Paper.li – content aggregation for the easily frightened

Content. We’re not so much waving in it, as drowning. IDC says that in 2011 we created 1.8 zettabytes (or 1.8 trillion GBs) of information. In 2012 it reached 2.8 zettabytes and IDC now forecasts that we will generate 40 zettabytes (ZB) by 2020.

Of course, that’s not all human-readable data but I’ve been looking around for those kind of figures and it seems we’ve given up on calculating the size of the blogosphere, Twitterverse or any other social media-verse-osphere in any meaningful way.

So let’s forget about quantifying data. How do you feeeeeeeeel about it?

Personally, I feel overwhelmed a lot of the time. Google Reader was great for grabbing a ton of feeds and filtering the wheat from the chaff. It closed. Yahoo Pipes does something similar but has a steep learning curve and is a bit flaky.

TweetDeck was the answer, I thought, with its persistent filters. And as I wrote recently, Feed.ly is starting to pique my interest in RSS again because it’s a better way of actually finding out what people are writing about, properly, rather than just sharing.

But it’s still all a bit, well, panic-inducing. I dip into TweetDeck and have a nibble but hop away quite quickly again like a tiny frightened rabbit. Feed.ly, while more relaxing, can also scare the faint of heart, especially with its title-only layout. There are magazine-type apps such as Flipboard, which recently expanded into the web(osphere) and Google Newsstand. This seemed a way forward, by presenting items in a neat, concise layout but try as I might, I never really managed to get them quite how I wanted them.

But Paper.li just works for me.

At its simplest and most effective, you just plug your Twitter feed into it, which creates a publication based on the most shared content, that was shared by the most influential people. So it’s almost a Twitter ‘expander’, taking the most relevant tweets and expanding them back into full articles. You can go much deeper into different sources of content, filters, customisation and so on, but at the basic level it works marvellously well.

I’ve been using it for quite some time, ever since Neville Hobson’s version cited me as contributing to his daily publication. I used it to help promote Byyd (recently reactivated I see) and am currently helping LoopMe with it too. Oh, and I’m also using it myself, obviously.

However, forget about sharing for a second. My publication is actually really useful to me. This is because it represents something of an amazing intersection between the people I want to follow, and the content I want to read.

What I really like about this approach is that I get an email in my inbox each morning telling me that the new edition’s ready. I go and take a look, and there it is: my magazine, with the most interesting articles that I really need to read. Not columns of content or masses of titles. Just the top, say, four or five articles distilled for my pleasure.

So forget about building feeds or creating lists, or scanning vast swathes of information rolling in front of your eyes like so many fruit machines. Just start up a Paper.li publication, plug your Twitter timeline into it, tweak it a little with filters, and away you go. If it’s not quite right, tweak it again a few more times and you’ll soon have your own, simple, relevant daily digest.

I think the next radical step in Paper.li’s evolution is going to be some sort of unique delivery system. I see a great opportunity to offer the magazine in, say, a PDF format so that people can print a hard copy. Or, how about this: a centralised printing facility that not only prints but delivers, maybe via third-party agents that specialise in news, with franchises based in local communities offering a valuable source of local employment. It might catch on…

Whoops there goes another Bitcoin

Confidence. It’s what makes the world go around. Money too, as the song goes, but that’s pretty much an index of confidence. Watch the stocks plummet and you can be sure there’s lack of confidence there, or even the presence of panic. See the indexes climb and there will be some pretty confident people behind them. It’s certainly not love. Don’t you just wish the financial markets would hire more confident, less panicky, more loveable people?

So the latest Bitcoin hack – to the tune of $1.75 million, from Chinese Bitcoin exchange Bter – is another knock to the confidence behind the cryptocurrency. Bitcoin hacks have been coming thick and fast of late, or perhaps that’s just because the media spotlight switched onto them when James Howells threw away four million pounds’ worth of Bitcoins when he dumped his hard drives. Or it could have been when the major exchange MtGox was forced to close, taking 850,000 Bitcoin out of circulation (and then finding 200,000 of that in a discarded offline wallet).

I became intrigued by Bitcoin quite some time ago, as I do by most new shiny things that promise new ways of working. It seems to have come straight out of someone’s head (and we don’t know exactly which someone that is, although Newsweek once thought it did) and into the world quite literally without intermediation.

Bitcoin is a currency that exists outside of centralised government control, with a limited number of Bitcoins in existence. New Bitcoins come about by solving tough computational problems. The more computation thrown at the problem, the tougher the problems get. It is decentralised and self-balancing. The problem is, it doesn’t seem to work.

As a virtual currency it brings into sharp relief the idea that money doesn’t really exist. The money I have sequestered in bank accounts isn’t really there. It’s just ones and zeroes. Not even that – it’s actually just some magnetic impulses on a storage device somewhere. (No wonder some people still keep their money under a mattress).

The difference between Bitcoin and ‘real’ money is that ‘real’ money – even if it’s just magnetic polarisation – is backed up by the government. If a huge sunspot were to wipe all our hard drives tomorrow then hopefully the banks will have contingency plans, such as back-up centres behind lead-lined vaults buried miles beneath mountain ranges.

But virtual currency holds no such backup, by definition. OK, so the trace of payments is distributed across all peers, but as we’re finding out, rapidly, this is no protection against hacking, it would seem. Online wallets are insecure. Offline wallets can wind up in the local recycling facility. Entire exchanges go ‘pop!’, like balloons.

So another hack, another knock to the confidence of what was once supposed to be a brave new world of currency exchange. Strange isn’t it how these brave new worlds can turn so sour? Remember how the web was supposed to facilitate creative freedom? Or how social media was going to give everyone a voice? I pretty much gave up blogging because I realised my voice was being drowned out by the noise, so I had to come up for air. I’ve only started again because I need to exercise my writing muscles once in a while.

What now for Bitcoin? Let’s take it from Gavin Andresen, chief scientist at the Bitcoin Foundation, the closest thing to a central bank for the nascent cryptocurrency: his opinion is that Bitcoin is dangerous and people should steer away from using it. That’s one of the most important figures in Bitcoin as reported by the highly credible FT. So, that gives me confidence. Don’t even approach the glass. For now.

Gotta love cloud storage

I’ve never lost any data. Ever.

Actually, I tell a lie. I once lost ALL my data. I was recklessly drinking some Becks beer while doing some file management and somehow managed to delete everything from a drive that didn’t have the trash can activated. Thirty rather desperate (and suddenly sober) minutes later, I’d downloaded a good undelete utility and recovered it all. Phew.

Apart from that however, I’ve been something of a back-up freak over the years. It started when I got into home music production. All those hours of recording, arranging, mixing… to lose it all would have been devastating. This brings into sharp relief what we mean about the value of data. Sure, it has business value when you make it work for you. But it can also have immense personal value.

But as our data grows, and becomes more sensitive, backing up becomes more onerous. You forget. You can’t be bothered. You get out of the habit. You need a 1TB hard drive to back up a 1TB hard drive. You need secure, off-site storage – and when you’re working freelance from home, you might not have ready access to a nice, locked drawer somewhere else. And the more human intervention comes in, the more likely you are to screw it up. One day you will back up the wrong way, from the backup to the live. Or, your backup drive will corrupt and you’ll only find out when you really need it. I shudder to think…

Enter cloud storage. Now, I can just hear the stifled laughter. You’re thinking “Why is Brendan talking about cloud storage so late in the day? It’s been around for ages.” This is true enough and I suppose I’m a relatively late convert. But you never know, someone might be looking around for opinions on this, and if they find mine, then I’m telling them: go for it. In fact, if you’re looking around for opinions on this, and you just found me, then I’m telling you: go for it.

Cloud storage is brilliant. I never realised how brilliant until I really started using it. Now, whenever I save a file, and that cute little icon on the systray spins around, I know that I’ll never lose it, that in fact I can go back to a previous version if I need to, and that I can access it from any of my machines, anywhere in the world (mostly). And I don’t have to do a single thing. In fact, I don’t even have to spend one Bitcoin on it. It’s free. This is absurdly amazing. If it didn’t exist, someone would have to invent it. Which they already have, of course.

But cloud storage also opens up creative possibilities. For example, I’ve developed my own social media monitoring system, called ‘Bob’ until I think of a better name (although I’m starting to like it). Bob downloads data, aggregates it, cleans it, and then presents it in ways that I – and my clients – find useful. Where does Bob download the data? To cloud storage, of course. This means that I can query Bob at home, or in the client offices. It doesn’t matter. It’s entirely transparent to Bob. If I ever licensed Bob, I could have clients each with their own private cloud storage, all feeding data into their version of Bob. Marvellous.

Another possibility: your own personal music library. If you can get enough storage (or don’t have too many songs), then just port it all across to a cloud drive and you can access that from any machine, anywhere, and you’ll never need to back it up again.

Cloud storage is also a hugely useful facilitator for collaboration. I run the social media and programme editorial for the Kop Hill Climb, now a major international automotive event in Princes Risborough, Bucks. The entire organisational crew, comprising well over 20 people, uses cloud storage to share and store files. And, as Kop Hill Climb is a charity, generating around £50,000 each year to local causes, the fact that this storage is free is a welcome bonus.

So there you go. Cloud storage. It’s ace. There are plenty of articles out there detailing the various offerings available so I won’t bore you with the details, go and have a look (the PC Advisor cloud storage review seems comprehensive and up to date at the time of writing).

But if you really want to know, this is how I’m using it (note that I’m using several services because that means I get them for free within their storage limits because I’m a cheapskate):

  • Microsoft OneDrive – for my personal work. I use this simply because it’s baked into my Windows 8 installation. It seems a bit slow to upload but apart from that it chugs away nicely in the background.
  • Dropbox – for Kop Hill, and for one client, because they both use it. I find Dropbox rock-solid, but it doesn’t cope with concurrency very well (that is, when two people are accessing the same file). This can result in lost work or duplicate files, so watch out for that.
  • Google Drive – for another client, again simply because they use it. Honestly? Don’t touch it with a barge pole. I’ve had serious issues with Google Drive not syncing, resulting in lost productivity trying to figure out what the latest versions of files are. Really. Don’t go there. Unless something radical has changed, this is, in my opinion and experience, not fit for purpose. Sorry Google.
  • Mega – to store all my music, because you get a wopping 50GB free. OK, so it’s run by Kim Dotcom. OK, so he’s a controversial figure to some. But in a strange way I trust him more than I trust the likes of Google and Microsoft. At least there is a spotlight on him. And it just works.

I’ve also dabbled with Amazon Cloud but I found that a bit clunky. Just my own take on it.

There are other services too, so check them out as per that article. This just works for me. Between them, OneDrive and Mega ensure that when I save stuff, it remains saved. And, so long as I have strong passwords that I change, it remains safe too. Meanwhile Dropbox and Google Drive enable me to work with other people, albeit with more than a little frustration from Google Drive.

Let me know how you get on.

Four-dimensional social media analysis (no, really)

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Quadrants. Marketers love ’em. Actually, I like them too. I like the way you can draw two axes and plot things on them, and get an instant idea of often quite complex issues.

I’ve been using this approach quite a lot recently to plot social media. There are many, many things you can measure once you start grabbing data. For example the Facebook insights dashboard is very rich, plus you can download the data and do your own analyses. The Twitter analytics are good too. However, if you really want to know what’s working you need to measure across channels, and across competitors. Measuring across channels means that you need to use metrics that work across all of them, to compare like for like. And measuring across competitors means you need metrics that are publicly, consistently, readily available.

So what to measure then?

Well, as I’ve already said, there’s plenty you can measure but that doesn’t fall into these categories. The most important measurements are obviously what you have decided your business needs to look at, which might be extremely specific such as reduced time to market, improved support outcomes and so on. But a good start is audience size and engagement.

Do this: identify a handful of competitors, and in a spreadsheet note down how many Facebook likes they have, and what their ‘Talking about this’ total is. Then go into Excel and plot them in a scatter chart. You can do this either way, with likes going across and ‘talking about this’ going up, or vice versa. What’s important is that you now see where you lie in relation to the competitors, just for Facebook. Your objective is to move across and up. In three months do the same exercise and you’ll see whether you’ve succeeded.

This is very basic, and I can just hear some of you out there wincing at the idea of reducing social media down to this. But sometimes you do need to distill to key metrics, not least for internal reports. Time-pressured CEOs might not want breakdowns of every possible metric. If they can just say a chart that shows you’re moving across and up, that might be enough.

Across channels, across competitors

So that’s just Facebook. Now think about plotting the other owned channels, and how audience size and engagement might be measured. For example, Twitter audience size is followers, and engagement is retweets or replies (hint: use Topsy to count these). YouTube audience size is subscribers, while engagement is channel comments. And so on. Note again that these all must be what works across all channels, and is readily, consistently, quickly available. I agree that view count might be attractive on YouTube, or loop count is impressive on Vine, but there’s no equivalent of these on, say, Facebook. You could go through individual comments for each video on YouTube, but that would take ages. And you could look at the number of views your blog gets, but you can’t for your competitors.

Three-dimensional analysis

Now you’re looking across your owned channels, and comparing them to competitors, and that’s a good start. But if you’re getting into pulling data via APIs and suchlike, you can also draw more insight and add more dimensions. For example, if you’re pulling in user data, you can identify the number of unique commentators. Change your scatter chart to a bubble chart, and now your audience size can be across, your engagement can be up, and the size of the bubble can be the number of unique commentators.

Or, if your data includes sentiment analysis, you can use that in some way. A nice way to show this could be to have engagement going across, sentiment going up, and the bubble size representing audience size. But be careful: automated sentiment can go wrong. That’s why I tend to ignore it, and just deal with the other three axes.

Can you beyond three dimensions?

Can we have four dimensions? Audience size, engagement, unique commentators and sentiment? Unfortunately not it would seem. It would be great to have a sliding scale of colour intensity for the bubbles but I don’t think Excel does this. If it does, please let me know! Also, it could just be a bit too complicated.

What about time? That’s another dimension, right? This can get quite interesting when you plot over time. You can do this in Excel using macros to go through the data but it can get very complicated and slow, plus your data has to be in exactly the right format for the macro to work. So I’ve been using a Windows macro recorder such as JitBit to update the date in a spreadsheet, grab the resulting chart, paste it into Photoshop in a new layer, and build it up that way. Then export as an animated GIF and you can start seeing the ebb and flow of how your owned channels are behaving. It’s a bit like watching one of those cool time-lapse videos of clouds scudding across the sky or flowers growing, blossoming, and dying within seconds.

This is what you can see at the top of this post. It’s from work I did quite a while ago and I think it’s old enough to share publicly now. You can see how the bubbles move around and I can tell you now that they do correspond to marketing activity. This actually goes beyond just charting using owned channels and in fact takes all mentions across all channels, so giving us an idea of where we lie in the marketplace of conversation.

In this instance I was able to show that the work I did had an impact, at least within just the social media-sphere. I’ve since used similar methods to prove similar effectiveness and actually secured more funding for social media initiatives. If nothing else, this shows that data analysis can lead to ROI. Now, let’s see if we can plot that…

Data, you need

This is a cross-post from Ranieri Communications…

Actual output from one of my dashboards

Have you seen Particle Fever yet? If not, you should. There’s a seminal moment when, on achieving collision, a Cern star states triumphantly: “We have data.” It’s the point at which the theorists craned their necks eager to see what the experimentalists could actually prove. Suddenly, this wasn’t theory any more.

If you’re in any way serious about your social media, you need to make sure you have data. Without data you don’t know what the current situation is, so you can’t measure where you’re heading, so you don’t know whether or not you’ve been successful. You need data to know whether your strategy is working.

What data exactly? Well, that depends on what you want to achieve. Say you want to use social media to improve your SEO. What makes you think you have a problem with SEO in the first place? What needs fixing? Better find out first, because that’s how you’re going to measure success. Or perhaps you want something more qualitative around reputation management. How are you going to quantify this? Where are you going to get the data from?

There are three approaches to getting data depending on how much time, expertise or cash you’ve got: manual, semi-automated, and fully automated. Here’s a quick rundown of each.

Manual: get typing

Everyone loves a spreadsheet. They’re amazing things and you can go a very long way by manually entering data that is publicly available and then drawing insights from it. The key here is to use data that you can compare like-for-like across social media channels to get an idea of how they’re doing. So, while Facebook’s dashboard for example is rich in data, and you should certainly be using it to improve your performance, a lot of the analysis isn’t available for other channels such as Twitter, or Instagram, or your blog.

At the very basic level, you can look at two essential metrics that work across all of social media: audience size and engagement. The audience size is the total potential audience you could reach with your message, so that’s fans of your Facebook page, followers of your Twitter feed and so on. Engagement is when people actually do something in response to reading about you, so they retweet you or they comment on your Facebook page.

Do this for your competitors too, build this up over time and you can start seeing patterns in the data. You’ll see spikes that correspond to activity, and how to develop more advanced metrics off the back of these. How about dividing engagement by reach to get insight into how engaged your audience really is? How about adding frequency so you can start forming an idea of tweet quality? How about requesting access to the client’s Google Analytics and looking at how social media referrals to the website are behaving? Develop your own charts, stamp them with your logo, and you’ve got a bespoke measurement system. Port this to an online resource such as Google Docs, and you’ve got an online dashboard. Nice.

Semi-automated: learn APIs

If you’ve got an in-house geek (the one you keep in the cage in the corner and occasionally feed with Haribo) then they might like this: you can start getting involved with Application Programming Interfaces (APIs).

An API grabs the data directly rather than going through the manual procedures. So, by using the Twitter API you could directly interrogate the Twitter database and get follower figures, retweets, times of tweets and so on delivered direct to your machine rather than having to input it manually. You can also use the APIs of other social search engines such as SocialMention and Social Searcher that do a lot of the grunt work for you, by searching across multiple social media sources and aggregating them.

So, by downloading the results of API calls, you build up a store of data that you can then aggregate and analyse, again in Excel. With a canny combination of download managers, batch files and macros, you can do this all with just a couple of keystrokes.

The difference here is in quantity and types of data and therefore insight: you can accrue literally thousands of data points detailing who said what, and when, and you can start understanding who your influencers are, and what your issues might be – plus those of the competition and therefore the industry at large. At this point you really do start understanding the landscape.

If you have a smattering of statistical knowledge you can also start charting the ebb and flow of debate. Moving averages show the underlying trends. Crossovers of moving averages are highly significant. And so on.

Fully automated: bring in the Big Guns

If fully manual requires investment in time and semi automated needs investment in expertise, then fully automated is the money play. Here, we’re talking systems such as BrandWatch with millions of sites categorised, crunching huge amounts of data using dedicated server farms. It’s the rocket science approach and while this is mostly the domain of large companies that provide consumer services such as telecoms companies, there’s also a strong argument to be made that smaller agencies can use them profitably by sharing the cost across several accounts.

Hands, APIs, BFGs: Which one’s right for you?

If you’re not storing and analysing any data currently, then you need to start, right now.

At the very least start recording reach and engagement, ideally alongside competitors. It’s a useful exercise as of itself because you really start to understand cause and effect, and get to grips with the concepts.

When you get the hang of that, and you’d like to dive deeper, see if you have a geek in your organisation, or a latent geek, or know someone who keeps one. They might be able to ramp you up to the semi-automated solution and then you become something of a social media data guru.

And when you’re finally seeing the shiny green numbers coursing through the very fabric of the Matrix itself, and you’ve landed that major social media account – or you’re a postdoc working at Cern – it’s time to hoover up as much data as you can possibly get your hands on. Even if you don’t uncover the secrets of life, the universe and everything, you’ll know what drives conversation, and that’s a decent second.