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…

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.