Most of my election effort will be trying to elect Lib Dems. But if you are non-party political and want to influence the election, this post is for you*.
Recognise that seats matter more than votes
In the UK political system seats are all that matter in practice. You probably can’t remember what percentage of the vote Labour got in 2005. But you can almost certainly remember who was in power after the 2005 election.
So anything you want to achieve needs to influence how many MPs get elected who agree with you.
Not all seats matter the same in the election
Britain’s electoral system means that most seats stay with the same party time after time.
Roughly 200 seats, out of 650, really matter.
Not all votes are the same
Point 2 means that the only votes that really matter are in the marginal 200 seats.
But roughly 50% of voters always stick with the same party. Leaving only 50% of people worth trying to influence, in the 200 constituencies you are targeting.
Not everyone votes
Voting is voluntary in Britain.
Even in a really high turnout election, only about 70% of people will vote.
So you need to target a specific type of person
Add together points 1, 2, 3 and 4 and it means that you need to target swing voters in swing seats who might realistically vote.
Who are these people?
They are a varied bunch. But we can look at what is most typical of them.
Almost every bit of research I’ve seen says that this group is concentrated:
Living in the suburbs of Britain’s cities and towns. This is where the swing constituencies are. Think of suburban Leamington Spa or Derby.
Middle aged. Young voters tend to have very low turnout (and there aren’t enough of them). Old voters tend not to change their minds, after a lifetime of voting a particular way.
Not very interested in politics. In fact giving very little attention to politics day to day. This group typically make up their minds during the election campaign, often on election day. A surprisingly large number make up their minds in the voting booth.
If you want to make an impact consider if you are going be relevant to this group of voters.
If not what are you going to change?
* Psephologists will notice some significant simplifications here. For everyone else – if you seriously doubt any of these points, drop me a line and I’ll dig out sourcing.
Election 2017 will be a test of both messages and digital campaigning for Britain’s political parties.
The big battle in the campaign will be to frame the election choice. There are two possible frames.
Frame 1: Leadership
In this frame public debate focuses on who would be best Prime Minister. This is probably the campaign Theresa May wants.
Theresa May will do well in this scenario. The public know her, and, largely, trust her. Jeremy Corbyn will do disastrously. Many members of the public don’t have a fixed opinion about Jeremy Corbyn. This will change after 7 weeks of Jeremy Corbyn being constantly linked to terrorism. Labour could do extremely badly.
I’m biased, but in this scenario Tim Farron has the opportunity to break through as a new voice. Tim is a clear communicator and funny. From an normal background in the north of England he’s as far as you can be from a typical member of the political class. However he’ll face the challenge of being seen as a credible Prime Minister, leading a party with just 9 MPs.
Frame 2: Brexit
In this frame the public focuses on Brexit for the length of the campaign.
Theresa May will face an opportunity to pick up a lot of pro-Brexit votes from Labour and UKIP. But she’ll also face two risks.
Firstly the Brexit focus might help UKIP. Secondly moderate Conservatives may defect to the Lib Dems in England, and the nationalists in Scotland and Wales.
Again Labour look set to do extremely badly in this scenario – losing a large proportion of their votes to either Lib Dems or Labour or nationalists.
Again I’m biased, but there’s a big Lib Dem opportunity here.
The digital election
A simple way to think about the impact of digital on the election is to consider reach and impact.
The Conservatives will have a significant advantage. They will be able to buy huge reach on Facebook and YouTube, as they did in the 2015 General Election.
Labour have a secret weapon though. Back in 2015 they had around 6 million email addresses on their database. And, whatever you think of Jeremy Corbyn, he gets a lot of free reach on social media. While they have been relatively uninventive with their email, they have a significant, free, reach to the public. And you can assume they will raise several million pounds this way.
However reading accounts of the 2015 Conservative and Labour campaigns, the Brexit campaigns and Trump’s election here are three areas to watch:
Impact on marginal constituencies: How much advertising is seen by voters in marginal constituencies? This should be reasonably easy to test through polling, as Lord Ashcroft did, crudely, in the run up to May 2015.
People often forget about Facebook Groups. They’re low profile, and fairly simple. But they have over 1 billion users. Anecdotally it seems they are influential over everything from private business discussions to the hottest parties in Los Angeles.
This makes Bots for Facebook groups interesting. It will be possible to engage in a Group without being allowed in. For instance BT Sport might create a Bot with sports results for private football groups. Or a software company might create a news bot for a specialist set of customers.
On that note, it turns out the most effective ad of the Trump campaign used an old video of Michelle Obama attacking Hillary Clinton. It’s routine for political campaigns to follow their opponents everywhere, hoping to catch them off-guard. I wonder how many corporates have considered this?
There are really two ways to fix a problem like this.
Option 1: Whitelist appropriate YouTube channels
The first way is to whitelist YouTube channels that advertising appears against. For instance you get the most popular 100,000 YouTube channels and put them through a verification process that weeds out inappropriate content. Human tagging, combined with machine learning (in the literal sense), would make this expensive but not impossible.
However the, say, 1.9m channels which would not get verified are 95% of the channels, but likely only, say, 20% of the views. So YouTube would need to give up that ad inventory. Or it could sell it to less squeamish advertisers at a lower rate.
Option 2: Find inappropriate YouTube videos
The second option is messier but less costly for YouTube. It relies on the fact that people who put up ISIS videos want them to be seen. So YouTube can put human review in for any video that:
Isn’t from a known safe channel or user.
Has any tag or keyword that is risky (e.g. ‘Iraq’).
Has more than, say, 400 views (And so is likely to have any meaningful impact)
Is gaining views quickly.
Add these together and the number of videos to review drops dramatically. Get your reviewing mechanism to learn from human judgement, and you should, fairly quickly, be able to handle the problem.
YouTube/Google have some of the world’s best machine learning experts, huge budgets and good reasons to move quickly. They can fix this.
Technology companies constantly hype their own processes and products. Often this hype is technically truthful, but misleading. But hype takes on a life of its own, misunderstood or exaggerated. Eventually hype leads to absurd claims. These cause real damage.
To protect yourself look at three laws of marketing hype.
Law one: Impossibility – The technology doesn’t exist or is impractical
Impossible claims are at the centre of technology hype. Or the hype focuses on things that might be technically possible, but impractical.
Mass scraping of data is usually impossible
First consider a Guardian claim about Cambridge Analytica’s work for Donald Trump during the 2016 election.
The article claims that Cambridge Analytica harvested data from people’s Facebook profiles.
Here’s the problem though: This is almost impossible. The vast majority of people’s Facebook data is private. This data can only be seen by their friends or if they choose to reveal it to a company.
Of course you could try and scam your way to people’s data. You could create thousands of fake Facebook profiles. Then try to befriend all 128m American voters. Then copy their data. While this is possible on a small scale, Facebook is generally good at weeding out large scale scams like this.
No, Facebook doesn’t know more about you than your partner
A related example is almost any article that claims that companies know huge amounts about you. Yes, there is a lot of data, but it’s not always meaningful.
To test this visit Amazon. Then ask yourself how good their recommendations are. They’re probably ok – but not amazing. Amazon have more data to make recommendations about what to buy than virtually anyone else. Yet they struggle to get it right. To complete your personal test visit Google, Facebook and Apple’s websites. Have a look at how well personalised they are.
Why is this?
The vast majority of data is either private, anonymised or just not very useful. If you want a guide to what’s possible then have a look at the world’s best companies at selling data. Simply create an advertising account on Google. Or look at the business sections of Facebook, Amazon, Experian and Axciom.
Impracticality is closely linked to impossibility.
AI is currently going through a bubble that is often based on impracticality. Read reports on autonomous cars and you’d think every taxi driver in the world was about to lose their job. Then look at Uber’s current testing, which recently leaked, and you’ll find that humans had to take over the driving every 0.8 miles on average. That’s a long way from a technology that is going to satisfy regulators and insurance companies.
Illegal is also impossible
A final angle on impossibility is the law. Is the technology legal? And does it comply with relevant social network policies? Data protection laws, and discrimination laws, might simply make it illegal. And if a company scrapes millions of people’s data from Facebook they can expect to be sued.
Law two: Value – New technology isn’t worth the effort
New technology might be practical and legal. But it often isn’t worth the effort compared to easier and cheaper approaches.
There are lots of simple ways to improve digital campaigns, because they are simple to test. It’s common for testing to increase the effectiveness of a campaign by 10-20%, quickly and at low cost. So the second law is that technology may not be a better use of your time than simpler alternatives.
Another way to look at this law is to ask if you have exhausted your current testing programme. Have you got the right strategic choices? Are you using the right channels, for the right purposes? Have you optimised your customer journey, from acquisition to conversion? Is your creative optimised?
Even tech companies don’t always use cutting edge technologies
I recently reviewed the digital marketing programmes of five of the world’s leading marketing automation providers. If anyone is going to be using cutting edge technology, it’s this group. Yet all of them fail on at least one basic measure. Several have very poor quality email programmes, with virtually no personalisation. Some of them don’t re-market to people who visit their website. Their websites are generic even when they know something about you.
Why is this? Some of it is inertia. But it also reflects a judgement that new technology isn’t worth the effort of implementing it.
Apply the second law to claims that people can predict the future (e.g. the X-Factor). You could try through social listening but that’s hard to get right. It’s easier, and probably cheaper, to commission a traditional poll.
Law three: Reach – Technology doesn’t reach enough people to work
A valuable technology has to reach, and influence, enough people to justify its cost. Reach comes down to two things:
Penetration – What proportion of the target audience encounter your technology?
Time use – How long do your audience spend with the technology?
The problem with anything new is that not many people use it. By definition. So there needs to be a route for it to penetrate its market.
This can sometimes happen organically. But most of the time this needs time and marketing.
Apply the third law to VR discussion and something becomes clear. Most VR won’t get to enough people to make a difference. Why? Because there’s no distribution system for it, unless it’s through Google, Apple, Facebook or Amazon.
What is the most hyped technology?
Look at the three laws and what areas of technology look overhyped currently?
Not enough reach, and not much evidence of impact in many cases.
AI that’s implied to be fully automated. If you re-cast these claims as ‘software tools making people smarter’ then there are plenty of sensible cases. But implying that you have an artificial intelligence, when you just have programmatic media buying, you’re pulling a con.
Almost all discussions of Social media that don’t mention reach.
‘Data science’ that claims to know people better than they know themselves.
 There’s also a claim that Cambridge Analytica used ‘machine learning to “spread” through their networks’. This is a confused claim, that might charitably be read to mean that Cambridge Analytica used machine learning to understand what content was most liable to be shared on social media.
Beyond Twitter, and the odd private Facebook group, I find that almost a lot of the most useful things I hear come from email lists. So I’ve gone through my favourite lists to find my top four*.
Matt Muir’s weekly emails are an amusing mixture of rants, music and technology. But what makes them really stand out is his list, every week, of major social network changes. The number of Facebook announcements weekly alone is boggling – add in the other social networks and it’s astounding Matt keeps on top of them. But he does – and we all benefit.
Benedict Evans is about as knowledgeable as you get on the mobile industry – his collection of blogposts / podcasts, and links provides critical analysis of what’s going on – based on solid research and thinking, with a good dose of scepticism.
Azeem Azhar’s weekly round up of cutting edge technologies, especially focused on AI, is consistently fascinating. On top of a day job, being an FT columnist, he somehow has time to organise what are meant to be (I’ve never be able to go to one) excellent dinners with leading thinkers.
James is a former colleague and mate. But he’s also be writing this superb mix of geekery for a few years – rightly making him one of the most respected people in the UK Social industry. I have really no interest at all in half of his stuff – that’s about superheroes and stuff like that. But the other half mixes feminism, social media developments and technology rather brilliantly.
“The value of the top 10 corporations was $285tn (£215tn), beating the $280tn worth of the bottom 180 countries, which include Ireland, Indonesia, Israel, Colombia, Greece, South Africa, Iraq and Vietnam.”
Even, usually sensible, commentators like Scott Galloway use this comparison.
It’s a simple enough story. It goes something like this:
Trump’s data agency, Cambridge Analytica, gathered 5,000 data points on everyone. They used this to psychologically profile people, and deliver highly personalised advertising online. This exploited your character, fears and interests. And this swung the election for Trump.
Dig under the skin and this story has a few flaws. Using Cambridge Analytica’s own data, we can see that it probably didn’t swing the election.
To understand why, we need to kill a myth. Which is that Trump’s campaign knew how individuals behave and think in intimate detail.
This requires Trump’s campaign to have abundant data on millions of voters.