Trends abound in Marketing. It’s an exciting part of the discipline’s continual evolution.
And the Artificial Intelligence (AI) space is no exception.
Over the past few years we’ve seen the rise and rise of AI discussion and solutions in marketing. From identifying new market opportunities through machine learning, and driving demand with intelligent research and targeting, through to assistants, image recognition, and personalised product recommendations.
I have spent the past month talking to a wide variety of industry thought leaders and experts in the AI space – from business, agency, and tech vendor perspectives. With the aim of identifying how Australian marketers are using AI solutions to enhance and anticipate consumer interaction. In this post, I would like to share some of their experiences and learnings to date.
However, before we jump in, as I’m sure most of you know, AI dates back decades. Let’s take a quick look back at how AI emerged.
Snapshot history
During WW2, British computer scientist Alan Turing cracked the German armed forces’ ‘Enigma’ code with an ‘intelligent’ machine. He had built on the work of Konrad Zuse, Warren McCullough, Walter Pitts and many others around the first working program-controlled computers, game theory, and the logical behaviour of nervous activity and connected neural networks.
In 1950 Turing released his landmark “Computing Machinery and Intelligence” paper, outlining the Turing Test. A test of a machine’s ability to exhibit ‘intelligent’ behaviour. In short, if a machine could carry on a conversation (over a teleprinter) that was indistinguishable from a conversation with a human being, then it was reasonable to say that the machine was “thinking”.
In 1956 the term ‘Artificial Intelligence’ was coined by a handful of mathematicians and scientists in a brainstorming workshop at Dartmouth College, New Hampshire. Called “The Dartmouth Summer Research Project on Artificial Intelligence”, the workshop was widely considered to be the founding event of AI as a field. Although no specific theory or methodology was agreed, the researchers continued with a 2 month study of AI with the shared vision that computers can be made to perform intelligent tasks.
During the ‘space race’ to achieve firsts in spaceflight capability, the long-term Stanford Cart project took many forms from around 1960 to 1980. It was originally designed to test what it would be like to control a lunar rover from Earth and was eventually reconfigured by Hans Moravec as the first computer-controlled, autonomous vehicle. Successfully traversing a chair-filled room and circumnavigating the Stanford AI Lab.
Human fascination from a philosophical sense was fast becoming a reality, and in the 1970s, Japan started to lead the way with advancements in machine learning algorithms in robotic systems.
However, it was IBM in 1997 who shocked the world with its Deep Blue chess-playing computer system (originally called ChipTest, then Deep Thought) that defeated the reigning world chess champion at the time, Garry Kasparov.
After major surges, AI funding started to dry up after the dotcom burst in the early 2000s.
However, as the digital landscape became mainstream, and computing power advanced again, AI has now become the next mega-trend – after ‘mobile first’.
Now we see regular headlines around the world from Alibaba, Apple, Google, Amazon, Tesla, Uber, Microsoft, Salesforce, Facebook and many other businesses, about advancements in autonomous mobility, affective computing, artificial co-workers, chatbots, cognitive computing and intelligent personal assistants.
One of the more memorable ones being about Sophia, a social humanoid robot developed by Hong Kong based company Hanson Robotics. Sophia’s face and voice were modelled on the actress Audrey Hepburn, and she was unveiled at South by Southwest (SXSW) in 2016. A year later Sophia became the first robot to receive citizenship – granted by Saudi Arabia.
Exploring solutions for marketers
One of the biggest applications of AI in marketing is for more intelligent data analytics, specifically predictive analytics to better understand customers.
In late 2016 Salesforce launched Salesforce Einstein, artificial intelligence embedded in the Salesforce Platform, claiming Salesforce to be the world’s smartest CRM.
Einstein is a layer of artificial intelligence that delivers predictions and recommendations based on people’s interactions with technology. Marketers and sales teams can use Einstein analytics to identify insights to automate responses and actions, and make employees more productive. For example deals can be identified in terms of prediction to close, giving teams the right ones to focus on.
Einstein is also being used in social media. For example, when a customer redeems an offer by taking a picture with the product and posting it to social media, Einstein uses image recognition to identify the company product and auto-responds to the customer. Einstein can also understand the intent behind the user’s social post and respond with offers for a particular brand.
At the same time, Adobe responded with its Adobe Sensei product – AI and machine learning for customer experiences. Also particularly useful within the Adobe Creative Cloud & Campaign ecosystems where it can used to identify better imagery to drive conversions, allow you to write an email and have it translated into multilingual options, and modifying length of copy for different channels.
In 2018 Alibaba launched its own AI copywriting tool that can write 20,000 lines of copy and thousands of ads in a second. Launched through its digital marketing technology and big data unit, Alimama. Esprit was one of the first brands to test it in Taobao and Tmall, adjusting the length and tone of its advertising copy and choosing whether they wanted the ads to be “promotional, functional, fun, poetic or heart-warming.”
One of the challenges in all of the above is not just understanding human behaviour, but also understanding and interpreting human language.
So as we move from hype to reality, here’s what some business leaders are doing to test the waters.
Dave King and his new company Move37 make a move into augmented creativity
Dave King, cofounder of Move37, touched on some fascinating points in terms of using AI in partnership with people in the process of problem solving and creativity.
Firstly, I asked Dave about the meaning behind Move37, and then what they’re aiming to do in the AI space.
For those of you who may not know, Dave cofounded The Royals agency along with Andrew Siwka, Stephen O’Farrell and Nick Cummins back in 2010. And he remains a Director and advises them on innovation, AI and other emerging technologies.
He says his love for all things digital stemmed from the first text browser that he used with dial-up when dating a girl at Monash University in the early 90’s. She was doing a long distance education course, and Dave was starting to tinker with accessing servers across the world on this thing called the internet. He ended up building the first website for the Arts Faculty as a Psychology Course Advisor under direction from the Dean.
Dave continued in his career to create websites for an ISP, then went to “an awesome job” reviewing video games at Hyper Magazine (Next Media), worked at MSN in the late ‘90s followed by exploring emerging technology including mobile TV and IPTV with Sensis.
Always fascinated by human behaviour, Dave started looking at how machines could work in different ways in the creative world.
The name, Move37, was inspired by a move in an historic match in South Korea in 2016 of the 2,500 year-old game Go. Between 18-time world champion and Korean Grandmaster, Lee Sedol, and Google’s artificially intelligent Go-playing computer system, AlphaGo – designed by a team of researchers at DeepMind, a London AI lab now owned by Google.
AlphaGo’s 37th move in the match’s second game was first described as “very unusual” and thought to be a mistake, but then realised it was simply “beautiful” and “creatively genius” in the way that it turned the game. It had calculated that there was a tiny one-in-ten-thousand chance of a human ever playing the move, and played it anyway. AlphaGo then went on to win the game and the overall match by four games to one.
Dave talked about one of the exciting opportunities for AI. How to inject some novelty and randomness to help people think laterally. Allowing creative ideation to be more than just by humans. Thought of as more in partnership with machines. And says he’s “testing using machine learning, data mining and natural language processing to augment ideation, conceptual creativity and invention”.
Move 37 is working with a number of algorithms, models and datasets including GPT-2, developed by Open AI. GPT-2 is state of the art in the field of text generation, but Move 37’s ambitions are to bring common sense reasoning and causality to its AI.
Open AI is a non-profit AI research company comprised of a team of hundred people based in San Francisco with the mission to ensure that artificial general intelligence (AGI) benefits all humanity. The GPT-2 model trained itself on 8 million webpages including unstructured conversations on topics in Reddit threads, and the related interesting external links that people have recommended that have received at least 3 upvotes.
But Dave sees AI in its very early days in the creative realm. His focus is on augmenting machines and knowledge workers. People whose role requires thinking for a living – utilising their experience and the ability to collect and analyse data for decision making and action (ie: marketers, architects, urban planners, researchers, scientists, lawyers, academics, engineers etc).
The main focus of Dave’s work at Move37 is creating an augmented creativity engine. A platform or capability to brainstorm in new and interesting ways, which can make sense of the mass of information available, and distill it in creative ways.
Dave and his team’s approach centres around creative thinking and critical thinking, with creative reasoning capability to improve people’s ides by introducing new lenses, new perspectives, new directions.
He sees humans as currently being the curators of what might work when AI tools offer solutions.
Whilst machines and AI are relatively good at correlations between things, AI hasn’t been great at explaining where recommendations come from, or the transparency behind causality. And therefore one of the major issues is the lack of trust in AI recommendations in the realm of major problem solving.
However, Dave sees this as a big opportunity. To be focused more on a common sense understanding of the world and the data. Looking at developing tools that understand causality, frame relationships, and understand patterns and analogy.
So for knowledge workers, it’s about artificial creativity augmenting their expertise.
Dave is reverse engineering creative process and taking the popular practices in commercial creativity where there’s a problem that people need to work through to create a solution.
He sees creativity as putting things together in novel ways, and believes creativity can be learned. It can be developed, practiced, iterated and improved. So the platform that he’s working on will help people get better at problem solving by understanding the relationships and connections between things. Users will have the chance to combine concepts and knowledge in novel ways to identify relationships that are interesting and that weren’t readily identifiable or thought of before.
Mauricio Perez outlines how to get your chatbot right
Mauricio Perez is a Human Centred Design (HCD) strategist, specialising in Service Design, User Experience (UX) and Customer experience (CX).
Mauricio helped assist nib health funds (nib) to become the first Australian health insurer to introduce AI technology (a chatbot called nibby utilising Amazon Alexa) to assist health insurance enquiries.
The chatbot provides customers with access to simple responses regarding their health insurance. And unlike many other chatbots, nibby is integrated into nib’s web platform, allowing it to intelligently move customers to the right sales or claims consultant as a customer’s query becomes more complex, and to offer assistance during key customer service moments.
Mauricio didn’t think it would be difficult to design a conversational interface flow to get people to the right place. What he learned, however, was quite the opposite. Mapping the flows with text seemed easy enough until he realised the enormous range of language constructs that people used in even responding to a bot greeting in the context of getting what they needed.
For example, in concept testing, some people assumed they were talking to a real human and responded in terms that were not understood by a nascent nibby. The variants in natural language varied by English capability, education levels, jargonistic terms, emotional states, device usage and so on. At times, it wasn’t explicit that the human was conversing with a bot and when they found out, some experienced a sense of betrayal and distrust. For best practice, always ensure that the bot presents itself as a non-human and announce its limitations so that people have a realistic expectation.
Something else that tended to annoy people was the lack of variety in the type of responses when there was an unknown input. When people expressed a need and the bot did not recognise the words, it gave a limited number of responses which tended to affect the level of trust people had when they kept getting the same response. Instead, try to guide the user in responses that will set them up for success.
Another challenge was that the technology had already been decided upon. This meant that the flexibility of what could be delivered was already being compromised. In hindsight, it would have been a better exercise to perform a “Wizard of Oz” test. This is where somebody emulates a bot in another room and captures the common ways in which an unknowing human would interface with them. Only then once the conversation variables are captured, should a technology decision be made knowing the customers’ conversational contexts and the parameters that the technology should flex towards.
In addition, this test would determine the kind of personality would best suit a future chatbot and even why it might vary its language in some contexts. Should the conversation be playful or dry? Understated or enthusiastic? Formal or Informal?
Also, he learned that conversation decisions should be made collaboratively. Copywriters should be working together with customer support staff and developers. A good collaborative tool like LucidChart or BotMock should be used.
Mauricio highlighted that “integrating with backend systems and humans became one of the biggest challenges. The logic of the front end conversation had to match the logic at the back end of the system. Ensuring that the customer experience was being enhanced and not breaking down.
Thinking about the service of delivering a chatbot, there were some important lessons learned to help guide the launch of a chatbot, especially when preparing for the human behaviours and technology chosen. There was another lesson learned when the chatbot was launched: Human Hand-overs. This is when the bot needs to hand the conversation over to a human to continue or when things go wrong.
- Ensure your bot captures all the different ways somebody might ask for a real human.
- Ensure that the systems being used to hand-over are quick, reliable and easy to use from a contact centre perspective. Otherwise, people will disengage from the conversation by the time a real human reads the conversation and gets back to the user.
Finally, it was tempting to start adding UI (user interface) elements like buttons or form components into the conversation interface. While this made it easier to get the user to get them to their destination, this made the chat interface less accessible and limited the capability of NLP (Natural Language Processing). The addition of UI into the conversation is then further limited when the logic needs to be scaled or adapted to a voice interface in the future.
Read more about the Nibby Case study, and Chatbot and AI Design principles
Brad Bennett and Mercer Bell are realising the full value of Customer Experience with AI
Brad Bennett is the Executive Strategy & Analytics Director at Mercer Bell, Australia’s leading CX agency. Brad has the common thread of technology throughout his career. Having been a developer at tech consulting agencies as well as having worked in crisis communications and developing tech products. He leapt into marketing over a decade ago moving from New York to Sydney. And is now focussed on the strategic side of connecting clients’ products and services with customers.
Brad makes the key point that CMOs now have far greater control over technology budgets and decisions around tech investments. And when it comes to AI one of the key challenges is demystifying what AI actually means.
Brad says, “it’s hard enough for tech people, let alone marketing leaders who may not be grounded in a development background, to be able to cut through the complexity of the space. The lack of detailed technical knowledge behind AI solutions makes it hard to determine how to fully deploy AI solutions, which can lead to unrealised potential or unrealised business value in solutions.”
However, whilst it’s a complex space there are some great first steps being taken by many marketers.
Especially utilising machine learning for deeper customer profiling to identify better patterns in structured data that the human eye may not be able to identify.
There have also been some good quick wins in the more production-based space of automating optimisation tasks. For example knowing a range of messages and scaling the targeting to the right person at the right time.
One of the big opportunities is in the predictive space. Predictive modelling theory and practice has been around for years, however, it’s wildly complex and challenging.
Next-best actions and next-best product recommendations over longer time frames is a great battleground for marketers. Especially when understanding that most consumer decision-making is made using a ‘system 1’ irrational & emotionally biased mind.
Given how irrational consumer behaviour can be, then discovering logical patterns is confusing. Brad sees that one of the exciting areas is trying to trust the outcome of machines with patterns that don’t really make sense. Patterns that are more likely be the real truth behind how consumers are behaving and therefore need to be focused on and optimised accordingly.
His final word of warning for Australian marketers is the unrealised value of AI applications due to a capability gap. Given the complexity and deep skillset required for AI development, tech architecture and data integration, it’s not a simple conversation. And as the tech-solution companies tend to be leading the way, then marketers are often being over sold when it comes to AI solutions.
So Brad sees that being careful not to over invest in a tech stack with AI tools and solutions built in, as critical. Value-based and general business use cases need to be established first, which helps better define a successful way for moving down the early days of the wild west AI path.
Henry Innis, of Mutiny Group, is identifying greater marketing value with predictive AI models
Henry Innis is the Chief Strategy Officer and Founder of Mutiny Group, a team, of data scientists, engineers and strategists that help put the rigour and measurability back into marketing. He talked about how cloud computing and advances in deep learning models that sit within a neural network now help marketers to look forward and predict results, rather than viewing data as a retrospective exercise.
Predictive modelling has leapt forward with the power of the cloud where enterprise-wide data can be analysed much quicker and cleaner than ever before.
This allows marketers to create models to conduct analytics for effectively predicting the impact of marketing spend. Speed and accuracy have become hallmarks of these new AI-based models. Which means marketers can spend more time looking forward, scenario planning, and making hypothesis based on their budgets and channel options.
Henry views AI for marketers as the processing of data that complements human activity and works together with creativity, rather than being viewed in isolation.
The time series, or frequency, of data feeds, is therefore, absolutely critical to identify the impact of activity. Henry reinforces the point that models are only as good as the data being fed in. But if you get them right, then they’re massively powerful.
As a result, a more numbers-driven conversation can occur that is more strategically aligned to the business. Hence giving CMOs the power to talk about potential solutions and options with the C-suite, in particular the CFO.
Jeremy Smart sees AI as augmented intelligence to help marketers be more effective and productive
Jeremy is the Digital Agencies Segment Leader, Asia Pacific/Japan at Acoustic. Formerly the Watson Customer Engagement (WCE) business unit at IBM.
IBM sold the WCE unit off to private equity firm Centerbridge Partners in early 2019. Centrebridge set up Acoustic as a stand-alone company to compete with the likes of Adobe, Oracle and Salesforce.
Their focus is on point solutions that work well together as a collective within an open ecosystem world. Meaning, given that there is so much technology today, and most companies typically use all sorts of different technology rather than one unified system, then marketers need to have solutions that integrate all the myriad of technologies.
Acoustic sees AI (and it’s other marketing solutions) very much as ‘co-workers’. Offering marketers proposed ideas, insights and solutions that the marketer can then use their knowledge to decide which way to go. This builds a much greater level of trust versus just expecting the ‘black box’ to do its thing and run the show.
Jeremy sees Japan, Korea and Taiwan as doing very interesting things. Plus he highlighted how Singapore’s Government has been looking to establish Singapore as Asia’s Silicon Valley. As well as the developing countries of Thailand, Vietnam and India emerging with interesting start-ups and application of AI solutions.
However, Jeremy identifies that one of the biggest issues is the lack of capability around the understanding of technology and marketing working together. Today, the greatest marketing advantage is technical marketing talent — the martecheter.
He refers to a recent IBM research study that highlighted as many as 120 million workers in the world’s 12 largest economies may need to be retrained or reskilled over the next 3 years as a result of AI and intelligent automation. Only 41% of CEOs surveyed say that they have the people, skills and resources required to execute their business strategies.
The bottom line for CMOs is not to get swept up in the technology sale. Rather focus on doing the due diligence around the detail behind it. Lift the hood and understand what is available to solve business and marketing challenges. And definitely walk before they run – testing in small areas and then scaling up based on business results.
Then AI can be viewed as augmenting marketing activity. It can be seen as intelligent software that can support the marketing team to be more effective and productive.
Effective around campaigns and understanding end customers better and more effectively and efficiently engaging with them. With smart programs that can identify information through better customer profiling, better prediction of behaviour, and better engagement. Ultimately resulting in greater efficiency. Which, as a result, allows marketers to focus on more strategic areas by freeing up individuals from more mundane, production-based tasks.
John James is building measurable AI applications
John James is an independent strategic consultant for CxOs and Founders, focussed on growth and revenue. He talks about real life, measurable applications of artificial intelligence. Including a tool that he’s building for CMOs to help reduce complexity and improve the operational efficiency and effectiveness of marketing throughout the end to end process of the marketing funnel.
John sees AI as a bit of a buzzword at senior management level at the moment. However, true AI, should be seen as evolutionary computational algorithms. That learn and adapt over time without much human intervention and can operate within a neural network (a brain).
One example being RankBrain for Google’s approach to search. RankBrain is a component of Google’s core algorithm which uses machine learning (the ability of machines to teach themselves from data inputs) to determine the most relevant results to search engine queries.
Before RankBrain was released in 2015, Google utilized its basic algorithm to determine which results to show for a given query.
After RankBrain was released, it is believed that the query now goes through an interpretation model that can apply possible factors like the location of the searcher, personalization, and the words of the query to determine the searcher’s true intent. By discerning this true intent, Google can deliver more relevant results.
The machine learning aspect of RankBrain is what sets it apart. To “teach” the RankBrain algorithm to produce useful search results, Google first “feeds” it data from a variety of sources. The algorithm then takes it from there, calculating and teaching itself over time to match a variety of signals to a variety of results and to order search engine rankings based on these calculations.
John has been fascinated by the opportunity of applying AI for marketers, and is building an application that can help marketers measure the revenue impact from activity, and link it all the way back up the marketing funnel to brand dimensions. In essence, an autonomous business growth engine to drive financial outcomes for businesses with minimal human intervention.
This involves a technically complex approach that connects a lot of disparate systems together. And utilises a long term unified view of data, channels, brand, sales and post-sales activity.
It’s a system which makes the agency versus in-house decision a little redundant. By facilitating operations and connecting strategy to tactical execution, measurement and feeding it back to a circular process of growth.
John is focussed on automating more of the strategic planning side of marketing to ensure unified outcomes are the focus.
He sees the application as going a long way to removing much of the human bias (political and/or vendor bias) in decision making to get a better result. Helping marketers to be able to adapt to market conditions quicker. And to automate some of the processes that are getting in the way of implementing activity. For example, automating brief writing and the ability to brief expert vendors locally or internationally instantly. Removing much of the back and forth and risk for miscommunication or reinterpretation. And hence low level, low value coordination type jobs can become redundant.
His AI solution can be adapted to different sectors and different products and services all based on understanding the customer decision-making process.
John warns that one of the hardest things with AI and automation is the dynamic nature of markets. But by having the agility to make better planning decisions based on unbiased data, then AI can help give marketers a competitive edge.
Conclusion
So please take a slow, deep breath.
Yes, it may be the feeling of another wild frontier, however, AI is a mega trend that is here to stay. There are amazing opportunities to deliver real business value for marketers.
It’s not Doomsday with robots taking everyone’s jobs. Whilst we’ve seen Amazon testing cashier-less stores, other businesses, like Starbucks, are investing in their people and rewarding customers using data.
AI can be seen as augmenting and assisting marketers to ultimately do better marketing – becoming more efficient and more effective.
Whether you’re wanting to unearth previously hidden market opportunities, mine data better, or are starting to join up customer journeys through the myriad of channels, take the time to delve below the surface of AI and really understand what the technology can do.
Remember, a self-learning algorithm is only as good as the data being fed into it. And as most people have outlined above, testing in small areas, refining, optimising and then scaling out a solution is a very prudent approach.
If you’re starting to have AI discussions, or are part way through an initial AI project, then make sure you surround yourself with a variety of expert views. It’s important to find the right unbiased advice.
And if you need any help in being pointed in the right direction for your requirements, or identifying your AI readiness, then we’d love to hear from you.
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