I try to include a few mobile screen shots in my presentations — if only to highlight what is becoming an increasingly depressing truth.

If I were being really careful though, I’d make sure that I used mobile screen shots even when I wasn’t making a point about mobile. Yes, it might add a minute to my workflow. But it might also be the kind of positive discrimination that helps change attitudes (not least my own: I still have to make a conscious effort to think about the mobile experience.)

PlaceIt

Now, take a look at the following screen shot:

By placing the experience in context it tells a more compelling story (I believe) about the user experience. It’s more relatable, familiar. I can comprehend it better in terms of my own previous frustrations.

The best thing? It’s easy enough for us all to do… I used a free web service: PlaceIt (from site design & hosting service, Breezi) where you can choose your mobile device (iPad, iPhones 4 & 5, a variety of Android handsets) and a series of near-realistic environments. All you need do is upload a screen shot taken on your phone, and it will be automagically adjusted and placed in situ (/ht Jeff Taylor from the Social Marketers Facebook Group for this great link.)

Do bear in mind that screen dimensions are often peculiar to the handset; so a screen shot taken on an iPhone 4 won’t look good on an iPhone 5 for example.

This is the best thing I’ve seen all month; and I share it with the massive recommendation that you try it out, and consider using it in future presentations.

Related Notes

I’ve also made great use in the past of Fabien Kreiser’s Screentaker — an OS X app aimed at the native app developer. In theory at least, one should be able to create one’s own versions of PlaceIt. And it’s great for telling user-journey stories.

Another useful service (/ht my colleague Laura Cogo) is Google’s Ready To Get Mobile.

And now that Evernote’s Skitch screen capture and annotation tool works on both Android and iOS, it’s becoming an ever more essential app for me.

Are there any others? Recommendations for good, stable mobile emulators that work on OS X and Windows would be particularly gratefully received.

Edit: By coincidence, just after posting this I was listening to the latest Mac Power Users Podcast, where Serenity Caldwell recommended Reflector — an iOS-only screen mirroring app (for both Windows & OS X) that lets users mirror their iOS device’s screen on their laptop or desktop. You can take screen shots (I’m using the CMD-SHIFT-4 + Space bar key combo to grab the whole window) or record a screencast from your handset or tablet. Feels like something that will become a big part of my workflow.

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more than half [of total budget] goes to Facebook. YouTube and Twitter each get 13 percent, while about six percent is spent on influencers and 5 percent advertising on blogs.

Or – as pithily expressed in a ReadWrite Social headline, “Brand Marketers Totally Miss Social Media Influencers.”

The moment anyone proves to our clients’ satisfaction (or indeed mine) that influencers influence sales, I’m sure we’ll move away from Facebook’s TV-sized audiences and Twitter’s second-screen opportunities, news-hungry journalists and celebrity-studded firmament. The reality for most big FMCG advertisers is that — if anyone truly believes they are a soap powder or fizzy drink influencer they’re certifiably nuts.

Number Two in my list of things everyone should read: Chapter 4 of Duncan Watts’s ‘Everything is Obvious’. It’s long, but worthwhile reading. Nonetheless for those of you who are (like me) at the goldfish-in-an-industrial-techno-club end of the ADD spectrum, here’s the TL;DR:

marketing strategies that focus on targeting a few ‘special’ individuals are bound to be unreliable.

Watts is the only special individual to whom you should be listening today.

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So to try it out I downloaded some historic weather data from the Met Office, munged it around a bit (Correlate seems to take well to two column CSV formatted data using US-formatted dates — mm/dd/yyyy), then uploaded it into the tool.

Running the tool gives me a set of 10 searches that closely match the seasonality and trends in my data set.

When the temperature increases in the UK, we’re likely to be go fishing, be bothered by flies and spot grass snakes. Only after we’ve trimmed the hedge and creosoted our garden fences of course.

I slightly resent this image of the average English person as a coarse-fishing gardener obsessed by party boundaries, but in my soul of souls I fear that it’s probably fairly accurate.

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One of the articles I share most often is Martin Weigel’s ‘Engagement: Fashionable Yet Bankrupt’. The paper has become a bit of a touchstone for me; and I can’t recommend it highly enough. On re-reading it recently, however, I was struck by something I’d not really noticed before; the fashion for Engagement that he was discussing seemed to be much older than I’d thought.

So, armed with an exciting new discovery (ScraperWiki) I set off to datamine the BrandRepublic archives. My aim was to find articles that mentioned the term “engagement”, and chart them day by day.

Here’s the scraper I built. And here’s what I managed to come up with as a first stab at the visualisation.

It was deeply flawed, and over-plotting hides most of the detail, but I felt I was on the right track. So I ran the plot again, only this time I plotted the monthly totals instead of day by day (I’d never used R’s zoo library or table function before, but together they represent another nail in the coffin for my everyday use of Excel):

That’s a nice-and-steady looking upward trend. But looking at the data, I could see one or two problems. For one thing, Brand Republic’s search takes a few liberties: a search for “engagement” turns up results for “engaged” or “engaging.” For another, it seems that many clients engage agencies, not just their audiences. I chickened out a little, and using the keyword analysis toolset that I’ve been building, I tried to narrow and focus the list. This gave me a list of just over 60 bigrams (like “engage audiences”, “customer engagement”, “engaged with”). While this list would significantly reduce the results returned, I’d feel more secure about the findings.)

Armed with this list, I did a little more mining, and finally produced this chart, comparing the increase in posts mentioning “Engagement” and “Social Media” in the Brand Republic archive:

Things to notice

The trend for Engagement begins long before the trend for Social Media, and possibly even a time before that misbegotten ur-text, those Plates of Nephi of Social Media, “The Cluetrain Manifesto“. I believe that this has coloured thinking about Social Media’s strategic and business objectives, and not for the better. I suspect that we have inherited and assimilated the idea of engagement as a goal, as a KPI into our practice in the same way that the early Christians absorbed elements of paganism into their beliefs.

Marketers — who are preternaturally sensitive to trends as it is — are swamped by mentions of Social Media and Engagement. They can’t escape them.

And — don’t both lines look suspiciously as though there’s a feedback loop in place? Journalists write about trend x. Marketers read about trend x, come up with their response. Journalists write about their response. Should we worry about this? Or just accept it as the way of the world?

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These are the notes I made based on last night’s research, and the best links I’ve seen shared so far. All the “Tips” posts seem a little premature given the limited Beta roll out.

Facebook’s recommendation to Brands

1. Make sure your Page, Place or App information is complete and up to date
2. Strengthen your connections.

So — business as usual there, then. Get your Page in order, grow your fans.

Facebook’s “Introducing Graph Search” (and waiting list sign-up)

Implicitly, this is a better way to search Facebook. You want to find that girl you met last night at that guy’s party and you can’t remember her name? Want to make a list of all your friends who live in that town you’re visiting? Here you go.

Primarily, this will improve the Facebook user experience.

NB: The beta is rolling out in US only.

The Verge’s liveblog (with photos)

NB: The whole top bar title area becomes a search bar?

Chad Wittman’s “Is This The Facebook Search We’ve Been Waiting For?”

A late, but useful addition to this list. Chad points out:

Businesses with a physical location, AKA local businesses, will benefit the most from Graph Search. The second most benefitted businesses will be ecommerce. This should hold true at least through the initial phases of Graph Search. These businesses have the easiest input signals into the Graph Search algorithm, while also possessing clear-cut opportunities to obtain sales.

But points out many of the potential flies in the ointment, notably that the problem Graph Search is being hired to solve isn’t necessarily clear, or well-recognised.

Jesse Brown’s “Facebook’s B.S.-powered search engine”

Brown points out that the quality of the behavioural and surrendered data that Facebook is relying on is patchy at best. This will feed back into user experience.

Graph Search is only as good as the information we give to Facebook. And my Facebook information is garbage.

My profile does not include my employers or my alma mater. I don’t “check-in” when I visit a location, nor do I rate restaurants or movies on Facebook. I don’t “like” things because I like them, I like them when I’m trying to help my friends promote something, or to make a cheeky joke.

Venture Beat’s “Facebook stock closes down at $30.10 after announcing Graph Search”

Stock is down after rising on expectations of announcement.

Investors, interested in a new way to make money off of Facebook, pumped the shares further up to a high of $32, but the lack of information about a monetization strategy or advertising in search caused the stock to remain in the red. It closed today at $30.10 a share, down 2.74 percent.

Just as likely to be because investors “buy the rumours and sell the news”. I suspect the lack of “monetization strategy” could be a red herring here.

Comscore’s “What History Tells Us About Facebook’s Potential as a Search Engine” (June 2010)

Early indications that Facebook users were already heading in this direction

the fact that we are seeing the first real signs of a burgeoning “traditional” search experience bodes well for the future potential of Facebook as a search engine. I anticipate that we will see this type of consumer behavior evolve along the same lines of traditional search as more dollars flow towards social media.

My take outs

• This is primarily about improving user experience. Facebook’s search has long been its Achilles heel. Despite being more or less fit for purpose (it readily identifies the John Smith I’m most likely to know, rather than the most famous John Smith) it has always been a lacklustre experience. And yet, anecdotal evidence from Facebook suggests that users are treating it like any other search box; as a means to navigate the wider web. Furthermore, there are other kinds of search (“which of my friends live in London?”) that have been impossible to date.
• Graph Search searches behavioural data (listens, likes, checkins) and surrendered data (profile information). Not all these data will be good – or rather, there will be a spectrum of reliability. Spotify Listens are probably good data (unless, like me, you share an account with a whole household.) Restaurant checkins may be heavily biased towards those offering checkin deals, and Page Likes to the biggest advertisers.
• Graph Search may not lend itself to all brands I doubt that “what soft drinks are most popular among my friends” will be heavy volume, whereas “what restaurants are good in Dublin” could be.
• User experience is key to the development of this search. User satisfaction with results will determine what kinds of search become popular. Facebook is particularly good at responding to behavioural data, and I’d expect to see the search become optimised for these (if only in terms of typeahead prompts.) I’d expect to see some interesting differences between mobile and desktop usage.
• The Page has become more important. Until this announcement, I had come to believe (with many others) that Pages might be in decline except as a means of injecting fan-endorsed stories (and ads) into users’ news feeds. The new search may well restore their strategic significance.
• Open Graph objects are increasingly important. This trend continues. Brands and retailers must Open Graph-enable their owned spaces and e-commerce engines if they want to appear in search.
• Feedback from search data is essential for brands to understand how to optimise their search results Google has a strong set of planning and feedback tools. We know search volumes, search rankings. It’s not immediately obvious that these will be available to Facebook advertisers in the short to medium term (or in the case of rankings – ever.)
• Bing optimisation may have increased in priority. Bing results will (as ever) be included in the results; although (I assume) only when Graph Search fails, or as secondary material. However, the news looks good for Microsoft (and, indeed, $MSFT seems to up on the announcement.)

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It’s notable when looking at Twitter data how many of those bigrams are in the form “rt @{username}”, and how they’re distributed. In the 7 days of tweets that I’m using as my sample corpus, one even makes it into the top 10:

If I plot out their occurrence versus their rank, it seems that they follow a Zipf-like distribution.

The random amplification problem

When we’re looking at social data from Twitter, most tools that I’ve used will take all the retweets into account (there may be a few exceptions, and I’d be grateful if you’d let me know.)

But it seems to me that these retweets should be seen as “Just One (Wo)Man’s Opinion”; and that (in most cases) we should de-dupe them mercilessly.

This tweet from ageing minipop Ariana Grande turns up more than 400 times in my sample corpus (and was retweeted almost 3k times that week.)

#PerfectAlbums Channel Orange @frank_ocean

— Ariana Grande (@ArianaGrande) December 18, 2012

Now, it may be that lots of people agree with her about Channel Orange, but her retweets account for around a third of all mentions of the album in my sample set. Does that reflect on the popularity of the album or that of @ArianaGrande herself? How might that affect your predictions of the album’s success? This may be a bad example; after all, it peaked at #2 in the Billboard 200. But you probably know what I mean.

On the other hand, the earned media effect of a 4m follower Twitter account holder like Ariana would have to have some positive effect on sales. So under other circumstances you’d want to know both who was tweeting and how often they were being retweeted (incidentally, Ocean’s label Def Jam is owned by Grande’s label Universal. I’m fairly naive about the record industry, so I’m plumping for “coincidence”. After all, there are kinda sorta only three majors these days, so coincidences will provide a satisfactory explanation.)

The Zayn Malik example

I thought I might do a little further digging into the relationship between fame and retweets. Here’s the plot.

It seems fairly inconclusive. Sure, there’s a bit of an uptick as users enter into the realms of the super-Twitter-famous, but equally there are some stinkers.

Take a look at the pink dot at the lower right. I’ve singled that out for special mention. It marks the 5 retweets (over the 7 day period) of a tweet by popular beat combo One Direction’s Zayn Malik. Zayn may have more than 7m followers, but when he says vacuous things like,

RT @zaynmalik : So , if cheese is orange does that mean lemons are green?

then even he must be doomed to obscurity. Clearly some tweets are going to be less retweetable than others, even if you’re cute and famous. So I began to make mental notes for some kind of more complex traction model that took into account both fame and retweet worthiness.

Luckily I checked. Zayn tweeted this two years ago.

So , if cheese is orange does that mean lemons are green ?

— zaynmalik1D (@zaynmalik) January 22, 2011

Since then, this epigrammatic masterpiece has been shared and reshared more than 5k times, spiking regularly as it touches the souls of new audiences.

So here’s a problem that I hadn’t really considered. If a single meaningless tweet from a One Direction band member can live for two years, how’s that going to effect relevance?

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Last night, Mrs Mediaczar asked me why I was doing this. “Surely”, she pointed out, “you’re reinventing the wheel.” This is true, of course — and I’m not even a particularly good wheelwright when you come to it. I muttered a bit but I do have my reasons. Most of these have to do with flexibility; the freedom to create and tweak ad hoc workflows that suit individual routes of enquiry. I also think that it’s important to have a feel for one’s research data; a feel that one can’t get if you’re divorced from the nitty gritty.

But the reality is that I’m not really writing anything. I’m just stringing together a set of Unix tools that are intended for more or less exactly the purpose I’m using them. The Unix command line has a wonderfully powerful tool set for playing with text data, and it’s a pleasure to be able to wield things like grep, sort and wc (I don’t feel the same way about see and awk, but that’s what perl is for in my world.)

Moving along

Anyway, the basic stuff is out of the way, and now’s my chance to do something a little more interesting. In the last article, I moaned about word clouds a little, and I’m going to take the opportunity to do it again with a slightly contrived example. Here’s the word cloud visualisation of ~2.7k tweets I just pulled.

And here it is in a Wordle

Now both of those could be probably be used by an enthusiastic social media guru to tell a story (although I’d like to see how they explain the double appearance of the keyword “orange” in the Wordle version.)

But what I know (and they couldn’t) is that the search I ran was for orange AND "not good" (did I mention that this was contrived?)

What has happened is obvious: the word “not” is a common stopword, so the visualisation application has removed it, notably changing the story in the process.

Bigrams

“Not good” is a bigram — two tokens (words in this case) that appear together. I’d like to be able to analyse these alongside the simple keyword list I produced last time.

The process I’ve got mapped out is simplicity itself:

1. identify the bigrams, and
2. remove the stopwords, being careful to retain the ones I consider important.

The first job is made even easier by a quick Google. Thapelo Otlogetswe has already published the perl code to do this which I’ve modified only very slightly here.

here’s what it does to the orange AND "not good" content:

Already I can see the difference, but there are still lots of useless stopwords here. For this exercise, I’ve decided that I want to retain the stopwords “not” and “rt” (I’ve just added these to an hash called %ignore, and told the stop word filter to ignore any bigram beginning with those words. I’m sure that this will come back to bite me on the arse later.

So now we see this output (which is more or less where I wanted to get to be):

Running this process on the data I collected in the first of this series of posts, I get the following list:

Friday, 11 Jan 2013 23:44:33 Oh no! I’ve just discovered that for some reason or another, the code I borrowed from Thapelo is over counting. I’ll come back and fix this. I also need to check whether it’s necessary to lose the apostrophes in the bigram builder, or create apostrophe-less versions in the stopword list. But it’s late now, and the mini-mediaczars wake early. Bed time.

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Is that so obvious so as not to merit mentioning? Why should it be like this?

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