AI and machine learning:

• 15 million UK jobs could be automated
• But our fear of ‘robots’ isn’t likely to materialise in the way we imagine, it’s happening around us e.g. McDonald’s food ordering module
• Machine learning architecture is multi-layered, the challenge is replicating the output that our eyes perceive by passing layered representations onto each stage
• Humans are able to take learning from one process and apply it to another
• Narrow AI we have made a lot of progress on, e.g. Siri
• Strong AI, e.g. Ex Machina, we’re not there yet
• The gap is being bridged at the moment by deep learning – the process of getting from narrow, task based AI, to the idea of consciousness and independent ‘thought’
• E.g. Google DeepMind
• There’s actually potential for strong AI to become better than humans, because it would have an infinite capacity to process and manipulate data
• Machines wouldn’t make the same mistake twice, or be influenced by irrelevant external factors, in the way that humans are vulnerable to
• The mapping between input and output can therefore become ‘perfect’
• This can solve all kinds of world problems; science, information, manufacturing, etc
• This can then become proactive, e.g. in content creation, because it now understands the ‘rules’
• Bees are extremely intelligent – their learning isn’t deep, they don’t need a lot of layers between seeing and processing in order to understand what they need to do to survive
• They can also adapt, taking learning from one task and applying it to another, pattern recognition etc
• Support Vector Machine Processing can help us understand input/output  ‘rules’
• Understanding how to speed up this process, e.g. taking examples from bees, would catalyse our progress in the AI field
• Brains sort input in amazing ways, e.g. the cocktail party problem – being able to tonally categorise sounds to differentiate the conversation you want to hear from the ones you don’t
• Smell is the same – we can separate the difference between the smell of coffee and the smell of bread, even though they we would perceive that in a bakery we are smelling both at the same time
• If we can make a computational simulation of a bee’s brain, we can use it to steer a drone, for example
• Simple brains like this are a good way to start as we progress towards strong AI
• As people interested in AI and its development, we have a role to play in making AI smarter – we need to understand intent, user patterns, the makeup and delivery of quality content

Insights:

•  There’s no correct way to build an analytics dashboard – decide what you need by what you want to achieve
• Have them updating in real time and be ready to respond as things change
• Create a universal analytics dashboard that includes data from all elements of the business, that way you can see how they all interact and make the most of opportunities
• Have someone in charge of all of it, who is empowered to make decisions and advocate for changes in direction when needed
• Use them in a gamified way to encourage improvements and optimisation internally – who can top the charts, which team can produce the best results
• Encourage a shared learning culture around this
• It’s worth paying for a dashboarding tool that does everything you need, because it enables this culture and saves money in the long-term
• Be prepared to fail a few times before you get it right
• Make your customer dashboard an internal product that everyone in the organisation can learn from
• Build it up iteratively, get a few things right and go from there
• There’s an element of increasing internal understanding of analytics, to drive the internal demand for acting on what’s possible
• Google Data Studio
• Data will move this way, because it’s more flexible than GA
• Make your data sharable in a way that makes sense to people who need it
• BigQuery
• “Data Studio will become the default digital marketing tool”
• Customers are complex – paths to conversion don’t go in a straight line
• 85bn calls are made to businesses every year
• Calls convert 20% better than clicks
• 1.7 trillion dollars per year is phone call influenced
• We need to be able to credit digital with these calls, that is often the end of a digital journey (e.g. calling an estate agent, you can’t add a house to your basket)
• A single customer view should include offline methods as well
• Can we get to a stage where we know what the customer is calling about before they call, because we know what they were looking at and what they’re likely to want to buy
• Speech analytics helps us to make marketing decisions on how products are described by customers
• Google predicts voice search will overtake text search by 2019

Agile and big brand strategy:

• Aim for what’s currently impossible
• Don’t take shortcuts, go for greatness
• We have to make search content as good as the best piece of content we can imagine
• We need to understand how to market our content better, without crossing the line into becoming a hack, or creating content for content’s sake
• We’re contributing to a greater good, we all have a responsibility to provide things that are valuable, meaningful and will change the world or make it a better place
• Will Google be able to measure value and contribution to the world in the future, as a quality metric, using ethics as a ranking signal
• Create hero pieces that define your brand, promote your values and help you stand out from competitors
• Google updates its algorithm 1.6 times a day
• Organisational culture – legacy systems, huge signoff processes, lots of stakeholders, institutionalised co-workers
• Only one third of businesses actually map their current position on any one issue before they start working to change things
• Customers don’t care about your backend systems, loyalties and products
• Tools are only ever as good as the talented people who are using it
• Educating stakeholders and gaining trust
• You need to be credible, reliable, understanding and motivating
• When you strip everything away, there is only one goal, and that’s the boilerplate of your brand
• The people you win over get you access to people beyond them
• Create a ‘single point of truth’
• Organic strategy ‘you can’t buy love’
• Loreal have the challenge of tracking customers who convert on supplier sites like Boots
• Transformation is a continual process
• Know the talent you have around you
• You can judge brand perception from search, e.g. ‘what have I done to break my Mac programme?’ Versus ‘My PC isn’t working’

Paid social:

• Facebook has 1.4 billion active users a month, that’s 79% of all internet users
• 500,000 new profiles created every day
• Compared to Google, its ad platform is fairly new
• It grew 50% from 2015 – 2016
• Paid social is very different to organic, he recommends expertise from people who specialise in doing that on its own
• Set things up as campaigns in their own right, don’t rely on boosting a post
• Make the most of lead generation forms, retargeting the people who started and didn’t complete
• Use Canvas and Collections
• AdEspresso is a bank of ad examples
• Think about advertising as a story, you can’t build a relationship by asking people to buy something before they know who you are
• Sequential ads help with ad fatigue, there’s a higher conversion on the third ad
• You earn the right to ask them to buy, you need to earn that within the paid social journey
• Think about hooks, e.g. before a new campaign
• Think about the whole package, the sequences should run at the same time as your email marketing, organic efforts, etc
• Make the most of engagement audiences
• According to Bing, the CPC on ‘lawyers’ is $109!
• Don’t do the same work twice – use your AdWords data to plug into your Facebook ads
• Information charities have the advantage of being able to produce thought leadership content

Keynote:

• We don’t live in a world of perfect information and perfect trust
• The quest for optimisation is difficult in that world
• If you become exceptionally good at something, you’re probably sacrificing something else
• You need disobedience and unpredictability to keep you adaptable to inevitable change
• Nature never purely optimises along one dimension
• Never stop doing the left field things, because every now and then you hit on something better
• If you try to sell something in the wrong context, you fail, however ostensibly ‘perfect’ the formula
• There is no objective reality
• In marketing, you have the choice to solve either the reality, or the perception
• E.g. TFL – they couldn’t change waiting times, so they changed the perception of waiting, by introducing arrival time updates. People don’t mind waiting if they know how long they’re waiting for
• Get people to focus on something else, if you can’t change the reality
• Wagamama introduce you to the idea of random food arrival by asking whether you’ve eaten there before. Everything still arrives in a funny order, but we’re ok with it because they’ve sold us on why
• “The trouble with market research is that people don’t think how they feel, they don’t say what they think and they don’t do what they say”
• Delayed prescriptions are post-dated so people don’t cash them in because they’re generally better before they need them (e.g. solving the antibiotic problem)
• It’s easier to get fired for being illogical than unimaginative
• Our brains are generating a plausible narrative from everything we perceive
• McDonald’s is reliably ‘good enough’ which prevents us from wading into something that could be surprisingly bad and is therefore the biggest food company in the world
• We’ll accept a lower average for the trade-off of a lower variable, it’s not optimal, but it’s not catastrophic
• This is why we end up doing things ‘the same old way’
• This is why big agencies can be uninspiring – they don’t fail, but neither are they likely to do anything amazing. They’re ‘safe’ and if they mess things up, it’s on them, not you
• Test counter-intuitive things, because your competitors won’t