Why Do We Need to Know What AI Is Thinking?

People may never be able to fully understand how AI thinks, but transparency is still needed to build trust with users and optimize the productivity of these platforms.

How important is it to understand the technology we use? Practically everybody uses a computer on a daily basis, whether at work or at home, but relatively few of us know how that computer stores files or connects to the internet. The fact of the matter is that nearly all devices today operate at such a high level of complexity that understanding their inner workings is completely impractical. What really matters is that we produce results in partnership with that technology.

But artificial intelligence is different. A huge reason behind lagging adoption of the technology is the simple fact that it is misunderstood, engulfed by numerous myths and misconceptions that cause many people to worry that it will radically change their day-to-day work processes or even take their job.

Combatting those myths will take a degree of comprehension on the part of users, despite how complex the technology may be. Though AI may be too complex for anyone to fully grasp, even a rudimentary understanding will build trust with a skeptical public.

Can Humans Really Understand Machines?

A growing school of thinkers, including technologist David Weinberger and Facebook’s head AI scientist Yann LeCun, argue that understanding an AI’s thought process is not only unnecessary, but actually detrimental to the efficacy of the technology.

The main strength of AI, Weinberger argues, is that it makes decisions based on more variables than humans are capable of considering at any given time. Since the complexity, speed, and nuance of these decisions are beyond the scope of human understanding, rendering the technology understandable or explainable to the average user would require simplifying the process in such a way that would limit its efficacy.

Optimization Over Interpretation

But of course, this doesn’t mean that AI-enabled tools should be black boxes, completely illegible to their users. Though marketers may not be able to fully understand the technical details of how AI works, it’s clear the opacity of AI makes them worry about control — our recent customer survey revealed 25.5% of marketing AI users felt they had little control over their platform’s activity. Instead, measures of transparency should be introduced that allow users to at least understand the AI’s end goal in making a decision.

No technology is perfect, and at one point or another, any system will need to be calibrated. By giving their operators a certain degree of transparency into what inspires its decisions, AI algorithms can facilitate active collaboration with humans, which will only help build trust and dispel common myths about the technology. 58% of agencies responding to our survey said they discovered new audiences with their AI, but those kinds of insights can’t happen if marketers don’t trust the information they’re getting from their autonomous partners.

That’s the motivation behind tools like Inside Albert, which we created to grant users transparency into the world’s first marketing platform built from the ground up on AI. Instead of limiting the scope of the platform, Inside Albert gives marketers the information they need to calibrate and recalibrate the way they use Albert. Any understanding of how the platform works, even if it’s limited, gives professionals the opportunity to see AI as a tool that augments their work, rather than a threat that replaces it altogether.

Before AI adoption can really surge, some degree of transparency needs to be established. The real value of AI isn’t in its algorithm or its computing power — it’s in the relationship between that power and the human operator that understands how to leverage it. But that relationship has to be based on trust, and that trust has to be based in transparency.

How to Choose Your Agency Partner

The digital marketing landscape is changing quickly, and ad agencies need to adapt if they want to stay relevant.

Executing a highly personalized marketing campaign across a variety of channels is more challenging and complex today than ever before. For many companies, the knee-jerk reaction to this deepening complexity is to turn to an advertising agency for expert assistance. In certain circumstances this can still be an effective move, but it’s no longer the kind of common sense choice it was when print, television, and out-of-home advertising dominated the industry.

As Albert™ CEO Or Shani points out in CMO, “The role of the media buyer as middleman in digital ad buying is very different from the role of middlemen in traditional media.” If companies fail to adjust their relationships with ad agencies and media buyers, Shani continues, “advertisers put themselves at risk of absorbing even more costs in the form of marked-up prices at the bid level — or of the entire service provided by partners.”

As such, marketers looking to choose an agency partner must take a strategic approach to the selection process by making sure that each candidate is capable of providing value in today’s marketing environment, not the marketing environment of the past. Ad agencies that have truly kept up with the times will check all three of the boxes outlined below.

A Demonstrated “Right-Fit” Track Record

At the end of the day, “marketing” is a catch-all term that can take on significantly different meanings in different sectors. A heavy industry manufacturer may need to advertise just as much as a restaurant does, but this doesn’t mean that the same ad agency will be the right fit for both. When choosing an agency partner, a company should carefully examine candidates’ backgrounds to ensure that a potential partner has a demonstrated record of success in the company’s specific field.

This holds true not only for industry expertise, but for account size as well. If your company would be an agency’s largest or smallest account, this should prompt you to reconsider the decision to commit. Is the agency capable of handling a campaign portfolio this large? Conversely, is the agency nimble enough to meet the needs of a portfolio this small? Account size shouldn’t always be a disqualifying factor in and of itself, but it’s vitally important to consider.

A Capacity for Predictive Insights

As the volume of data bearing upon critical marketing decisions grows, strong data analytics will become increasingly important. When it comes to evaluating both specific ad performance and broader campaign effectiveness, a company’s agency partner will often be the only one with all of the data necessary to perform the requisite calculations.

Unfortunately, these evaluations tend to only go as far as a rundown of metrics like impressions, ad clicks, starts and stops, and click-through rates. While these metrics certainly help paint a broader picture of overall campaign performance, they don’t provide companies much in terms of forward-looking insight. True campaign optimization can only occur when a company is able to surface and act on predictive insights, in real-time, like those provided by autonomous tools like Albert. By implementing this kind of AI tech, companies can begin to understand what is likely to happen in the future, not just what has already happened.

Completely Transparent Operations

According to the Association of National Advertisers’ industry-shaking 2016 Media Transparency Report, “Numerous non-transparent business practices, including cash rebates to media agencies, were found to be pervasive in the U.S. media ad buying ecosystem.” When agencies are compelled by these suspect incentive structures to repeatedly direct their clients’ ad spend toward media that may not actually be in the clients’ best interests, the entire purpose of an agency partnership begins to break down.

As such, companies must do everything they can to ensure that the agency partner is organized and dedicated to delivering positive outcomes, not claiming kickbacks or rebates from ad publishers.

More often than not, this will include a willingness on the part of the agency to work alongside an AI platform like Albert. Artificial intelligence marketing tools make marketing more efficient, and an agency that refuses to recognize this value is unlikely to be a good partner for any company hoping to stand out from its competition.

Why Optus Chose Albert For AI Marketing

At a recent iMedia event, Naomi Simson, Co-founder of the Big Red Group, interviewed Angela Greenwood, Director, Acquisition & Customer Marketing at Optus, about her thoughts on:

  • The future of marketing – and our addiction to attribution
  • The role AI will play – and what people fear the most
  • A marketer’s priorities, a melding of analytics, creative and the big idea.

Naomi Simson: At Optus, there was a real sense of urgency driving the implementation of AI technology. What were the commercial drivers behind this strategic direction?

Angela Greenwood: It was a few things. It can be very, very difficult for us to understand what the right level of investment across channels is in digital, and how we move money between these in real-time. Often, it’s very much a siloed approach to how much investment we’re putting into each digital media channel. We wanted to understand if we gave an autonomous AI tool the freedom to move funds between all those channels, and to serve the ‘right’ creative in real-time, what would happen? So there was a healthy amount of curiosity about what we could achieve.

NS: In a marketing environment where the open web is becoming a thing of the past and we’re dealing with the ‘Walled Gardens’ – each of them claiming they ‘own the customer’ – the notion of attribution becomes even more complicated. We as marketers focus so heavily on attribution, so what was that conversation like on your journey to AI

AG: We still have a very healthy interest in attribution. We still put a lot of effort into investigating effectiveness – but you can only ever look at that retrospectively, not future-facing. And for us, the amount of sessions we attribute to one single channel –  when we know that a customer’s journey is way more complex than that – means we can get really tripped up on thinking one channel or piece of creative is more or less effective than it is in reality. What we’ve been able to see with AI is that some of the creative constructs that work today, no longer work tomorrow – so we want to be able to see that in real-time.

NS: Being able to test ideas at scale was one of the driving factors that attracted you to Albert AI. Tell us about that.

AG: AI can take a lot of the heavy lifting away from some of the lower-level tasks around digital media buying. But what it actually creates is a lot more tasks around how you feed this engine with enough creative to be able to personalize at scale to prospecting leads. Because the possibilities are endless – they’re only really limited by what we’re able to put out there.

NS: Let’s talk about the agency relationship, and the human side of AI and what it means for the people on the team and their concerns and challenges.

AG: I was pretty concerned and challenged myself. As performance marketers, we have a bit of a reputation for being just a little controlling, so to be able to go from a very detailed digital marketing plan that’s broken down to the nth degree, to a single line item that says, ‘here are the dollars, go do’, that’s terrifying. It was really important to get the set-up right. We had to get back to basic things like getting our naming conventions right and making sure all of our campaign structures were set up correctly.

NS: I can imagine there was a lot of fear for the individuals involved – but as you’ve noted it’s really the low-hanging fruit that AI takes care of, to really free up the people to focus on those interesting strategies and learning pieces. Tell us about that journey for your people, and your agencies, and how they shifted from the execution to the strategy.

AG: It will never cease to amaze me how much people will cling to low-value tasks. So once you separate your people and your agencies from all that, you free yourselves up to think more strategically – like how we’re going to tailor our value propositions to different audiences, how can we actually do that really personalized creative at scale, and how can we take the insights we’re getting from the engine and do something with it? And that’s the really big step that we’ve been able to take. And also, because this type of tool works very fluidly across all the different digital channels, it has actually opened up opportunities for our internal teams to become more cross-functional, and they’re now thinking much more holistically about the customer journey.

NS: What does success in marketing look like for Optus?

AG: It’s a number of things. We want to be as efficient as possible. Digital still pays a really massive role in driving brand consideration, but it’s also really important for us to keep building the top of the funnel through that activity. It’s really important with an AI solution that you’re actually pointing in the right direction – because if you feed it the wrong signals and optimise towards the wrong thing, it will go really hard after the wrong thing. So that’s been a really important learning – how do we make sure that we’re actually optimizing the right activity to the right result? And that’s never going to be uniform across everything that we do.

NS: Let’s talk about cookies. We didn’t just set this up for now, we wanted to plan for the future – so how does this AI solution deal with privacy issues and knowing who your customers are?

AG: For advertisers, the most important thing we can do is make the most of all our first-party data, and being a telco, that’s a unique position to be in. We do actually have a fair bit of that, so for us, it’s about how we can we utilize that first-party data for the AI to find suitable look-a-like audiences. And then how do we incentivize uses to engage with our own platforms? How do we ensure we get more people using our app so that we are not dependent on the outside world for that view of our customer.

NS: Do you have any particular campaigns you have run that have helped you identify intent to purchase?

AG: Where we see value in Albert and how some of our assumptions have been challenged is around thinking that certain audiences have intent for a certain product, and how Albert then runs through a full range and offers alternatives. We’ve actually found some really interesting crossovers between audiences in terms of intent that we never would have understood before Albert. For example, if someone has intent to buy a post-paid mobile, they actually have a strong intent for accessories as well, so then how do we capture that as part of the ongoing conversation with that customer?

NS: That’s what AI does particularly well – it’s looking for, based on certain previous behaviors, that intention data, and taking all of that information to start predicting ‘what next?’ It’s almost impossible for a human to do that.

AG: You would need a massive team of data scientists to achieve anything similar at scale. And anyone who’s tried to hire a data scientist knows how hard that is. Albert enables us to do all this very rapidly.

“We have had a very established test and learn program at Optus for many years, but the speed now and the scale at which we can get those insights is just so much faster now. We are an incredibly competitive category – telco is a blood sport, and it’s very much about how you gain market share and take market share from competitors. So we will do anything we can to get a competitive advantage.”

NS: What does the future look like for Optus and your AI journey? And I’m talking no more than the next 6-12 months.

AG: For us it’s about how do we get more signals in? How can we get more data in for that to work for us? How do we do a better job of ingesting offline data to optimize towards an omnichannel result, and how do we better leverage our first-party data? And it’s also about the creative side – we have only dipped our toes in terms of what Albert is able to do when it comes to creative optimization, so for us, it’s about how we set up so many different creative variants to be able to really maximize results.

“Don’t fear the machines. It frees us to be more creative and more strategic and that’s a win for the client and agencies.” – Angela Greenwood.

Read how other clients used Albert’s AI capabilities to fuel their digital marketing and advertising

Albert is distributed in Australia by Marketics, a wholly owned subsidiary of the Big Red Group. This interview was originally published by Naomi Simson.

Automation + AI: The Chocolate & Peanut Butter of Ad Tech

Artificial intelligence (AI) is sometimes confused with automation, and the terms are often used interchangeably. When talking to vendors, it’s critical for digital marketers to decipher exactly what’s being offered so you know it will truly address your needs. Today, let’s demystify these terms and discover what happens when the two are combined.

Differences Between Automation and AI:

Robotic Process Automation (RPA) software is great for simple activities and repetitive tasks that follow instructions or workflows set by individuals. It is best suited for highly repetitive and predictable tasks. Automated tools require manual configuration and human supervision to effectively execute campaigns. The trick with RPA is for humans to anticipate every permutation so the machine is programmed to behave the right way every time. This is why constant vigilance is required. If the environment changes, marketers must manually step in and make the necessary adjustments.

AI refers to how computer systems can use huge amounts of data to imitate human intelligence and reasoning, allowing the system to learn, predict and recommend what to do next. An AI capable of understanding marketing KPIs can use various algorithms that act in concert to find signal in the noise of data and find paths to solutions that no human would be capable of. Most AI today works in an assistive fashion, providing next best action recommendations to humans who then decide whether to trust them or not and then manually make adjustments.

Combining AI & Automation: Sweet Treat!

When robotic process automation is combined with elements of AI such as machine learning, the result is known as intelligent process automation (IPA). An IPA tool is powerful because it allows us to reap both the benefits of automation – increased speed, efficiency, time-savings, and ability to scale – with the insights, flexibility, and processing power of AI.

Marketers who use IPA are able to augment their capabilities, while off-loading repetitive campaign management tasks to the machine. It’s different from pure robotic automation in that the AI can start, stop or even alter what it is doing based on the environment in which it operates. What’s more, because the best AI systems allow marketers to set guardrails, there’s no chance of unforeseen events taking outcomes too far astray. For marketers, this means faster, more personalized execution and processes, greater use and accuracy in data, and improvements in overall customer experience. Marketers shift from fussing over bid adjustments and budget allocations to higher value add, human-centric contributions like, “how do we evolve our value proposition to drive more business?” Because of these clear benefits, Forrester predicts that by 2020, 25% of Fortune 500 companies will report hundreds of examples of IPA use cases.

Benefits of AI + Automation for Marketers

IPA technologies not only surface insights for marketers but actually turn insights into action. For example, Albert can synthesize historical digital campaign data across channels, craft strategies for execution, and explore different combinations of messages, creatives, and frequency across audiences. Evolving relentlessly over time, the intelligent machine’s autonomous capabilities allow it to actually shift budgets, adjust bids, audiences and optimize campaigns 24×7 in relentless pursuit of KPIs that a marketer has set.

This is especially important as customers continue to demand more from brands; Salesforce’s Fifth Annual State of Marketing Report revealed that 53% of customers now expect personalized offers, and 62% expect businesses to anticipate their needs. IPA technologies are becoming the only way to deliver personalized touchpoints for an optimal customer experience across paid digital channels.

Key Takeaways

Implementing intelligent process automation can help marketers achieve better paid digital campaign results while surfacing customer, media and market insights that inform not just marketers, but the overarching business strategy. In the face of potential economic slowdowns or other unforeseen external market conditions, marketers are finding that new technologies like IPAs can help them innovate, scale and increase efficiencies to stay competitive.

To learn more about how Albert plans, executes and optimizes paid digital marketing campaigns, contact us.

How Can You Engage Customers Through Powerful Digital Experiences?

Each of your customers is unique, and each has different expectations and needs when it comes to experiencing your brand. Here’s how you can offer every one of them a unique digital experience.

“Customer experience” is a term that practically everyone in the business world is using, but is frustratingly difficult to define. Some might think of a seamless smartphone interface when asked to explain what a positive customer experience is, for instance — others might focus on the customer service side of a brick and mortar retail space.

But the fact of the matter is that defining customer experience by a singular interface ignores a fundamental truth about today’s omnichannel market. A customer’s experience isn’t just defined by how much they like your store, or how easily they can access your website. Rather, it’s a grand total of all their interactions with your brand, whether they’re visiting you in person, on your website, or via social media, or reviewing their invoice, or chatting with a customer service representative. As HBR contributor Adam Richardson points out, customer experience isn’t just a snapshot in time: it describes the entire arc of that customer’s engagement.

That being said, digital media is constantly giving us new ways of understanding and engaging with customers, raising their standards for experiences of your brand every day. Even if you already have the in-person experience down to a science, without the proper digital tools, customers will come away feeling that they haven’t received the best possible service. Here are a few suggestions from Lithium Technologies VP Dayle Hall that can help your company deliver the kind of high-quality digital experience your customers are looking for:

1) Be An Active Listener

One of the first things Hall notes in his article is that simply listening to what your customers say isn’t enough — it’s only by engaging in social listening that brands can set themselves apart from the competition. When customers take to social media to voice their feelings or ask a question, writing back goes a long way towards making customers feel like they’re really being heard, even if it’s just to say “thanks!”

2) Create a Conversation

If you’re really looking to curate an impactful online experience, it’s critical that you do the proactive work of creating a conversation with your consumers rather than passively waiting for them to interact with you.

Remember, though, that a monologue is not a conversation. Just posting articles or writing tweets won’t do you any good if there’s no one on the other end who wants to respond to them. This is where the listening can help you: if you’ve been paying attention to the kinds of content your customers want to see, it’ll be much easier for you to encourage debate, prompt questions, and provide them with valuable, personalized insights.

3) Keep Customers on Their Toes

In a seemingly-endless digital sphere, consumers are constantly deluged with new content. That means they’re much harder to surprise and delight, as Hall puts it. In order to create a positive experience, you’ll first have to create a memorable one. Before engaging in any digital action with consumers, Hall recommends asking yourself “will this digital moment make a customer smile?” The key, as with most marketing, is generally to stick to your guns: curate a unique voice and perspective for your brand, and if it’s original and endearing, everything else should follow.

4) Always Have a Plan B

In a perfect world, you’d never have to turn the tide of consumer opinion — in the real world, it’s critical to have a backup plan in case your PR strategy goes awry. The amazing thing about the internet is that you can respond in real-time to consumers, so the minute things get hairy, your first line of defense will always be the digital sphere. Always be ready to turn a not-so-great news story into a positive customer experience.

Powerful Platforms for Powerful Digital Experiences

Of course, when you’re looking to conquer the digital space, it’s helpful to have a high-tech digital solution in place. For the best results, AI-powered marketing platforms like Albert create powerful digital experiences by hyper-targeting user segments and catering content based on their specific needs and behavior. By leveraging the use of autonomous digital tools with the ability to detect trends and insights that are all but invisible to the human eye, you can much more efficiently and seamlessly create a positive customer experience, even in today’s crowded media landscape.

Just How Critical Is DX to Your Brand?

A good digital experience (DX) is one of the most important components of a modern marketing strategy, but it’s difficult to achieve without the help of a powerful AI tool.

Though there are countless other examples spanning just about every market niche imaginable, Uber is perhaps the quintessential case of an exemplary user experience compensating for an unremarkable product. Despite offering more or less the same core service as taxis and other car-for-hire outfits at a similar price point, Uber has managed to go from small startup to $100 billion industry-leader in the course of a single decade.

This incredible success can largely be attributed to the usability of Uber’s app and the top-of-the-line brand experience the company delivers to its customers. Stories like Uber’s drive home the point that, in today’s marketplace, a good user experience is just as important as a quality product and competitive pricing.

A Deafening Digital World

The value of a top-notch user experience has been heightened greatly by the rise of digital tech, which has greatly increased the portion of time consumers spend engaging with media. Research indicates that the average smartphone user touches their phone 2,617 times per day, a figure that can rise to as high as 5,400 touches among particularly active users. As such, a poor or unmemorable user experience gets quickly drowned out by the constant barrage of information to which the modern consumer is exposed.

In order to survive and thrive in this kind of landscape, companies have no choice but to create a comprehensive, engaging digital experience (DX) for each and every one of its potential customers. Building a brand involves more than an eye-catching logo and an attractive core offering: it’s about articulating a purpose, eliciting emotions, delivering interactive experiences, enabling new behaviors, and inspiring social connections.

Collectively, these brand-building endeavors amount to the creation of a fully-realized digital experience, and are often the decisive factor in a company’s success or failure.

The Inherent Complexity of Modern DX

A good DX is not restricted to a single, make-or-break marketing touchpoint, however. In fact, it covers a customer’s entire experience with a brand in the context of their unique digital environment, from online ad campaigns, to the company’s website, to reviews on third-party sites, to text or email purchase confirmations. Consumers do not experience any brand in a vacuum, and marketers must take all of the digital noise occurring around the brand’s touchpoints into account when crafting their DX.

Unfortunately, the sheer number of channels and devices on which consumers experience branded content makes it difficult to predict how a DX strategy will play out and undermines your ability to measure its success once it has been implemented. No matter how experienced a marketing team may be, it simply isn’t humanly possible for a team to collect, track, and analyze the millions of data points necessary to define specific customer journeys across digital channels.

Building a Strong DX with AI

Thankfully, cutting-edge tools like Albert™, the world’s first fully-autonomous marketing platform, can help marketers overcome the obstacles presented by today’s oversaturated digital spaces, as well as craft and deliver a high-quality DX to each unique customer.

By leveraging his advanced artificial intelligence capabilities, Albert organically understands customer journeys across all channels and devices. That liberates marketers from much of the time-consuming busywork that holds them back from generating more compelling creative materials and producing a DX that will stand out from the crowd.

Ultimately, as Uber and others have shown, modern marketing comes down to quality of interactions more than quantity of touchpoints. An engaging DX is the central component of a quality-over-quantity approach to marketing, and is therefore absolutely essential for any brand hoping to establish itself as a leader in an increasingly overcrowded marketplace.

What We’ve Discovered About Implementing Autonomous AI

At Albert, we can already see that in the very near future, all paid digital campaigns will be executed by autonomous artificial intelligence (AI) guided by marketers. Through our experience helping Fortune 500 clients on their journey toward overall business transformation, we’ve discovered the following insights that might be of interest to brands who are thinking about implementing an autonomous, cross-channel AI marketing solution like Albert.

Why Collaborate With A Machine?

People have unique strengths. We can empathize, be creative, or be inspired to come up with ideas seemingly out of thin air. On the other hand, an AI-powered machine can transact, iterate, predict, and adapt to changing conditions at a pace beyond our comprehension. When marketers collaborate with an intelligent machine, the AI empowers them to expand their reach far beyond the capability of traditional advertising technology. Today, a machine acting intelligently on behalf of marketers can paradoxically enable a brand to present itself as more human, personalized, and flexible. 

When marketers implement autonomous AI, it means that instead of operating the technology, they start to collaborate with it. This often involves shifts in a team’s mindset — especially for those with sophisticated domain expertise. Machines take a different path than humans would, because they can focus on everything at once, as opposed to the way we work, balancing just a few variables in order to find performance. As a result, it is important for brands adopting AI tools to ensure that implementation not only integrates technology but people as well.

Digital Marketers Get To Shift Their Focus

Marketers who adopt autonomous AI are not always prepared for subsequent shifts in thinking required by the human team. Insights gleaned from an AI tool may surface learnings that shed light on previously undiscovered patterns or insights that change a team’s priorities or strategy. For instance, what does it mean that the machine uncovered a new audience the marketers didn’t know they had? Or what are the implications of hundreds or thousands of long-tail terms that the machine is able to make perform profitably? When brands adopt AI to take on rote, repetitive campaign management chores, marketers find they are able to use their traditional marketing strategy and creative skills to unearth the meaning of the AI’s discoveries, find stories in the data, and craft compelling value propositions from them.

Surprise Becomes a Way of Life

Brands often encounter startling revelations when first implementing autonomous AI. For example, rather than crunching campaign data, more energy starts to be spent on making decisions related to what creative elements should be provided to the AI as well as thinking about overall campaign direction. Because the AI tool’s cross-channel capabilities deliver messages to the right person at the right time, human expertise can be redirected from a narrow focus on bid, audience, and budget allocation tactics to asking more strategic questions such as: are my messages resonating? And, is my overall marketing working?

Additionally, the rate of creative fatigue can be eye-opening for brands. Because the machine offers scaled capacity to efficiently test creative iterations at a pace that was never before possible, teams get to test as many variations and ideas as they can create. Marketers find suddenly they can test and learn more quickly, understand when to use a rational or an emotional proposition, and develop ever more learning agendas to feed the intelligent machine. Unlocking actionable insights and discoveries often steers teams to try new entirely creative techniques and tactics.

Similarly, the process of reviewing campaign performance speeds up to real-time from once a week (or month). And suddenly, those reports are actionable. In pursuit of performance, the AI may request more budget in one place while limiting spend in another. Or, the system may inform that engagement with one audience is increasing far beyond another, so it begins looking for more of the same in other ways. Marketers are on point to think about what opportunities this creates for other parts of their business.

AI Implementation Takeaways:

Overall, we’ve found that implementing an AI creates new vistas for marketers to return to their roots: storytelling, using creativity, and building value propositions that resonate with consumers.

While the implementation is fast, learning how to fully utilize an autonomous AI to deliver on the promise of business transformation takes time. But, with adequate planning and preparation of staff, companies can speed up their path toward successful human+machine collaboration.

To learn more tips about autonomous AI, download this commissioned report conducted by Forrester.

Machines Are Fixers; Humans Are Visionaries

I recently sat on a panel with Amy Hu of H&R Block and Andrea McCullough of Dunkin’ Donuts about how machine learning impacts martech. The moderator asked, “Has artificial intelligence actually been achieved, or is what we’re seeing in the market right now machine learning?” Amy was adamant in her response, “No, it doesn’t exist,” to which I followed with an emphatic “Yes, it does.”

With the World Economic Forum now predicting that machines will do more workplace tasks than humans by 2025, it’s difficult to deny that artificial intelligence (AI) exists. But it’s also often difficult to recognize AI, even when it’s right in front of you.

When the first artificial intelligence business solutions began emerging a few years ago, I remember someone saying, “People expect artificial intelligence to feel magical, like a laser show or something.” This isn’t usually the case, of course. Currently, most early business adopters are using machine learning and AI to address legacy issues that their industries haven’t been able to.

In marketing and retail, these legacy issues stem from a convoluted digital landscape that continues to evolve faster than brands and marketers can keep up with (e.g., too many channels, too many devices, too much data and too little transparency).

Additionally, there are internal issues that create unnecessary complexities. Brands’ tech stacks are often convoluted and unmanageable. They have disparate data sources across departments, channels and vendors. They’re often working with multiple vendors who aren’t interacting with one another. And because processes are so manual, it’s difficult to scale them significantly without scaling resources.

Against this backdrop, artificial intelligence’s inevitable first job with any company is to act as a fixer. From there, its next job is to drastically scale the efforts the company had put in place pre-AI.

Amy Hu of H&R Block described their use of machine learning as “fast-paced technology, but old-fashioned marketing.”

Andrea McCullough shared how Dunkin’ is using machine learning to recommend products to guests, increase sales and build out their loyalty program. A customer stepping into a Dunkin’ Donuts, for instance, might receive a notification asking, “Would you like a blueberry donut with that coffee?”

In other words, Dunkin’ Donuts is using some of the most cutting-edge technology in the world to perfect the age-old art of the upsell. This is something that goes back to 19th-century retail when proprietors knew their customers and could predict what they might like. The better the prediction, the better their business. This fundamental philosophy hasn’t changed; it’s just taken on a different format and must be deployed at scale.

Businesses are using AI and machine learning to scale the things they’ve always done at a level they never imagined possible (say, running 20,000 campaigns simultaneously). From an outside view, this might not look cutting edge. It seems like more of the same. A lot more.

For brands like H&R Block and Dunkin’ Donuts, advanced technologies are enabling a necessary course correction. In the process of solving problems that humans aren’t equipped to address manually, AI is returning brands to their human roots.

For marketers and retailers, this means a return to strategy — to dreaming up campaigns or experiences for new audiences the AI has discovered. It’s ramping up creative output, enhancing consumers’ engagement with their brands, exploring new ideas and, generally, unearthing themselves from the manual, data-centric and technical matters that had them acting more like robots than humans for the last decade.

Machines aren’t visionaries, after all. CMOs, CEOs, marketing people — they come up with the vision. They come up with the experience. The AI platform then does the work. This is where the AI stops and the human starts.

As AI games researcher Mike Cook said in response to human gamers’ recent victory over an AI when playing eSports game Dota 2, “The bots are still very good at moment-to-moment, but they seem bad at macro-level decisions.”

It’s important to note, however, that man and machine are not at odds. Humans will continue to lead vision. AI will step in to fix the things that humans broke while trying to execute on that vision alone. And they’ll do it in a way that’s both highly complex and totally simple, giving their human counterparts the luxury of not having to do the work of machines.

In a way, relying on machines to be more human is as cutting edge as it gets. More cutting edge, even, than a laser show.

Article previously featured in Forbes, Machines Are Fixers; Humans Are Visionaries, October 26, 2018

Why AI Increases Demand for Creativity in Marketing

AI is freeing marketers to spend more time doing the work they do best.

As adoption of artificial intelligence (AI) continues to rise across industries, marketers are already discovering that the technology is creating more opportunities for them to do the fundamental work of marketing: improving customer experience and growing their business. AI works incredibly quickly, and it’s unearthing connections and insights from raw data that would take humans weeks, months, or even years to discover. It’s also applying these insights, proving them out, and then sharing them with human colleagues to apply throughout their businesses.

But while AI is great at learning patterns, humans still do creative best. As a result, the growing role that AI plays in marketing will not just drive efficiency by automating the execution, allocation, optimization, and attribution of digital campaigns, but create demand for more creative and strategic work. In effect, this coming shift to the marketing industry will require productive collaboration between marketer and machine.

Smarter AI Will Require More Creative Content

The result of this interplay between marketers and their AI colleagues is that the implementation of AI actually requires more creativity from human marketers. As AI has become a more powerful force in modern marketing, it has already shifted many marketers’ focus from toiling in spreadsheets to designing strategies and creative material inspired by the data in those spreadsheets, then sharing transformative insights with colleagues across their businesses. In other words, it’s placing an emphasis on the high-level work that most marketers joined the field to do in the first place.

That’s largely because AI platforms will always require creative input from humans. For example, AI marketing tools like Albert can use real-time data to target consumers with creative content tailored to where they are in the funnel and what kind of buyer they are. However, they can’t do any of that without a human marketer translating business goals into terms the machine can understand and generating creative elements it can utilize in campaigns.

And because AI tools’ cross-channel orchestration capabilities facilitate many more effective contact points between consumers and brands, creative fatigue tends to occur faster. This in turn requires the exploration of multiple creative strategies and ever more approaches to fresh, engaging content.

AI Makes Reaching Users Easier

AI also garners valuable insights about the customer experience, allowing marketers to determine what customer behaviors are predictive of churn, what customer experience actions have been successful or unsuccessful, and which prospects brands should interact with to increase conversions and retention. From that information, brands can create new solutions and products to address consumer needs that they never would have otherwise realized existed.

AI Is the Future of Creative Marketing

AI technology is inspiring marketers with the power to execute and manage customer experience at a scale that was previously impossible. As a result, the demand for innovation and creativity from humans will continue to increase. Luckily, creativity and making connections is something humans excel at. With AI as our data and orchestration partners, it looks like marketers are ready to meet the challenge of today’s empowered consumer.

Why Performance and Brand Marketing Efforts Must Be Aligned

Contrary to conventional wisdom, there’s no reason why brands can’t do effective performance marketing and brand building within the same campaign.

As we’ve discussed ad nauseum, performance has arguably become the most valuable currency in a digital media marketplace plagued by a lack of transparency. Gone are the days when responsible spending consisted of little more than handing over a lump sum to your media buying team or agency partner and crossing your fingers. Today, any trust between advertising partners is grounded in quantifiable results.

For most stakeholders, the rise of “performance marketing” has been a net positive. The proliferation of data generated by consumers’ online actions has empowered brands to optimize for lower-funnel KPIs more effectively than ever before. As clicks become harder and harder to come by — the median CTR for display ads purchased through the Google Display Network sits at just 0.46% — the ability to focus on performance-oriented metrics like leads, sign-ups, conversions, and sales cannot be overvalued.

That said, there’s an argument to be made that many companies have taken too strongly to performance marketing — at the expense of their brand. Performance marketers’ (healthy) obsession with tangible results is productive when directed at lower-funnel efforts like retargeting, but it can do more harm than good when directed at upper-funnel brand marketing efforts.

Finding the Middle Ground

Asking a performance-oriented question about a branding effort — say, “How many new customers have we acquired through a Super Bowl ad?” — is like measuring your weight with a ruler; the tool simply doesn’t fit the task. Companies can optimize for metrics like brand awareness and brand sentiment, but not by relying exclusively on a standard performance marketing toolkit.

Aligning performance and brand marketing efforts can be an enormous challenge. Many organizations are tempted to dismiss performance-oriented brand marketing efforts as impossible, choosing to dedicate separate campaigns to each respective end goal. But the truth is that such campaigns are possible — they just require a firm commitment to collaboration and compromise.

Compromise Is Key

For a company’s performance marketers, this compromise begins with acknowledging the value of upper-funnel brand-building actions. These actions tend to be harder to evaluate than the lower-funnel actions they’re used to, but these marketers should make an effort to cross the divide; they have tremendous value to add to any brand-building campaign.

One key asset performance marketers can contribute is their talent for personalization. While maintaining a coherent brand identity remains as important as ever, there’s an immense upside to providing each customer with a unique brand experience — a different perspective of the same core “brand object,” so to speak.

Performance marketers are optimally equipped to figure out which audience segments respond to which pieces of content. Their goal shouldn’t be to dictate the shape of a company’s brand identity, but rather, which threads of a brand narrative are best-suited to which audiences.

Conversely, a company’s brand marketers must become comfortable with tossing aside their “gut instinct” in favor of their more performance-minded colleagues’ empirical evidence. Human intuition still has a critical role to play, especially when it comes to crafting effective creative materials. Still, brand marketers must learn to face the numbers and accept when their pet brand-building initiatives aren’t as effective as they intuited they would be.

Artificial Intelligence, Genuine Solutions

The takeaway here is simple: brand-building and performance marketing should not be treated as distinct activities, but as two mutually-informing sides of the same coin. A company should optimize its media mix by taking a comprehensive view of its marketing activities, not by evaluating its upper-funnel and lower-funnel KPIs in isolation.

The challenge is doing this at scale, in real time. Customers are won and lost in the blink of an eye in the digital age, and companies simply can’t afford to wait for their brand and performance marketing teams to work through their differences at every juncture of a campaign. That’s where a tool like Albert, the world’s first autonomous marketing platform built from the ground up on artificial intelligence, comes into play.

By leveraging sophisticated machine learning algorithms, Albert is able to pilot thousands of micro-campaigns simultaneously, helping companies gain insight into which pieces of their content work and which ones don’t — the perfect marriage of brand- and performance-oriented thinking.

For example, Albert might notice that one campaign is performing exceptionally well with a certain audience segment and suggest a strategy for delivering similar content to that segment in the future. Alternatively, he might notice that a piece of creative is underperforming regardless of its contextual deployment and inform a team of marketers that the creative material behind it has probably become a bit outdated.

A tool like Albert provides companies with a remarkably easy way to ensure that their brand and performance marketing teams have access to the same information at the same time, which is the first necessary condition of cross-team alignment — and ultimately, of better results.