As has often been observed, there’s something irresistable about schadenfreude. That’s one reason for the obsessive finger-pointing by the digerati every time a new brand experiences a social media crisis .

Another may be our unholy desire for traffic. After all, I’m writing this blog post in response to today’s Nestlé-Greenpeace-Facebook storm (if you’re coming to this story late, Sam Ismail’s post offers background). More to the point, at least four bloggers cleverly promoted their coverage of the events in the Facebook comment stream.

Several social media mavens got so involved that they joined the side of the protesters against Nestlé: twittering animatedly, changing their avatars, publishing lists of brands owned by Nestlé (and which were therefore eligible for boycott), or by referring the community manager to the Cluetrain Manifesto or Gary Vaynerchuck videos.

Examples of similar foul-ups pepper our presentations, mine included: I’m presenting on crisis management on Tuesday. All my case studies come down to two things:

• people inside your business fucking you over through ill-will or ignorance; and/or
• people outside your business fucking you over through ill-will or ignorance

Put that aside. As an industry I’d argue, it can seem that we’re obsessed with failure. And we’re nasty with it. Other than politics or the pro-wrestling circuit, I can’t think of another industry so ready to criticise one another in public. At each new crisis, the web buzzes with our speculation, exaggeration, misinformation, chinese whispers, and rumourmongering of which the events of today were just an example.

Social Media Pogroms

Why are social media gurus so ready to criticise each other in public? I suspect that our readiness to criticise the brands and agencies for whom (under different circumstances) we might work is in fact an artefact of social media. This is a an example of social behaviour. Compare it to mob behaviour. Playground behaviour where netiquette is ignored. We want to join in the fun; even when the fun turns out to be kicking someone when they’re down.

Karma

Kerry Gaffney and I once came up with a rule-of-thumb: “don’t be too quick to jump in because there but for the grace of God go you.” It sounds sententious, but we reckoned that it would pay off in time; people would, we felt, be less likely to jump on us when we inevitably #failed.

Here’s what I think: until we can grow up and leave the playground, we won’t be able to help our clients gain the confidence they need to make the right decisions in this area.

Individuals should try to come to terms with the fact that they work in marketing; and stop trying to moonlight as anti-globalization anti-corporation cowboys. Learn to love what you do, for fuck’s sake.

And I think we should go a step further. We should stand up for each other. Only a very few (and highly honourable) people stood up for We Are Social when they took an unnecessary pasting over Eurostar. I’m ashamed to say that I wasn’t one of those; while like many others I left messages of support on Robin’s post, I didn’t help to fight fires on TechCrunch or elsewhere.

And that brings me to the last idea: where we can, we should probably try to help clients in distress — even if they aren’t our clients. That might help our industry look professional and responsible. It’s a bit of a big ask: “why,” you might ask, “should I spend valuable business cycles helping someone else’s client?” But wouldn’t it be more constructive than spending those same few meagre cycles adding fuel to yet another social media fire?

Post Script

Incidentally, in the unlikely event that you’re interested in my take on the Nestlé situation: a bunch of social media gurus and pre-existing anti-Nestlé activists probably won’t do too much damage to the brand. Anything to do with Facebook has a whiff of slacktivism to me. Within a week or so there’ll be another stinking piece of Social Media carrion around which we can snarl and posture. Also, a campaign to knight Eddie Izzard seems to have more traction in the non-guru social media world.

One of the problems I quickly encountered when noodling around with Mechanical Turk is the limited and clunky web interface. Amazon has a handy comparison table which shows you what I mean by “limited”. Below is a look at the web interface for managing submitted HITs which will show you what I mean by “clunky” (which you can click for bigger.) None of it is JavaScript enabled — so every button-click requires a page reload. And there’s no logging for who’s been paid, and who hasn’t. Aargh!

After my first foray into using bonuses to engineer better results, I found that I needed to pay over a hundred bonuses. It rapidly became clear that paying these using the web interface would be nearly impossible, forcing me to look at the command line interface tools a little faster than I’d been planning.

Amazon provides a pretty full toolkit — but right now I’m only really interested in the grantBonus.sh tool. This meant I could (fairly quickly) knock up an improved workflow.

1. Download all completed HITs as a CSV using Mechanical Turk’s Export Results button;
2. Use a spreadsheet to approve and reject each HIT manually (need to improve upon this);
3. Export the sheet as a CSV and use Mechanical Turk’s Review Offline button to load it back into the system and automatically process the basic payments;
4. Append bonus values in a new column;
5. Export the sheet as a tab-delimited file;
6. Use a perl script to build a list of shell commands (I’m guessing if I were more comfortable with shell scripts I could skip this step); and
7. Run that list of shell commands

Not – of course – as simple as it could be. Each stage could be automated more; and at some point will come the moment when I don’t need to use the web interface at all.

Anyway — here’s that perl script.

[sourcecode language="perl"]
#!/usr/bin/perl

print "\tProcess complete: $count bonuses to grant in $outfile\n";
END
[/sourcecode]


When London PR blogger Melanie Seasons started her blog two and a half years ago, the subject of her first post was her first post from her MySpace blog. In fact, she took most of her content from there as well. She calls her first post “a cop-out first post of another first post”, but I think that she might have spun it as a “metapost”.

In some ways, the post you’re reading now could be another metapost — a post about first posts. But it’s really about new ways of working.

I know about Melanie’s first post because I’ve been carrying out some quantitative research using first posts. I took a user-generated list of UK PR blogs that I helped curate last October, and attempted to identify the date of the first ever post for each blog.

This is a task that’s almost impossible to automate. Getting the newest post is a cinch for a computer – the oldest post not so much. And yet it’s relatively simple for a human to perform the task – generally it’s just boring and repetitive (although I challenge you to find the first post on Jed Hallam’s blog, Rock Star PR). I’m not one of those people who enjoys repetitive tasks, so I decided to take this opportunity to set up the Magic Bean Lab’s first experiment; to test the efficiency of various alternative labour sources.

Method 1: e-lancers

I used Freelancer.com: a well-established e-lance and outsourcing marketplace I’ve used several times in the past. As we get more used to buying things that we can’t see over the web, the e-lance market has become a no-brainer.

I employed two e-lance researchers. I’ve found that running researchers in parallel on projects like these reduces the need for overmuch error-checking. Quality Assurance (QA) rapidly becomes the biggest overhead in any project like this.

My quick-and-dirty QA process runs as follows:

• Compare results side by side
• If the results agree, accept this as the correct answer
• If the results disagree, do some checking myself.

In the event, the two freelancers agreed in 88% of cases, and I only had to check the remaining 12%.

An obvious problem: if both freelancers agree on an incorrect answer, I won’t check it. This happened in approximately 4% of cases during this test (I’m working with a known set of data here, or — of course — I wouldn’t be able to tell that.)

Another problem which you wouldn’t see otherwise: it’s fairly time intensive. I have to post the project, wait for the bids to roll in, assess the bids and so on. The whole process took about 48 hours from start to finish.

Still, it beats doing the work myself, and it’s fairly scaleable.

Method 2: Amazon Mechanical Turk

I’ve been noodling around with Amazon Mechanical Turk for a few months now. Mechanical Turk, for those of you who don’t know, is named after a late eighteenth century hoax that — while it purported to be a machine that could play chess — was in fact powered by a person sitting inside a box.

Amazon describes its Mechanical Turk service as follows:

Amazon Mechanical Turk is a marketplace for work that requires human intelligence. The Mechanical Turk service gives businesses access to a diverse, on-demand, scalable workforce and gives workers a selection of thousands of tasks to complete whenever it’s convenient.

So instead of paying a freelancer a fee to perform all the research, I can split the job into its constituent parts that Amazon calls HITs (Human Intelligence Tasks) and pay a fractional piecework fee (say $0.10) for each blog on the list. This has two advantages:

1. It’s much faster. Instead of one person working through the list in sequence, the list is processed in parallel;
2. It’s cheaper. There seems to be a lower limit to the bids on Freelancer.com of around $30 per job; and
3. It’s extremely scaleable. Using Amazon’s API, I can embed humans into automated processes. More on this in later posts, I hope.

The only problem? In the words of one Turker I’ve interviewed, “There are few spammers in mturk who spoil the mturk community.” In other words, I have more QA problems. It seems that people are more inclined to be dishonest when smaller sums and greater anonymity are involved. I’m sure that a behavioural economist like Dan Ariely could explain this in detail — but for the moment, please let’s just accept that most people are more inclined to cheat you out of $0.10 than $100.

So here’s the first thing I tried.

• Run three Turkers in parallel
• Accept the earliest date received

Clearly there are problems with this method. But not as many problems as you might think. Results were approximately 80% accurate. And it took less than 2 hours from start to finish: that’s 46 hours faster than the e-lancer method. And remember it’s costing me less, although I’m paying for answers that the method deems to be inaccurate.

But surely I could do better.

Method 3: Amazon Mechanical Turk and “elementary game theory”

To be honest, I probably know less about game theory than you do. I know about the Prisoner’s Dilemma and the Three White Hats (which probably isn’t even game theoretic; only going to show how little I know.)

But I’m using “game theory” both very loosely and very specifically to mean “I know a little about how people try to game systems, and I’m going to do my damnedest to use that knowledge to hack their behaviour.”

So here’s what I told the Turkers:

Each HIT is being performed three times, and the results will be checked against each other:

1. When all three HITs agree, a $0.10 bonus will be paid to all workers.
2. When only two HITs agree, those two will be accepted as the correct answer. The third result will be rejected.
3. If all three HITs disagree the requester may consider rejecting all three.
4. Occasionally, it may be the case that it is very hard for you to find the correct date. We want to make it worth your while to find this. Please note this in the Additional Comments box together with the process you used to discover the date and we will consider paying a discretionary bonus of $0.50 on top of any other bonuses. For particularly hard HITs, therefore, the total potential upside is $0.80

Please, take your time, and get the right answer. It will be worth it for you, and worth it for the other workers performing this task!

My hope was that I would discourage the spammers. Here’s the logic:

Spammers know that they won’t get paid unless at least 1 other person agrees with them. For this to happen, either:

1. They’d have to be lucky enough stumble across the right date by mistake (at odds
   of around 3650:1 for blogs created in the past decade); or
2. They’d need to be accidentally matched by another troll (same odds.)

Incidentally, it turns out this “majority rule” is unpopular with the Turkers; mostly because they fear the spammers don’t read the instructions, and will still queer the pitch (which seems like a legitimate and logical strategy for spammers).

Nevertheless the task was completed in much the same time as the previous method; and with startling results. The answers received were 97% accurate. That’s more accurate than the freelancers (just under 96%), at a lower cost, and in a fraction of the time.

Comparison of the three methods

In the table below, Cost per Response is adjusted for accuracy.

Tomorrow I’m getting married, so I probably won’t be posting for a while. Not, of course, that I’ve been posting a lot recently.

Without wanting to get sentimental (it’s not that kind of a blog, and I’m not that kind of a man) I can say that not only did I never believe that I’d find someone like Krista, but that now I have found her, I still can’t really believe it.

I’m saving the rest of what I have to say for my speech tomorrow evening. There are all sorts of little surprises planned for the day, but one of the biggest surprises right now is “what Mat will be saying in his speech” because I’ve yet to write it. Tim has told me “be nice to everyone and try not to sound like Hugh Grant.”

Anyway, the hashtag for my wedding will be #wediaczar. Given that the audience is startlingly low on digital media bods, it’s not like I think it’s going to trend or anything, but it seemed like too good a hashtag to waste.

Geek alert: if the title of this post isn’t a dead giveaway I should tell you — unless you’re interested in APIs and badly-put-together bits of code — this probably isn’t for you.

I’ve recently found myself using a service provided by Damon Clinkscale called DoesFollow. All it does is answer the simple question “does twitter user A follow twitter user B?” Apart from a frill which lets you reverse the order of your question (“does twitter user B follow twitter user A?”) that’s all it does. You can even interrogate it from the address bar like this: http://doesfollow.com/barackobama/mediaczar

While I was thinking about how useful a service this is, I was suddenly struck by a moment of clarity. A lot of the research I’ve been doing could be simplified by something like this.

Quite often I want to find out whether MPs or congressmen or PR people follow each other on Twitter.

The way that I’ve been doing this until now is

1. make a list of the people who I’m interested in researching
2. for each person on that list, grab the list of all the Twitter people whom they follow
3. process the list so that only relationships between the people on the list show up

If all I’m doing is checking to see who follows whom, then this is a horribly wasteful way of doing things. The Twitter API limits the number of calls one can make on it — so this wastage leads to things taking much longer.

If only I could cycle all the names I want to check through something like DoesFollow!

Well – it turns out that I can. And in theory it’s not much harder than using DoesFollow. The Twitter API (which is what DoesFollow uses, after all) has a method called friendship/exists. All we have to do is send Twitter the following request:

http://twitter.com/friendships/exists.xml?user_a=barackobama&user_b=mediaczar

and it will come back with the answer:

<friends>true</friends>
or
<friends>false</friends>

Kludge-y perl code

(This fabulous picture courtesy of There, I Fixed It)

So I tried to do this using Yahoo! Pipes, but there are too many nested loops. You need to do something like this:


get list of names

}


There’s no easy way to get Pipes to do this, as far as I can see (I’ll keep trying, but if someone else can help, I’d be v. grateful.)

So I’ve pulled together a badly-written perl script to do the work for me.

The script

[code lang="perl"]
#!/usr/bin/perl
# checks the Twitter API to find the friendships between a list of usernames
# this should really use the NEW API call that would let us halve the number
# of API calls
# author: Mat Morrison
# date: Friday July 10, 2009
use warnings;
use LWP::Simple;
# set up variables
# we're just using a whitespace delimited list for the moment
my @usernames = qw(kerrymg mediaczar timhoang titusbicknell);
# let's build the matrix with a hash of hashes...
# to begin with, we'll include diagonal values -
# that is -- we'll check to see whether @mediaczar follows @mediaczar
foreach $user_a(@usernames) {
foreach $user_b(@usernames) {
# we should put in a conditional clause that will check for the diagonal values
# and not bother checking whether someone is a friend of themselves...
$url = 'http://twitter.com/friendships/exists.xml?user_a='
.$user_a
.'&user_b='
.$user_b;
# get XML file from Twitter -- it's an astonishingly simple XML file that reads
# true
# or
# false
# so we don't need to do much with it...
$follows = get $url;
die 'Can\'t get $url' unless defined $follows;
# strip the tags - I'm using a generic "HTML stripping" regex
$follows =~ s/<(.|\n)+?>//g;
# we should probably convert "true" values to 1 and "false" values to zero or blank
# now let's push data into the matrix
$matrix{$user_a}{$user_b} = $follows
}
}
# spit out the data as a tab-delimited table
# print the top line first
for $user_b ( keys %matrix ) {
print "\t$user_b";
}
# now print the values
# they're all neatly arranged in the matrix so we
# can just print them out sequentially
for $user_a ( keys %matrix ) {
print "\n$source";
for $follows ( keys %{ $matrix{$user_a} } ) {
print "\t$matrix{$user_a}{$follows} ";
}
}
print "\n";
[/code]

Where next?

Most of my thinking is included above in the code comments. An obvious mistake I’m making is checking to see whether, say, @mediaczar follows @mediaczar. That wastes n API calls per search. But a more serious mistake is not to be using the new friendships/show method. Because it tells you whether user A follows user B and whether user B follows user A at the same time, it would save me lots of API calls. How many lots? Well take a look at this.

This is what I’m doing at the moment — checking each and every cell in the matrix:

This is what I’d be doing if I removed the diagonals:

And this is what I’d be doing if I used the newer API call:

I had to look up the formula for working this out without colouring in little boxes. With a little tweaking (to prevent the diagonals from creeping back in), here it is:

((n-1)^2)+n-1)/2

So — for a list of congress people (159 on twitter as at Tuesday July 14, 2009) that’d be ((156-1)^2-1+156)/2 = 12,090 API calls. Which is still a lot and will require some careful throttling, but (literally) not half as many as the 156^2 = 24,336 API calls that I’d need to run it as the script currently stands.

So – back to the drawing board for a while. I really can’t work out a programmatic way of doing this. Hmph.

A couple of weeks ago, I asked a bunch of people to send me their OPML files (for those of you who aren’t aware, an OPML file is what tells your RSS reader what feeds you’ve subscribed to — it can act as a way of moving your subscriptions between readers.) Some of the more trusting among them agreed, and that gave me the raw material for the first bit of my experiment.

Some red herrings

Along the way I uncovered a couple of things that were interesting but not (entirely) relevant to the experiment.

1. Some people are cagey about sharing their list of feeds: whether they consider it intellectual property, or whether they think that it may be too revealing, I don’t know.
2. Lots of people said things like “oh — my RSS reader? Haven’t looked at that in a while. I get all my news off Twitter these days.”

So what’s the experiment about?

You’ll probably know that I’m interested in networks of people and how information flows through those networks. I’m also interested in things like influence and whether and how we can identify and track its effects.

I’d heard about a word of mouth marketing campaign which was set up along the following lines: a sample set of pupils at a given school were asked “who’s the coolest kid in the school?” Clearly some names came up more often than others, whereupon the researchers went to those kids and asked them the same thing. By the end of the process, they had a good idea of who might be the key influencers.

This seemed like a good sort of thing to be doing. It was a simple idea, and apparently easy to execute. Everything else I was working on (the citation analysis and the network analysis in particular) seemed to be complementary.

A little history

So last year we did a version of this experiment for a client. We emailed and phoned a whole load of journalists and analysts whose beats covered our client’s interests and asked them:

Who do you read on a regular basis?

Who (other than you) should we be talking to?

If you were looking for information, where would you start?

Then we approached the people that they’d recommended and asked them the same questions. The purpose (as I’m sure you’ll have guessed) was to create a network map of who was whose go-to guys and girls. We’d take the data that we collected, push it through the usual processes and hey presto! we’d know who was really influential.

But what we actually found was that the journalists and analysts we asked seemed not to have any specialist sources. The people whom they cited were (in no particular order) their colleagues, the companies they covered (and their public relations representatives and agencies). Oh, and Google. Google was cited by everyone.

There were two obvious explanations for this:

1. It’s the truth: they really didn’t have any better sources. We’d all read about the collapse of journalistic standards; perhaps we were encountering it at first hand?
2. The privacy and intellectual property argument: maybe the journalists and analysts were constitutionally loathe to reveal their sources?

All my training and experience, however, points to the following reason:

3. Poor questionnaire design led to an inability to think (when put on the spot) of names and sources whom they believed were their influencers.

This is less unlikely than it may seem. If I were to ask you who your big influencers were, would you be able to answer?

All in all, it was a deeply unsatisfying exercise. We had envisioned what wild success would look like and this wasn’t even close.

Where we are today

I thought that it might be easier to ask people for their OPML files than it had been to ask them who were their influences. This is the equivalent of — say — asking to see a musician’s CD collection, instead of asking them about their musical influences. It’s not necessarily more accurate, but it might help expose a different picture.

Seven of my friends, colleagues and acquaintances sent me their OPML files, and that was enough to get started.

Early results

Between them, the first seven people subscribed to just over 1.5K RSS feeds, which gave me a lot of data to process. Here’s a picture of the network before I started processing the data.

You can see (I think) pretty clearly that there are several blogs (or RSS feeds — I’m using them interchangeably here) in the middle of the map that are linked to by several of the respondents. And there are some (around the edges) that are linked to by a couple of respondents. And there are lots that are linked to by only one respondent.

At this stage we’re interested in “indegree” — or “the number of OPML files in which we find RSS feed X.” Clearly the great majority (just over 1.3K) of the RSS feeds are single hits — that is, they appear in only one respondent’s OPML file. But that still leaves 180 RSS feeds with multiple hits:

Those 180 RSS feeds with multiple hits left us with a nice (and fairly predictable) distribution that looks like this:

Clearly the first-generation results aren’t very meaningful or accurate. Most often cited were author Seth Godin, ex-blogger-turned-lifestreamer Steve Rubel, and PR blogger Stephen Davies who were each read by five of the seven respondents. But more than half the respondents read my blog. Despite what I’d like to think, this is clearly an artefact thrown up by the sampling frame (my friends, colleagues and acquaintances).

Where next?

I’m hoping that we can extend this out a generation — and then keep iterating. I’m going to approach everyone with an indegree of 3 or above, and see how many of them will send me their OPML files.

I don’t hold out much hope of getting OPML files from people like Messrs Godin and Rubel, but if I don’t ask, I’ll never know, will I?

The current approach to WOM is to try to stimulate positive WOM while addressing or countering negative WOM. A sort of “accentuate the positive, eliminate the negative and don’t mess with Mr In-Between” strategy.

But what if we could do it a different way?

This idea stems from a conversation I had back in February with Martin Kelly and Andy Cocker of Infectious Media. Since that time I’ve chatted it through a couple of times with various interesting people. It’s not properly thought through yet, but following a chat a couple of weeks ago with Ketchum London’s new Head of Digital, the excellent Fernando Rizo, I’ve decided to put the idea out into the public domain to gauge what (if any) interest there is and whether I should continue to work on it.

“Word of Mouth” is hard to do well

I’ve read lots of word of mouth marketing case studies (there’s a great list over at WOMMA) and it strikes me that WOM is hard to do well for a few reasons. I don’t want to go into these in too much detail, but here are a couple of the structural issues:

1. Unless I’m a journalist, an A-list blogger or media personality or have some kind of platform, I probably have a very low reach.

   Despite everything pointing towards personal contact being the best impetus for positive word of mouth, most word of mouth campaigns compensate for my low reach by trying to get me to self-service my relationship with the brand and the campaign.

2. “Viral” distribution just doesn’t work the way most people seem to think it does; and this is particularly true when it comes to WOM.

   While I’m quite likely to tell stories about my personal experience of a brand and fairly likely to tell stories that involve a mutual friend, I’m much less likely to tell stories about other friends’ experience, and not likely at all to tell stories about friends-of-friends.

   Furthermore because of the ‘clumpiness’ of most people’s social graphs, geometric progression (the “I tell two people and they each tell two people and so on” effect) just doesn’t happen.

Homophily

One of the many reasons that WOM works is a thing called homophily — which roughly translates to “birds of a feather flock together”, or “you can tell a man by the company he keeps.”

I’ve written about examples of this before: for example, my analyses of twittering US Congresspersons and Westminster MPs which showed that one can predict with some reasonable degree of accuracy the political colouration of any given twitter account based on their mutual friends and follows (if you want to know more about the methodology, it’s worth reading Robert Hanneman’s chapter on cliques and subgroups.)

But there’s another side to the homophily coin; the social pressure to conform to the group’s norms.

Why I bought an iPhone

Let’s take a concrete example of this: it often seems to me that everyone I know has an iPhone. I made a conscious decision a few years ago not to buy an iPhone, but I’ve finally succumbed to the pressure.

While it’s true that several people have take time out of their busy schedules to tell me exactly how good the iPhone really is, I’m far more affected by what I perceive as the omnipresence of the iPhone. Everyone, it sometimes seems, has one except me.

Of course, on those occasions when I head down to visit my family in rural Hampshire, I’m reminded of the obvious truth: most people don’t have an iPhone. But that’s not the way the world appears to me.

How about turning it on its head?

What if we could identify people who are under social pressure to buy our products — who are being influenced by what their friends are doing and saying?

Let’s say for example that Apple and O2 (iPhone’s carrier partner in the UK) could work out that a significantly higher proportion than average of my Facebook friends access the service using the iPhone client, and could target me with special offers and rates to push me over the edge.

Instead of looking for WOM influencers, why don’t we look for areas of high potential — and target those people who are likely to be receiving lots of WOM stimuli?

Here’s an interesting ethical question: is it OK to ask employees to share company and client news through their personal social networks?

Here’s a hypothetical example. An agency has just launched a new ad campaign and posted the TV spot on YouTube. Is it OK to send an all-hands email asking people to share the link on Twitter and Facebook?

Let’s take it a little further. Is it OK to ask them to sign into YouTube using their personal accounts, and rate the video? It seems harmless enough, doesn’t it? You’re not telling them how they should rate it, after all.

But what if you asked them to leave comments? Any normal agency or client side social media policy will tell them that they have to disclose their relationship with the makers of the video. And you wouldn’t really want a whole bunch of comments that start “Hi, I work for the agency that made this ad and I think it’s really great,” would you? What makes the two things different?

Digging for victory

OK. Another hypothetical example. The social news site Digg is a huge source of traffic for most news websites. The Telegraph, for example, gets around 75K visits a day from the service. That’s an awful lot of traffic.

The thing is, with Digg, you really want to make the front page if you want the big traffic. Around 10K stories are submitted by users to Digg every day, and only 150 or so make it to the front page. So we’d need to be smart.

Digg is less open to being gamed than it used to be, but let’s say that a smart agency could still deconstruct Digg’s algorithm sufficiently that it can use its network of staff to improve the chances of a story (or review) making it to the front page that shows their client in a positive light.

All they’d need to do (say) is send an all-hands email that mobilised your staff to digg a particular story at the right time of day. Would this be legitimate? Digg clearly thinks not, but are they the best judge?

Ballot stuffing

Let’s say a client gets nominated for a Webby or one of the other user-voted awards out there. It’s common and acceptable practice for web services to use any means at their disposal to beg for votes.

So is it OK to send an all-hands email asking your staff to register and vote? Is it OK to ask them to use their Facebook and Twitter accounts to ask their friends and followers to vote?

Here’s what I think

I think that this is an ethical minefield, but I’ve got a couple of clear points of view that are up for discussion. First off, and from a purely business perspective:

If it’s valuable then clients should pay us to do it. If it’s not valuable we shouldn’t do it.

Ignore the ethics. When was the last time you sent an email that said, “Please share this with your friends and use billing code xxx.xx when you record it on your time sheet”? Of course, it only takes a few seconds to relay a message (fewer if you simply copy and paste the message from the all-hands email to your social media presences). What the hell – it’s all just part of the team spirit, isn’t it?

But if this service is of any value at all to the client, then we’ll see the demands on our time beginning to increase. Soon we’ll find ourselves doing several a day. Larger agencies with more staff will offer a more valuable service to clients than boutique agencies (“We have a thousand trained staff with Twitter and Facebook accounts primed to promote your campaign!”)

We could even work out some kind of ratcheted scale that said that people with more Twitter followers could bill at a higher rate; or we might start looking at unduplicated reach.

This might seem fanciful, but stories like the uSocial’s offer to game Digg’s front page for around $200 tell us that the thriving black hat market for this sort of thing is getting a little greyer.

If clients value this, then they should pay us. If we give it away as a “value added” service, then are we sure that we’ve communicated this properly so that the client understands the value we added?

How can we be certain we aren’t just undermining our digital value proposition?

Now, here’s the tricky bit where I bring ethics back into it. If we’re paying our staff to relay messages to their networks on behalf of our clients, what makes this different from spam?

Employees personal networks are their personal property

I’m one of those people who believes that the boundaries are blurring between our personal, public and professional lives. My colleague Chris Nee has posted about the presentation of self in social media, expressing a lot of how I feel rather better than I could myself, although that hasn’t stopped me from covering this elsewhere — notably in a discussion about our social media policy where I said:

I think that a good PR person is someone who manages their relationships well; who can tread the fine line between doing good work for their clients without abusing or exploiting their relationships. Who recognizes the value of their personal network, and their personal brand.

Do we have a right to ask our employees to use their personal networks on behalf of our business? I think the answer is – of course we do. We pay experienced staff more partly because of the social capital that they’ve managed to accrue in their address books. When you leave your job to go to a new agency, you’ll take your contacts with you along with all the shared experience, the favours you’ve done, and the favours you owe.

So if part of the reason we’re paying them more is because of that network, then we clearly value their network.

So why on earth would we encourage our staff to spam their personal networks?

I trust my peers. I don’t trust spam monkeys

Part of the reason for the success of social networking tools like Facebook and Twitter is that I can choose who I follow and who I don’t. I can restrict conversations to a bunch of people I trust and respect. Sure, I’ve got lots of Twitter followers with names like “Sophie1982″ and “EdelmanHR” but that just means that they hear my inconsequential blatherings, not that I have to hear theirs!

And I follow a lot of my colleagues’ Twitter streams. I’m pleased to say that these are — on the whole — full of meaty chunky content and devoid of spam.

But if we increase the spam content, what will happen? Here’s what (in the absence of evidence) I believe: their follow rates, retweet rates and mention rates will all begin to drop off. From your experience, what do you think?

Where does this leave us?

I’ve put a somewhat one-sided argument here. I’m sure I’m open to all sorts of rebuttals like, “surely, if the content is interesting/useful/entertaining then our staff will only be adding value to their networks?” and “but surely it’s up to them whether they want to pass it on to their friends?” I’ll wait for these to roll in before I start trying to address them.

But here’s what I think. I think that we’re trying to teach our colleagues to learn from managing their personal communities in order better to manage our clients’ communities. Anything that teaches them to prostitute their networks is a retrograde move.

Am I wrong?

On Monday evening I was in Vienna. Thanks to our partner agency IKP Porter Novelli I had the opportunity to talk to some of the best and brightest businesses in Austria.

The presentation was given in a fabulous private salon (Austrians seem to be keen on the “private” thing — perhaps because it helps them get around the EU smoking ban) which promised “Guten Wein mit Wirtschaf, Politik & Kultur.” I don’t know where my presentation fit in.

As we were going up the stairs I saw signs that my presentation had been advertised under the title “Facebook, Twitter & Co”, so I carefully changed the title of the presentation accordingly. The bits behind the title page never really changed.

If you’ve read the Integration Triagle presentation post, then the last third of what follows will be familiar and I suggest that you stop reading when you see Arnold Schwarzenegger for the second time.

I really need to credit Paul Mead, MD of VCCP Search for the meat of the first third of the presentation: it’s pretty much a facsimile lift from an inspirational presentation I saw him give a few months ago that has changed the way I’m thinking about Social Media planning. Thanks, Paul!

After the presentation, the IKP Porter Novelli team took me out for drinks. The next day for breakfast, they made me ham and eggs. Then Franz Ramerstorfer took me to a “typical Viennese café” for coffee and Sacher Torte. This is Franz.

Franz is the Porter Novelli network’s “Digital Ambassador” in Austria, and leader of the Digital Taskforce out there, so this sets a new standard for Ambassador behaviour. I do hope the other ambassadors take notice. Thank you Franz and everyone at IKP for a great opportunity, and a great time!

These are the slides from a presentation I did this morning on the topic of the Integration Triangle. I’ve talked about this here before in the article “5 Straightforward Ways To Integrate Your Communication Activities” — this includes some quick case studies.

I created these slides to support the presentation I was giving: they aren’t the presentation itself. This means that while you’ll be able to have a good guess at what I was saying most of the time, there will be moments when my meaning is opaque.

There are 70 slides in the presentation, including the front and back cover. Nevertheless, I gave the presentation in under 25 minutes. To save you doing the maths, that averages out at around 3 slides every minute (actually, there was a 4 minute delay in the middle of the presentation — so it’s more like 3-and-a-half slides per minute.)

In fact, my slides fall into two categories — those on which I spend fewer than 5 seconds, and those on which I spend more than a minute. This is more an artistic decision than anything else — I think that lots of slides going past very quickly give an appearance of pace and energy (which I dearly need first thing in the morning), but can rapidly become exhausting to watch and hard to follow without the occasional pause for breath.

Even with 70 slides, there’s so much more that I can say about the “Integration Triangle” as a planning tool — but I was trying to keep this to a single simple message. I’m hoping that (whatever they thought about my presentation, and no matter whether they liked it or believed what I was saying) the audience will remember what it was that I was saying, and be able to tell a version of the story themselves.

There’s just so much that we can talk about when it comes to the whole Digital PR thing that it all becomes rather overwhelming. I’ve just got off the phone to a colleague in Vienna (where I’m speaking next week) who wants me to talk to his audience about “Facebook and Twitter and Blogs” (oh my!) And I’ve got 45 minutes to do this. Of course I can do it. But what on earth is the “one thing” I want them to remember?