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One of the startling things about living in the US is the chilled out approach people have to drink driving. For anyone coming from the UK or Ireland, it’s a bit of a shock to realize that people here are happy to drive home after 3 …4 …5 pints.

My generation in the UK grew up while our parents were going through a change in mindset. In the early 80s it was fairly standard to be a bit tipsy and drive home, but by the time I learned to drive, it wasn’t an option – you wouldn’t do it, you wouldn’t let your friends drink & drive, and it’s not accepted as relevant.

I had a look to see if the data supports this anecdotal evidence. Since the 80s, there has been a dramatic drop in the number of deaths caused by drink driving in both the US and the UK. But deaths per 100k in the US are still nearly three times those in the UK: 3.21 vs 0.36.

(See Chart 1 below for more data and sources).

So what’s made this mindshift take hold in the UK? How has the government managed to effect behavioural and cultural change?

UK campaign

The UK anti-drink-driving campaign began in 1964, with a jolly video, with a Saul Bass aesthetic, reminding you that after eight whiskies you’re twenty-five times more likely to have an accident. This is no-nonsense messaging: driving when drunk will cause an accident.

(Bonus – a Benedict Cumberbatch lookalike at 0.09)

It wasn’t until the early 80s that the ads became more hard-hitting. In combination with stronger enforcement (breathalyser tests became commonplace), in the 80s the messages focused on the impact being prosecuted would have on the driver. They’d lose their jobs, or their license.

By the 90s, the messaging shifted: moving to the impact your drink-driving actions would have on others. This third phase aimed to create the idea that to drive drunk was unacceptable in today’s society – you would make yourself a pariah. This video is a close-up on a little girl called Kathy, who is listening whilst her mum shouts at Kathy’s father for killing a girl whilst driving drunk.

“She heard a kid at school say you were a murderer.”

(Video directed by Tony Kay, who went on to direct American History X)

This approach is the key to the cultural shift that we experienced.

This message means that you expect yourself to behave in a certain way. And this gets reinforced by your peer group, who have also set these expectations for themselves.

US campaign

In the US, the blood alcohol limits are the same as in the UK – 0.8%. The punishment in California can be pretty stringent – first DUI can get you a bundle of punishment, including up to 6 months in prison. But there hasn’t been the same societal shift, so people still nod and wink at ‘one for the road’.

Some theories around why this is:

  • Messaging: This has been more focused on the direct effects of drink-driving; on possible death, or punishment. But not as focused around the ‘shame’ of being convicted of a DUI. There have been some hard-hitting videos featuring video footage of the victims, but the core premise since 1983 has been ‘Friends don’t let friends drive drunk’. Which hasn’t done the same job of creating a culture where it’s unacceptable to drink and drive.
  • A later start: The US campaign only really began in the early 80s, with the formation of MADD and the Ad Council getting involved in producing public service announcements.
  • Investment: across a massive country, it’s incredibly expensive to fund comms that will spread across a population 5 times the size of the UK. I’ve never seen any comms on this in the three years since I’ve been here.
  • Appetite for government messaging: the US have a much more laissez-faire approach to authorities ‘meddling’ – what’s fairly standard in the UK would be seen as very nanny state in the US.

Summary

Shifting mindsets is incredibly difficult. This success in the UK made me ponder not just whether it could be applied in the US, but how else we could use the power of societal expectations? We wouldn’t want to take tips from Septa Unella and her bell of shame… but there is an opportunity to look at using this to change opinions in some of the big schisms facing us today.

Appendix – The data

Chart 1

The topline comparison of the data I’ve referred to in the paragraph above:

The data looks at fatalities caused by drink-driving, as these figures are accessible online. Convictions due to drink-driving seem to be much more difficult to come by.

For a more detailed breakdown of how I calculated these figures, click here to download (excel spreadsheet).

This chart has been built from the following references:

  1. http://www.madd.org/drunk-driving/about/history.html
  2. https://www.theguardian.com/news/datablog/2013/aug/01/drink-drive-deaths-number-changed-over-time
  3. http://www.drinkdriving.org/drink_driving_statistics_uk.php
  4. http://data.worldbank.org/indicator/SP.POP.TOTL

Additional references used for the article:

  1. http://www.telegraph.co.uk/motoring/road-safety/11215676/50-years-of-drink-driving-campaigns.html
  2. https://www.gov.uk/government/news/92-of-people-feel-ashamed-to-drink-and-drive-as-50th-anniversary-think-campaign-is-launched
  3. http://www.bbc.co.uk/news/magazine-29894885
  4. http://dui.drivinglaws.org/california.php
  5. http://www.adcouncil.org/Our-Campaigns/Safety/Buzzed-Driving-Prevention

 

Since the advent of sci-fi, we’ve been trying to create artificial brains. We’ve been trying to understand our own for even longer. The clever folk looking at Deep Learning are attempting to replicate human thought processes, by building ‘neural nets’, pieces of software that are connected by layers of ‘neurons’ that can, in a manner of speaking, think. That is, look at data, and teach themselves to form an observation.

This can take the form of hearing sounds, and understanding what the noises mean – or voice recognition, like Siri and Google Now. Or looking at a bundle of pixels, and determining what this abstract group of colours could be – is it a bike? Or a jellyfish?

This website has a great demo tool, where you upload an image, which works well for simple imagery, like our jellyfish friend:

Deep Belief - Jellyfish

Obviously, narcissism being what it is, I wanted to find out what it thought I looked like:

Deep Belief - Me

61% wig, 35% feather boa. Pretty darned accurate summary I would say….

Of course, the use cases extend beyond my own wiggery. Jetpac is a city guide that analyses geo-located Instagram photos. It parses the info it reads to create top 10 lists, so blue sky and greenery mean a scenic hike; lots of lipstick and teeth = a bar where lots of women hang out; while lots of moustaches, apparently, indicate a hipster bar.

[It raises interesting questions for people whose pictures are being used. Although Instagram pics are publicly available, it’s still a bit of a shock to see your face appear on the feed in the guide.]

These techniques are all being used by the big boys – Apple, Google, Facebook are all innovating within this space, and developing their artificial intelligence expertise. With the huge amount of images and video going online every day, the winning tech companies will be those who can crunch all this data, and really understand what the data means. How will this impact on users & brands though?

  • As the systems refine, they will be able to personalise and to predict what users want to an ever more refined degree. Facebook’s newsfeed algorithm will know precisely what you want to read. Images will be auto-tagged, and then auto-shared, knowing who you want to share with.
  • By the time a user interacts with a brand, they have already pretty much decided whether they’re going to purchase. Advertising & social targeting relies on what a user has previously done, and serves out ads based on that. If you can begin to predict what users will do in the future, you have a much greater opportunity to really influence their behaviour.
  • Social listening tools will be more accurate and more in-depth. Right now, these tools can be fairly blunt instruments in trying to monitor sentiment around a brand. Most of them can only look at what people write, in text that computers can read. In the future, if these tools can automatically interpret images and video, brands will have a much sharper understanding of how they are perceived.
  • The winners in this field will be those with huge amounts of data to crunch and interpret. Those tools that are more ephemeral with data – ie, Snapchat – won’t be in a position to learn anything about their consumer.
  • With great power comes great responsibility. In January, Google bought Deep Mind, a leading Deep Learning startup, for $500m+. Facebook had also been looking at this purchase, but one of the reasons reported as to why they sold to Google instead, was that Google established an ethics board, which would oversee how this data will be used responsibly.

At the moment, researchers say that the ‘neural nets’ are comparable, in numbers of neurons, to an insect. There’s a long way to go before the prospect of Samantha in ‘Her’ – an autonomous, self-learning operating system, with its own life, and feelings – becomes a reality. But it is there, beckoning us into the AI future.

As my friend pointed out, my enthusiastic call as the Superbowl kicked off referred to the wrong sport – baseball, rather than American Football. A strong start.

It’s fair to say, I didn’t have the strongest grasp on the game itself, apart from that it takes 4 hours to play 1 hour worth of sport. I was really watching for the ads, which is not how I normally spend my Sunday afternoons, but I had chips, dips, and beer, so I was rrrrrready!

As someone who has watched the Superbowl ads from the UK over the last couple of years, I was expecting to be all shades of excited. But, it turns out, watching the ‘top 10’ lists is different to watching all 43 ads. Lots of them were incredibly mundane, particularly considering that a 30” spot cost as much as $4.5m.

It’s an odd case this. The concept of ‘the Big Game ad’ seems to be a classic case of channel-led campaigns: old-school, ‘Let’s make A TV Spot for the Superbowl’. But all of the companies here do at least seem to see it as an opportunity to begin a conversation; 58% were using hashtags, and lots of them released the ads before the Superbowl, to get some momentum behind them before kickoff. So, what you would hope, is that these ads are just part of a bigger brand story, rather than sitting in isolation now the Superbowl budget has gone out the door.

So you don’t have to watch all 43, these are my favorite spots…

1. Goldieblox
After being refused the use of the Beastie Boy’s ‘Girls’ in their last spot, this follows through, fizzing with energy and awesome spirit. High investment from the little startup brand.

 

2. Beats Music
Promoting the innovative tie-up with AT&T, Ellen shapes a modern fairytale to launch the curated music service. This carrier deal is groundbreaking for a music service, and this spot adds character & emotion to a phenomenal price-point, and introduces the concept of ‘The Sentence’ to a mainstream audience.

 

3. Microsoft – Empowering
The rather staid tech giant comes out with a beautiful sketch about ‘what tech does’ – to get across their empowering message. The sucker punch comes at the end, where – aw, just watch it you hard-hearted oaf.

 

4. Hyundai Genesis – Dad’s 6th Sense
Amongst the many, weighty, (and often dreary) automotive ads, this had wit, charm, and warmth, and was really nicely executed.

 

5. JC Penney
So this wasn’t a TV spot, but their social team gegging in on the action. Whilst this wasn’t the most elegant, or subtle, of the non-TV party crashers, they were effective at getting talked about. Following some fumbled tweets, Twitter collectively leapt to the assumption that a drunk intern was at the helm. Of course, it turned out they were just ‘wearing mittens’. Not ground-breaking, but for a minimal investment, they: “gained over 10,000 followers last night, received over 40,000 @jcpenney mentions and 1,800 mentions of our hashtag #tweetingwithmittens.” A better investment than the $4.5m on the TV spot?

Have been playing around with Snapchat recently. Not in a rude way, you understand.

Snapchat’s raison d’être is the selfie. I had thought this service was part of the inevitable progress of our narcism to onanistic levels. And yet, the fact that these messages are sent one-to-one, unlike the one-to-many services of FB and Twitter, means that actually this is a less showy, more genuine interaction. You’re not putting out a ‘personal brand image’ to 300+ people. And its ephemerality means that the content is more throwaway, less considered. So, between friends, it’s a more intimate and less self-conscious interaction.

But, as a brand using Snapchat, you don’t get the benefit of the multiplier effect you see with the more ‘traditional’ social platforms; it’s more of a messaging system than a social network. Campaigns that have been run on Snapchat so far look back to a one-to-one engagement. And there isn’t a halo, or amplification effect, from these conversations.

What is helpful for brands: the new ‘Stories’ functionality gives an opportunity for brands to tell more of a story, as it groups together snaps for up to 24 hours (effectively like a Storify). And the action required to view a post (you have to hold down the ‘view’ button) means any engagement is an active, conscious decision. But, they’re not providing any stats that allow a company to track beyond a post being viewed or not, which makes it tricky to know what’s actually working.

The tech-startup theory works that first you create your user base, and then you can establish your business model. They’ve nailed the user base already (30m+ active users). They’ve rejected two buy-outs, for $3bn and $4bn. So do they have their business model planned out yet? The ephemerality of the messages means they’re not retaining user data, so an ad targeting model such as Facebook or Twitter’s doesn’t work here. What do they have up their sleeve in terms of a business model that means they can reject those mahussive buyout offers?

  • In-app licensing – getting users to pay for additional services. Or, as WhatsApp does, with their enormous 400m active monthly users, micro-charge users after the first year of free use. Reel ’em in, then they’ll be happy with a 99c payment.
  • Native advertising – getting brands to pay to get content to the right people… how will they know their audience?
  • In-app paymenrs for brands – Twitter are looking at instant, in-stream payments using a startup called Stripe
  • Getting brands to pay for access to analytics
  • Getting porn sites to pay for advertising based on the number of flesh coloured pixels on the screen…

What a town. Having spent years totally, utterly, unfussed about going, once it was on the cards I was obsessed. We spent two nights at the Golden Nugget. This place has a sharkpool, that sits within the actual pool, and then a waterslide that runs through the sharkpool*.

I’d heard of ‘The Strip’. I hadn’t quite understood what this was. A line-up of insane structures and bonkers flights of imagination.

Generally, tourism marketing bodies focus on the authentic aspects of local culture – ‘come here and meet the locals! Eat what they eat! Uncover the ‘real’ heart of this place!’ – but here, it’s tourism at its most constructed. The only thing that prevents the developers from indulging their inner 8-year-old is the money. And if there’s one thing that Vegas is not short of, it’s money. So you get rollercoasters, and flying women, and indoor gondolas, and sinking pirate ships. You get to drink 16oz of tooth-shrinkingly sweet margaritas from an Eiffel-tower shaped glass, while looking at $34m worth of Jeff Koons’ Tulips.

It turns out I am not a born gambler: $31 spent in 5 minutes, then panic at the sheer waste. Hedonism to make Hunter S Thompson blanch.

* This is an idea my boyfriend had when he was 8 years old. The fact that this is now reality is quite insane.

It takes a little while to march up the inclines, and a few wrong stairwells, but when you get to the top, it’s just amazing. Rolling round from the Pacific, to the Golden Gate Bridge, the Bay Bridge and its dinky little Treasure Island, all the way down the Bay, and whatever scenic little lumps of fog want to add to the scene.

What with all the hills, everywhere, San Francisco regularly gives good view. But a 40 minute trek to the top of Twin Peaks gives a glorious 360 of practically everything that makes the city so ace.

San Francisco is ridiculously pedestrian-friendly. It does mean the traffic crawls along, but everybody seems cool with that.

If there aren’t any lights, then you’ve got right of way. It’s like BEING A KING.

The public transport system is crazy-complicated. None of the maps make any sense. There’s a card that’s sort of like an Oyster card, called the Clipper card. But you can’t buy that in any of the stations – why would you want to do that, you crazy cat!? You have to go to a pharmacy to buy your transport ticket. Of course.

That said. San Francisco = hills. Unless you want to get cabs everywhere, you need to know how to schlep across town. While there are always a couple of crazies (hello, the guy smoking crack beside me at the bus stop), everyone else is super-helpful.

  • BART – a train that rattles round the bay area and to the airport.
  • Muni – a tram that also goes underground. I actually refused to believe this when I was told about this, but Tom is right. It does.
  • Streetcar – beeeeyootiful old trams that run along the flat streets, from the piers down through Market.
  • Cable car – the classic icon. Can’t believe you’re still allowed to hang out the sides of these. They’re ace, but expensive. TOPTIP: you don’t have to wait down the bottom of Powell St either – you can pick them up anywhere along the route.

Hello everybody.

I’ve just moved to San Francisco and will be writing here on some things that interest me. San Francisco itself, for one – I’m loving getting to know the city, and the whole area. Digital stuff too; where tech and marketing meet to give us ways to tell interesting stories. And anything else that warrants talking about.

Say hello!