategy. Though be wary. It’s important that the influencers, really do possess the potential for influence. Don’t get fooled by simple reach numbers. Make sure they are highly relevant so that the wider community they help you reach is the one you were looking to target.

Finally in a corporate situation the use of paid promotion should be considered as an alternative to provide this additional energy when it doesn’t appear organically.

Sheldon Cooper signing off. 

 

The 4 (F)laws of Social Listening

4 laws of social listening

Social listening is big business. Having access to a platform that can interrogate social media data has become a must have for many, if not most, organisations.

The problem is the vast majority of these platforms suffer from major flaws which can often lead to them producing pretty graphs and analysis that at best tell you very little and at worst are hugely misleading.

For social listening to be effective in providing genuine insight, it needs to fully address the following: refine, contextualise and weight the data, and be wary of sentiment analysis.

1. Refine

This is where it starts. Rubbish in, rubbish out. Within any social media the volume of spam (or robot created) accounts, conversation and content is vast. Any social listening exercise or platform must be effective at filtering this crud out, otherwise the resulting analysis is going to be seriously flawed.

Think of it like refining oil. You could try running your car on raw crude, but I wouldn’t recommend it.

Separating the wheat from the chaff is not a simple exercise. Just saying “let’s look at data from accounts with more than X followers” or “at least X shares” or similar sliders that many vendors provide doesn’t cut it. In fact, it can often result in missing really important conversations involving key individuals who just happen to be less active or noisy.

You need filters that are sophisticated enough to eliminate the noise, whilst retaining the signal, something Ray Dolby knew a thing or two about.

2. Contextualise

The keyword. That wonderful item, beloved by social listening platform users the world over. Selecting the right keywords has almost become an art for some people. They produce complex Boolean searches in a desire to find the conversations they hope are contextually relevant.

The thing is, the more complex your keyword search criteria, the more likely you’re missing something you needed to hear. I want to identify conversations about Apple the brand not the fruit, so I search for a string of “Apple” AND “XXXX”, or “Apple” NOT “XXXX”.

More sophisticated solutions use pattern recognition and topic clustering to identify the context and save you thinking of every AND XXXX or NOT XXXX. But what about conversations that are relevant, but in a less direct way? What about competitors like Samsung or Microsoft? When I ask for Apple data do we include these conversations or ignore them? Is it only when they mention Apple? Where do we draw the line?

The other major weakness in these approaches is that they rely on semantic content in the first place. If keywords aren’t present in the social media post then the system can’t identify them. Makes for a pretty major weakness when you consider the huge increase in visual based social media like minimally tagged Instagram posts, or tweets where it’s the picture that tells the story.

The bottom line is neither of these approaches is a human way of listening. We don’t rely on keywords to tell us if something is insightful or of interest; we have the capacity to recognise it when it is. Social listening solutions need to be designed with this in mind.

After all we are listening to people, not simply crunching data.

3. Weight

When Barack Obama gets up to the White House podium, the world listens. When @abc45xxx (not real) with 200 spam followers auto posts a link to an article, no one does.

I’m not talking about influence measurement here – though it’s a related topic – I’m merely pointing out that social media may have democratised conversation, but that doesn’t mean that every social media post should be given equal weight. It also doesn’t mean that only posts from “influencers” are important either.

Social listening solutions must take account of the relative importance of what is said, by whom and how people reacted to it and then weight their analysis accordingly.

4. Be wary of sentiment

This is simple. Automated sentiment analysis is quite simply not much better than a coin toss in many cases.

If you’re happy to make business decisions on this basis then fine, otherwise don’t believe those seductive dashboard graphs and accept that you’re going to have to take a more human approach to such analysis and that means you probably can’t do it for every last tweet, YouTube comment, blog post etc. 

Consequences

Compliance with all the first three requirements is an absolute necessity if a social listening exercise is to have any potential for producing insight.

If you don’t filter out the noise your analysis is flawed from the start.

Filter the noise, but don’t ensure you have the right context and you’ll be listening to the wrong conversations, even if they will be crystal clear.

And fail to weight what was said and you risk missing the key voices that were driving the conversation.

Social listening solutions need to start addressing these (f)laws properly and fast, before users realise how irrelevant and misleading a lot of what they are telling them actually is.

50 plus 50 equals 100 most influential UK journalists on Twitter?

The Press Gazette has announced its Top 50 most influential UK journalists on Twitter and I suspect most people won’t be surprised by many of the names. Like any such list though, there will always be people we might have expected to see who aren’t included.

With this in mind I put together this list of 50 (using Lissted), one or more of which on another day, on another basis, might well have made the cut.

Of course there will be other contenders too, so please feel free to suggest them in the comments.

I’ve created a Twitter list of these 50 plus the Press Gazette 50 here.

@andrewrawnsley

@andrewsparrow

@bbcnormanS (Norman Smith)

@BenedictBrogan

@BowenBBC (Jeremy Bowen)

@CathyNewman

@Dannythefink (Daniel Finkelstein)

@dansabbagh

@deborahjaneorr

@dpjhodges (Dan Hodges)

@edyong209

@faisalislam

@Freedland (Jonathan Freedland)

@gabyhinsliff

@gallaghereditor (Tony Gallagher)

@georgemonbiot

@greensladeR (Roy Greenslade)

@hadleyfreeman

@HilaryAlexander

@Hugorifkind

@iankatz1000

@JamesChappers

@janemerrick23

@jemimakiss

@thejeremyvine

@johannhari101

@kathviner

@lucymanning

@maitlis (Emily Maitlis)

@marinahyde

@marthakearney

@MichaelLCrick

@michaelwhite

@msmirandasawyer

@nicholaswatt

@PatrickWintour

@paullewismoney

@PennyRed (Laurie Penny)

@SamCoatesTimes

@ShippersUnbound (Tim Shipman)

@SimonNRicketts

@SophyRidgeSky

@steverichards14

@sunny_hundal

@suttonnick

@suzanne_moore

@toadmeister (Toby Young)

@tombradby

@VictoriaCoren

@zoesqwilliams

1/3 of LinkedIn’s Trending Content articles in March were from the LinkedIn Publishing Platform

LinkedIn has launched a tool, LinkedIn Trending Content, that lets you see the content that has been shared the most by different sections of its community. The site is currently showing the most shared URLs between 1/3/14 and 22/3/14.

Its an interesting tool for identifying the articles, and related topics, that have generated the most reaction in different groups.

It also provides the basis for a few other observations:

LinkedIn Publishing Platform content ranks very highly

In three groups – Students, High-Tech and Health & Pharma – the top ranked article is one that has been published on LinkedIn’s own Publishing Platform.

8 out of the 10 groups have at least one LinkedIn article in their Top 15 and in the case of Students, C-Suite and Marketing more than half of the Top 15 are from LinkedIn.

Only the Automotive and Financial Services groups have no LinkedIn articles in the Top 15. An opportunity there for someone?

In total 49 out of the 150 results are from LinkedIn (there are a couple of duplicates in there). I haven’t had the time to count the results for all the other individual sources but a quick scan suggests that none of them have more than 10.

LinkedIn Articles in Trending Content

17 shares are enough to trend in Automotive

The table below shows the number of shares on LinkedIn as a whole that the articles ranked 1st and 15th (the lowest position shown) in each section have received.

LinkedIn Trends Industry comparison

I assume the tool’s ranking is based (sensibly) on shares by members of the specific section e.g. Health & Pharma, concerned. This explains the instances shown in orange – IT Decision Makers and Financial Services – where the 15th placed article has received more shares overall than the 1st placed.

This must be because less of their shares were within the specific section being evaluated e.g. the 15th ranked article in Financial Services must have received less than 47 of its 297 shares in this specific section or it would have to outrank the 1st article.

The interesting takeaway for me is that the more specific groups – High Tech, ITDM, Health &  Pharma, Automotive, Financial Services and VC (with the exception of Marketing) – all require less than 500 shares across the entirety of the 270million+ members in order to rank in the top 15. In Health & Pharma, VC and Automotive it’s less than 150.

In a measurement context therefore a piece of content in any of these areas that is receiving tens of shares would appear to be significant.

US content dominates

All of the top ranked articles are from US sources and a quick scan of the others in each section suggests this is the case across the board. No great surprise given that US members are the biggest group at around 30% of the total.

Articles checked for share stats

Students

1st How to Answer Stupid Job Interview Questions
15th
How To Become A Jedi Knight

C Suite

1st 18 Things Highly Creative People Do Differently
15th Confidence. Conviction. Charisma: The Art of the Sale. 

Small Business Owners
1st 18 Things Highly Creative People Do Differently
15th 5 Hashtag Tracking Tools for Twitter, Facebook and Beyond 

Marketing
1st Behind the Preplanned Oscar Selfie: Samsung’s Ad Strategy
15th 5 Business Goals of Content Marketing

High-Tech
1st Big Data: The 5 Vs Everyone Must Know
15th Is Microsoft telegraphing the demise of Windows Phone?

Health & Pharma
1st Saying Goodbye to the Old World of Healthcare
15th A Third Of Nursing Home Patients Harmed By Their Treatment

IT Decision Makers
1st How to Build Trust as a New IT Executive
15th 6 IT Strategies to Stay Ahead of Data Center Trends

VC
1st New York VC Investments Top $1B In The First Quarter
15th The Top Venture Capital Investors By Exit Activity – Which Firms See the Highest Share of IPOs?

Automotive
1st First Times Drive: 2015 Audi A3 e-tron plug-in hybrid
15th Hyundai Revamps Sonata That Upgraded Carmaker’s Image 

Financial Services
1st Employee’s Whole Life at-a-glance
15th Should I Rent My House If I Can’t Sell It?

The Value of Social Listening – Dixons & Carphone Warehouse Merger Talks

Social listening potentially made investors £75,000 yesterday morning when a blog post from an ex Daily Telegraph journalist apparently lead to an increase of c. £75 million in Dixon Retail’s market capitalisation.

At 9.52am yesterday, Dixons and Carphone Warehouse confirmed they are in the “very preliminary” stages of merger talks.

This confirmation wasn’t planned however. An hour earlier their existence was the subject of a blog post by former Daily Telegraph M&A and Markets Editor, Ben Harrington. Ben tweeted a link to the post shortly after publishing:

Our Lissted application spotted Ben’s tweet, plus retweets from fellow journalist Neil Craven and retail insight provider Steve Dresser (a member of our Top UK Retail Influencers)

Lissted Dixons Carphone initial tweets

 

Some people were listening

Before Ben’s blog post, between 8:00-8.52am, there were around 500,000 Dixons shares traded and the price was pretty static at around 46.7-46.9p.

Dixons pre blog post

 

Share price leaps, apparently in response to rumour

In the 20 minutes after the blog post was published around 10x this number of shares (c. 5 million) were traded (mostly purchases) and around 90% of these were traded between 9.05-9.11. This resulted in the price rising to nearly 49p per share, an increase in the market capitalisation of the company of around £75m.

Impact of Dixons trading post blog post

 

The activity died down at this point and the price settled at its new level between 48.6-49p.

Activity and share price jump again when rumour confirmed

Then at 9.52, when the official confirmation came through, activity jumped again with around 20 million shares traded in the first 12 minutes after the announcement and the price reaching 50p.

It was only at this point that the mainstream media started to report on the talks.

Dixons reaction to announcement

 

The value of being early

Those who were aware of the published rumour, and prepared to purchase shares in the initial 20 minutes, would have acquired their holdings at lower pricing.

Based on estimates during this window for share purchases of 4-5 million, and average price paid of c. 47.5p, gives a potential gain of around £60,000-£75,000 for these “quick off the mark” investors.

Not the only value around

There were many other valuable reasons to be aware of this story quickly. Reputation management, analyst relations and HR being three obvious ones.

However, here’s an example where the £ note value of social listening can truly be demonstrated.

N.B I’ve focussed on Dixons because the trading activity around Carphone Warehouse during the period before the official confirmation was considerably less, which raises some interesting questions of its own.