A Blagger's Guide to Artificial Intelligence

White Paper

Artificial Intelligence in some form or another, is already deeply ingrained in marketing and commerce. Everything from product recommendations to search engine results, personal assistants to fraud detection make extensive use of AI, and that’s just the tip of the iceberg. The marketing world is abuzz with talk of AI and one of its offshoots, Machine Learning. But these terms strike fear into the heart of many a marketer. Are they just the latest fad? Do I actually need to understand this stuff? Even if I do, can AI really be applied within my business? What does it even do?! These are some of the questions we’ll answer in this paper. We’ll also look at the basics of the technology, explain the jargon and generally arm you with everything you need to start putting AI to work in your organisation.

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Defining Terms

As with most new technology, AI comes with a bundle of new terms to learn. With AI terminology, one of the key things to remember is that there’s a tremendous amount of overlap between terms - even experts debate the exact boundaries. Throughout the report, we’ll be using ‘AI’ as a catch-all term, but some of the more important words and phrases include;

  • Artificial Intelligence - AI is an umbrella term for the field of computer science which attempts to emulate the cognitive functions of the human mind, such as learning, reasoning and problem solving. It is often divided into ‘weak’ or ‘narrow’ AI, designed for one particular task, and ‘strong’ AI, which has enough apparent cognisance that it can find solutions to even unfamiliar tasks - or even suggest tasks that users hadn’t thought of.
  • Algorithm - At its simplest, an algorithm is a set of mathematical instructions that can be followed by a computer. Often visualised as a flow chart, these rules are followed in a particular order to process an input value into the desired output, whether that’s sorting data or solving a problem.
  • Machine Learning (and the difference from AI) - Machine learning is the ability of software and its underlying algorithm(s) to evolve based on its own output - to change itself for the better, beyond what has been explicitly programmed by a human. It performs tasks repeatedly, measures its performance, and applies those data points to improve the process – much like a human learning from their mistakes.

Some of the more complex, nice-to-know terms include;

  • Deep Learning - Deep learning is a type of machine learning which removes the need for ongoing human interaction from the equation. Rather than a programmer telling an algorithm what a ‘better’ result looks like, deep learning is able to make those assessments itself.
  • Neural Networks - Inspired by the structure of the human brain, neural networks use ‘neurons’ arranged in layers to process data - A neuron is simply a mathematical function. Data is input at the first layer; elements of that data passes through subsequent layers until an output layer is reached. Neurons can activate or inhibit neurons in the next layer depending on the result of their own calculation. In this way, a path to output is found.
  • Natural Language - Processing Natural language processing is the process of interpreting human language – written or spoken – so that it can be interpreted as a command by the AI. This creates a naturalistic interface that’s accessible to pretty much anyone, regardless of their technological skill and experience.


Chances are, you’d heard of AI well before the past couple of years. The term has been around since 1956, and the concept considerably longer than that. It can be traced back as far as the 14 th century.1 The Lullian Circle, developed by Ramon Llul in the 1300s, is seen as the first instance a machine was used to perform useful reasoning. Depending on your age, maybe you first encountered the idea of AI as HAL 9000 in 2001: A Space Odyssey , through Tony Stark’s ‘Jarvis’ assistant in the Iron Man films, or in news stories about Deep Blue beating chess champs at their own game. More recently, Google’s voice assistant, Amazon’s Alexa, Microsoft’s Cortana and Apple’s Siri are overtly bringing smart computing, underpinned by AI, into our everyday lives.

Google CEO Sundar Pichai predicted that during the next 10 years, we will “shift to a world that is AI-first”. A report from Tractica predicts that annual AI revenues will soar from $1 billion in 2015 to $36 billion by 2025.

So why is AI suddenly such a hot topic in the marketing world? It’s partly because the technology has finally come of age - especially with the exponential growth of cloud computing, which makes it cheaper and easier to handle the processing that AI requires. But, at least as important to the rise of AI, is that it offers a solution to a problem facing almost every marketer today: no human can deal with the sheer amount of data out there.

Whether it’s purchase histories, social media posts, location data, user preferences, we now have access to an overwhelming amount of information about customers. By the end of 2016, it was estimated that global internet traffic surpassed 1 zettabyte . This is equal to 1 billion terabytes worth of data, or 1 trillion gigabytes. In order to be useful, all of this data needs to be processed. As data grows exponentially, AI is able to step in and take some of the strain. So let’s look at a selection of the ways it’s being used to do that in marketing today.

AI in Advertising

One form of AI is already well established within digital advertising. Programmatic trading utilises AI to optimise campaigns, by measuring the success of an ad and using self-improving algorithms to target ads and make intelligent choices about creative, timing, the amount to bid for in impression and more. It’s also used to analyse consumer data, tapping into the constant streams of data being created by consumers online

Christopher Pitt, Head of Marketing at search marketing specialist Vertical Leap concurs. “Where AI is key to marketing is liberating the creative elements from the manual, time-consuming drudgery of data collection. Previously, 75% of our time was spent on analysis, with 25% of our time spent on the actual work. AI has enabled us to switch that round and to achieve more effective and efficient results.” Vertical Leap have implemented AI in their business, and are seeing positive results. Pitt says that AI is “freeing up marketers to do more actual marketing with increasingly less time and budget”.

AI has also started to work its way into the creative side of advertising, in a variety of ways. In some cases, that’s by integrating AI functionality into the ads themselves. IBM’s Watson Ads launched on Weather.com last year with campaigns from the likes of Toyota and Unilever. This ‘cognitive advertising’ format lets consumers interact with the ad, asking for brand-relevant recipes, having a conversation about car features, or clothes that complete their outfit.

AI is even having an impact on creative decision-making. PicassoLabs is a company which uses AI to spot patterns between images that drive the most user engagement, helping marketers make informed decisions about which imagery they should use in campaigns. Through both its AdWords and display platforms, Google has been learning what makes good creative or selecting different creative based on customer traits for years - This is AI at work.

There are many more examples out there too: In 2015, Clear Channel and M&C Saatchi experimented with a ‘Darwinian’ digital out-of-home campaign, which created new creative variants based on the reaction of passers-by recorded with a camera, using only well received iterations to inform the next generation. In 2016, McCann Japan announced it had made its first non-human hire in the form of an AI creative director, which can take a client’s creative brief, consult its database of existing ads and provide basic direction for a human team to develop.

Across the myriad ways in which this technology is being used in advertising, the core idea is the same. AI enables brands to anticipate consumer’s needs and deliver more personalised experiences – the goal that advertising has been working towards since time immemorial, and especially since the rise of digital.

AI in Email

A channel as well-established as email might not seem like a hotbed of innovation, but it’s yet another space where you’ll find AI at work in almost every corner.

Maybe you’ve used Inbox, Google’s experimental cousin to Gmail, which uses AI to suggest automatic responses to emails that look like they’ve been written by a human rather than picked from a range of presets or categorises email better by interpreting message content.

A similar idea is being applied on the marketing side by companies like Conversica and Saleswhale, both of which offer a virtual sales assistants. These offerings can send AI-composed emails to potential customers, dealing with enquiries and warming up old or inactive leads with introductory mail before handing them off to human staff to seal the deal.

There are plenty of other solutions out there, covering practically every challenge facing email marketers today. Boomtrain’s personalisation platform uses AI to tailor marketing emails to each user, by pulling in content that is likely to interest them based on past behaviour and then sending each email out at the optimal time to increase its chance of being opened. MarianaIQ helps find new prospects by matching social, web and proprietary data to create in-depth personas that promise to go beyond a person’s job title. Persado and Phrasee help marketers find the perfect subject line, using machine learning to find the language that performs best and tailoring that to a brand’s existing tone of voice.

The ultimate goal of all this is simply; getting email marketing closer to a one-to-one interaction, treating each recipient as an individual - even in mass communication – without requiring a member of staff to handle conversations with each and every one of them. The technology is currently a long way from being able to handle the whole conversation without human input, but AI can certainly make the process easier.

Using all the information at your organisation’s disposal, to make real-time decisions about what and when to communicate is a key concept in modern marketing. This intelligent segmentation - categorising (and continuously re-categorising) types of consumers based on an evolving set of discrete data points started life in email software, and has since grown to be the backbone of many a marketing stack.

AI in Commerce

Commerce is another area where AI is already well-established, perhaps more than you might realise.

Product recommendations, the ones that we’re all familiar with from Amazon, make use of AI recommendation engines to pick the items most likely to get people spending, based on their purchasing habits and browsing behaviour. In Amazon’s case, these recommendations are powered by DSSTNE (Deep Scalable Sparse Tensor Network Engine, or ‘Destiny’ to its friends), an AI framework which went open source in 2016 - in the hope that other developers and researchers can help it become even more sophisticated.

As AI evolves, it is helping online merchants of all shapes and sizes better anticipate their customer’s needs, based on past behaviour and the context of their current search. This helps create a more personalised shopping experience, where a customer sees the products they’re most likely to want or need and retailers are able to more accurately forecast demand and focus marketing spend.

One brand making slightly different use of AI is The North Face, which is testing an AI-powered search interface for its range of jackets using IBM Watson. Instead of using the kind of search filters you might normally expect to see on a retail site, it simply asks the customer a series of questions – starting with where and when they’re planning to wear it, so it can check the weather, before moving on to more specific questions, such as the activities they intend to do while wearing it. These answers, which can be given in natural language, are used to narrow down the search results, offer products suitable for the customer, and of course, allow TNF to suggest complementary products based on the data they’ve gathered. As well as showing the right products, AI also offers a way of selling them at the price a customer is willing to pay. Historically, dynamic pricing optimisation has been used most commonly by airlines - but, from Amazon to Uber, an increasing number of companies are using algorithms to adjust pricing based on demand, availability and competitor pricing.

Before arriving at any of those shopping decisions, however, AI can first help streamline the initial product selection process, helping retailers know what to put on their shelves in the first place. One notable example of this is Lesara, a German online retailer, which uses AI to collate data on what’s trending on fashion blogs and social media, in combination with its own analytics and sales data, to help inform its buying decisions. This process helped it discover trainers with built-in LEDs before other retailers - which went on to become one of its biggest-selling products. And the principle doesn’t just apply to retailers. Netflix famously used similar AI to decide on its first original series. Based on six years’ worth of viewing data of its shows and films, an algorithm concluded that a political show starring Kevin Spacey and directed by David Fincher would be a surefire hit – which led to the creation of House of Cards .

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Virtual Assistants

Perhaps the best example of AI at work is the one that people are most likely to be familiar with: virtual assistants.

These cloud-connected assistants interpret voice commands from the user and carry out basic tasks on their devices. For most of us, our first exposure to assistants would have been Apple’s Siri, but today the poster girl is surely Alexa, the software behind Amazon’s Echo smart speakers, which according to Morgan Stanley, had sold an estimated 11m units by the end of the November 2016.

As previously mentioned, there are plenty of assistants out there: Google’s Assistant, (which powers it’s Home speaker and is found in many Android powered devices), Microsoft’s Cortana (which is embedded in the Windows platform), Samsung’s Bixby (one of the big selling points of its latest handsets), and many more. Most likely due to its impressive market penetration, the majority of marketers have been focusing on Amazon Alexa - the technology that powers it’s Echo home speakers and is starting to find its way not only into a myriad of Amazon’s own devices and apps but, importantly, third party hardware as well. The language processing is even available for on-demand use in the Amazon Web Services Cloud.

As you’d expect from an Amazon creation, Alexa very much has commerce at its heart. One of the core tasks that the assistant can carry out is making purchases straight from Amazon, while brands like Uber and Domino’s Pizza have got in on the act by creating their own transactional ‘Skills’ (Skills are Alexa’s answer to apps). Assistants also provide opportunities for brand building and content delivery. Campbell’s has created an Alexa Skill that helps users find recipes, while tequila brand Patrón has a similar one for making cocktails, and Johnnie Walker’s Skill walks users through a personalised tasting session of its whiskies.

Of course, virtual assistants of varying application and specificity are popping up in more than just consumer devices. Adobe’s recently introduced Virtual Analyst, powered by its Sensei AI, analyses data to detect anomalies for users of its Marketing Cloud suite. Adobe’s offering joins IBM’s Watson and Salesforce’s Einstein in the marketing industry alone. The not-too-distant future will undoubtedly see AI embedded into more applications in the form of virtual assistants, as vendors strive to anthropomorphise technology to make it more accessible.

Chatbots are an increasingly prevalent consumer-facing application of AI.

Rather than voice, these bots use a text interface to interpret a user’s questions and provide answers. They are commonly integrated into existing messaging services, providing a familiar interface for users.

The technology actually dates back to the turn of the millennium, when IM services like MSN Messenger and AIM were in vogue, but bots have undergone something of a retooling in recent years. In 2016 alone, chatbot support was added to Facebook Messenger, Skype, messaging app Kik and work collaboration tool Slack, with some big brands jumping on board – including Burger King, H&M, 1-800-Flowers, and airline KLM.

So far, the marketing use cases for chatbots have been similar to assistants, and split into the same three basic categories. There are branding exercises, like the bots used to promote Universal Pictures releases Ex Machina and Unfriended, or the Facebook chatbot for oral health brand Signal Pepsodent, which encouaged children to brush their teeth regularly. Then there’s so-called ‘conversational commerce’, which enables users to make purchases without leaving the chat window, something that’s being utilised by brands as diverse as fast food chain Taco Bell and fashion retailer Spring.

The biggest area for chatbots, though, comes in the form of CRM. A key advantage of utilising AI is taking the pressure off human staff – a major concern with live chat support, where demand can often outstrip a brand’s resources. RBS did just this, introducing a bot named ‘Luvo’ to its web chat in 2016. Built by IBM using its Watson Conversation tool, the bot is able to understand natural language requests in order to help out RBS customers with everything from updating a home address to authorising a card for use overseas. There are limitations – primarily the fact that chatbots can initially only handle the questions they have been programmed with, so if the user has an unusual query, they will be unable to help. However, it is possible to create bots that know when they are being asked something better-suited to a human response, and pass the conversation over to a ‘colleague’, creating a seamless experience for the user.

Tom Head, Director at digital agency Lab, says that his agency are experimenting with commerce chatbots - having built a test site with a bot as the primary method of interaction. The initial finding was that customers had to work quite hard to find what they were looking for. “If you think about the information you need - style, brand, occasion, gender, etc - there’s no start point” says Head, suggesting that the AI effectively has to ask a string of questions before it can start making product recommendations. Attempting to understand intent is much more of a challenge, because when training and coding the AI, there’s a natural tendency to think in programming terms - “particularly when you’ve got developers coming from a logical ‘if-this-thenthat’ background, where we’re looking at process flows”. The chatbot engines that support the majority of deployments are constantly improving however, so we can expect to see exponential improvement in this respect. Head summarised Lab’s work by pointing out that the technology is still young, it’s growing fast, and there’s a lot more to come. They are achieving results now, particularly with narrow applications, but it’s important to consider potentially frustrating customer experiences when deploying chatbots in the wild.

AI for Business Improvement and Marketing Performance

One type of AI tech already at work in a wide variety of marketing departments is Predictive Analytics.

Nearly all marketers will be au fait with some form of analytics, but predictive analytics goes one step further than reporting; using data to make predictions about future trends and behaviour. Examples range from transactional real time predictions - such as a customer’s propensity to respond to an offer, to generalised performance predictions such as email open rates. Powerful predictions such as these can be used to shape marketing campaigns, control stock levels and much more.

But perhaps the biggest opportunity offered by AI doesn’t come as a pre-packaged solution or an easily defined category. Applied intelligently (pardon the pun), AI can be used in almost every facet of business - to identify problems, optimise processes and improve performance.

Each category of AI mentioned above makes use of these general principles in order to provide a product, or deliver continual improvement in particular disciplines (such as email), but machine learning algorithms and artificial intelligence appliances are available from Amazon, Google, IBM and more - they can be woven into bespoke applications or existing infrastructure.

Whether you’re trying to reduce basket abandonment, improve customer service, identify poor processes in your department or positively impact a plethora of other workstreams, consider leveraging AI. AI is designed to do the kind of heavy lifting required to analyse relationships, transactions, data or behaviour - and much more - on a massive scale. The technology gives your business the ability to evaluate each and every outcome, make decisions and apply this learning in real time. This kind of ongoing analysis and continuous, incremental improvement simply isn’t achievable by human beings.

Questions to ask if you’re considering bringing AI. into your business

Am I solving a problem, or jumping on a bandwagon?

Lab’s Tom Head suggests that perhaps a better way to phrase the question might be “Are we going to be creating a better experience for the majority of our customers by implementing AI?”. The same rules apply when adopting AI as with any other technology – don’t start with the buzzword and ask how your company can get involved. Examine the specific problems that you face and see if AI offers a way of solving them. Importantly; look for opportunities to create better experiences. If your issues are to do with scale – too much data, too many customer service requests – the chances are that AI is a suitable tool.

Which type of AI would help solve my problem best?

AI is, as this whitepaper has hopefully illustrated, a very broad church. If your brand needs to serve more live chat customers than it has the staff for, natural language processing and chatbots might be the way to go. If you’re looking to improve your creative, AI’s applications in advertising may be of more interest. If you simply want to improve your subject lines or understand your web traffic, there are tools available.

Who will be responsible for AI in your business?

Whether you like it or not, AI (in one form or another) is probably already at work in your organisation. When actively implementing AI, Head suggests an agile “test and learn” approach. He suggests that as the technology and its applications evolve, it’s important to be flexible. In the coming years, it will likely become so pervasive that no one person will be able to oversee it. That being the case, it’s imperative that any and all business leaders - particularly those who work with marketing, data, IT and commerce, have a firm understanding of the capabilities and applications of AI. In future, we will likely see specialised AI roles, even up to the C suite, as businesses look for opportunities, efficiencies and revenue that AI can help them realise. AI is here to stay - and is a tool we all need to understand and make work for us.

What does the future hold for AI.?

To get an idea of how far-reaching AI’s future impact could be, you need only look at the various ways that Google’s DeepMind AI technology is already being used: They’ve cut electricity usage in their own data centres by 40 per cent, they are working with the NHS in the UK to help speed up the detection and diagnosis of health conditions, and they’re besting world champions at the ancient game of Go.

In January 2017, a European Parliament report on the effects of AI and robotics said the world is on the brink of a new industrial revolution. Certainly, there will be concerns about AI replacing human jobs - According to a 2015 Oxford University study, advertising creative directors face a 33% chance of having their job automated by 2035, as do marketing associate professionals – while marketing and sales directors are relatively safe, with a chance of just 1%.

Looking further ahead, that may just be the tip of the iceberg. The aforementioned European report also considered the personhood status of AI, the likelihood of AI advancements surpassing the intellectual capacity of humans within decades and, slightly terrifyingly, the need for a ‘kill switch’. Given that you’re unlikely to be murdered by your programmatic algorithm, though, that’s not a concern for marketers right now. In its current forms, AI is relatively simple, and the best way to look at the technology isn’t as a threat but as another set of tools in your toolkit.

Right now, humans and AI are good at different things. IBM talks about its Watson platform being ‘augmented intelligence’, meaning that it is intended to support existing human skills and help scale them up, rather than replace them outright.

And perhaps the biggest opportunity is in using AI to free up marketers - allow them to delegate all the drudge work to machines and focus on the parts of their jobs that really need human input, whether that’s negotiating with clients or coming up with a killer piece of creative!

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