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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

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.

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.

Misconceptions about machine learning (ML) are running rampant among business leaders, and hindering organizations from effectively using and seeing results from transformative technologies. A new report from Forrester demystifies what ML really is and how to differentiate between complex terms. These are a few important takeaways you can use to get literate in machine learning and avoid the most harmful misconceptions.

Machine Learning is Misunderstood

Because there are so many complicated terms being used sometimes interchangeably or incorrectly – like neural networks, deep learning, and artificial intelligence – it can be very difficult to understand what machine learning really is. Forrester defines machine learning as “applied statistics and other algorithms to identify probabilistic relationships in data.” ML is not a number of things: it is not computers learning to think and make decisions like humans, nor is it all about predicting the future. In reality, ML is about identifying patterns in data. Depending on the context, and when the future resembles the past, machine learning can be helpful with predicting the future – but it’s not always prescriptive.

Best Tasks for Machine Learning 

ML is best used for ingesting and analyzing large amounts of data at scale and across multiple sources that are too complex for people to handle. However, ML struggles in situations where the data is noisy, limited, or when human judgment and reasoning is required. Optimal use cases for ML include: 

• Classification: categorizing observations
• Regression: predicting a continuous range
• Clustering: sorting data into groups based on similarities
• Anomaly detection: pinpointing exceptions
• Association rules: if-then scenarios

Good ML models will be less biased than most people, but ML is only as objective as the data that people provide it. As a result, it’s important to be aware of the bias that may exist in your data and take steps to minimize it in order to avoid letting the technology inherit historical inequities.

Machine Learning is not a Black Box

A fear of incorporating machine learning into business processes often stems from the incorrect assumption that ML models are too complex and that the rationale behind its decisions can’t be explained. The reality is that ML is more transparent than people – it’s just harder to explain. According to Forrester, “People are excellent at explaining their decisions, but their explanations frequently have little to do with their actual decision-making processes. The reverse is true of ML models”. Explaining ML models requires high ML literacy, as well as clear communication from data scientists. If an organization lacks either of these aspects, it can be difficult to grasp the machine’s decisions. 

Propel Your Business Towards Growth

Incorporating AI and ML can propel businesses towards incredible growth – but it requires a deep understanding of each in order to succeed. Download Forrester’s report to learn the Seven Myths of Machine Learning and enhance your machine learning IQ. 

Google announced recently that it will bolster privacy protection online by joining Safari and Firefox in blocking third-party cookies in its Chrome web browser by eventually phasing them out “within two years.” This is mostly relevant for retargeting and attribution outside of Google and Facebook’s platforms.

Unlike many ad tech solutions, Albert has never been reliant on third party cookies to deliver performance because the platform is built to combine ongoing analysis with execution.  For example, instead of being dependent on cookies for attribution, Albert uses continual lift testing to find the effect of campaigns on bottom-line revenue. This is a far more robust methodology than using cookies to track conversions. 

Albert is architected this way because the system is responsible for the allocation of budget. In order to do this well, it is critical Albert has the best data to understand at any moment what is working. This is the difference between a platform that actually controls execution and those that are designed to assist in campaign management or simply provide attribution reporting. 

While the next two years may bring uncertainty for much of the ad tech ecosystem, it does not for us. Albert is built on top of Google and Facebook and operates each publisher’s tools far beyond the capability of humans. We anticipate that Google and Facebook will develop and roll out new solutions within their platforms for tracking on the open web. As they do that, Albert will immediately be positioned to take advantage of them as a preferred tech partner of both publishers. Alternatively, if clients would rather use other new technologies for attribution or verification, the data will be able to flow into Albert in the same way we are able to receive it today.

To meet customers where they are in their journey, more marketing and creative teams are delivering “atomic” ad elements.        

Meet Your Customers Where They Are

In today’s increasingly complex landscape, marketers are challenged to meet customers where they are in their journey and deliver personalized experiences. According to a study by PwC, 73% of people report that customer experience is a key factor in their purchasing decisions. Despite this, only 49% of U.S. customers feel like they have a positive experience. If you aren’t delivering personalized customer experiences, you need to find a way to quickly keep up. Artificial intelligence can help close the customer experience gap by making it possible to create tailored, relevant customer ad experiences through atomic creative.

What is Atomic Creative?

Gartner defines atomic content as “dynamically, and often in real-time, combining different content ‘atoms’ to create a more relevant overall marketing asset and experience that specifically meets the needs of the recipient based on where they are on the customer journey.” These “atoms” are customizable, reusable content elements, such as copy, images, videos, or CTAs, that can be assembled into whole assets. Because atomic creative involves creating ad elements once and reusing them, it can be a huge time-saver for marketers. More importantly, the overall benefit of atomic creative is delivering tailored, relevant customer experiences that deepen relationships between audiences and brands, and in turn, increase conversions and loyal customers.

How to Develop Atomic Creative

Developing an atomic creative strategy requires a comprehensive review of data from previous touchpoints and interactions in order to understand what ads have resonated with your target audience at different moments. Marketers, creatives and customer experience professionals need to be hyper-focused on what they want to test and optimize. To effectively personalize ads for different segments and journeys, teams need to bring an experimental mindset to mix and match creative development so they can learn what works. In addition to identifying effective marketing propositions across key touchpoints, teams can discover that the customer journey itself may not be what they thought. To that end, it’s important to experiment with different value propositions that can reveal previously unknown patterns in the customer experience.

Get by With a Little Help From AI

All of the analysis, testing, and optimization that is necessary to deploy atomic ads and meet customers where they are is impossible without advanced technology. Artificial intelligence tools can parse through assembled ads while pulling in different data sets such as audience and budget to provide a holistic view of the customer journey. In Forrester’s new report, AI is a New Kind of Creative Partner, Analyst Thomas Husson writes that “content intelligence – defined as the use of AI technologies to understand and capture the qualities inherent in any content (its emotional attributes, subject matter, style, tone, or sentiment) – is crucial to improving engagement throughout key moments of the customer journey.” Armed with a deeper understanding of the customer journey and what audiences are engaging with, marketers can accurately pinpoint creative elements and messaging that delights consumers.

Even the most data-driven marketers don’t have the capacity to test as many permutations and combinations of ad elements at the pace and scale of a machine. AI tools can autonomously conduct multivariate tests by exploring combinations of creatives, headlines, copy, and more, to identify winning combinations of atoms that deliver higher engagement. Marketing and creative teams can leverage these rapid tests and become capable of answering ongoing strategic, creative, intuition and emotion-related questions. They find they can learn faster and produce exponentially better outcomes together with an autonomous AI than either human or machine could alone.

Surprise: AI Makes Your Creative More Human

Brands that prioritize agility and innovation in their marketing and advertising strategy will leap ahead of the competition. AI can be the fuel for delivering atomic creative, and marketers who incorporate insights from AI into their creative processes will see higher engagement. Leveraging AI as a creative partner will be the key to delivering relevant, targeted, and personalized ads throughout the customer journey.

As Fortune 500 companies continue to explore more efficient avenues to grow revenue, reduce costs, and improve customer experience, their understanding of the role of artificial intelligence in digital advertising is evolving. Based on our experience guiding clients through their AI adoption journey, this is how we expect marketers’ attitudes towards AI will shift in 2020. 

Marketers stop putting up with AI-washing

Gartner reports that artificial intelligence in marketing has reached the peak of inflated expectations. This is good news for solutions that are not AI-washing their existing offerings, as well as for marketers and advertisers who have already been burned by technology that does not deliver on its promises. In 2020, wary marketers will begin to cut through the AI hype by asking vendors more specific questions about how and why AI is deployed to solve the problems their solution claims to address. Rather than quickly adopt a new tool based on buzz, marketers will insist on understanding why the traditional way has failed until now and why a new approach is required. Marketers will no longer accept pitches from vendors who only provide vague answers about using AI “because it’s better” or worse, that it’s “AI magic.” They will insist on understanding how it solves problems in ways they never could have in the past.

Solutions that layer on “AI frosting” will continue to disappoint 

In a commissioned study conducted by Forrester on behalf of Albert, Forrester found that AI adoption in marketing jumped from 43% in 2016 to 88% in 2019. By the end of 2020, we expect that 98% of marketers will be using AI. However, the full benefits of AI are still elusive to many because 3 out of 4 marketers are implementing assistive AI, meaning the AI is layered on top of an existing/traditional application to surface recommendations and insights. This leaves it up to humans to take action. The true value of AI is delivered when it is combined with Robotic Process Automation (RPA), so the solution can take action autonomously in real-time. Throughout 2020, we anticipate that more marketers will seek out technology that combines AI with execution capabilities as they chase truly transformative impact. 

In-house marketers embrace human + machine teams 

Facing economic constraints as well as pressures to stay competitive, B2C marketers who have been testing AI on a smaller scale will be driven to pioneer autonomous AI solutions. These brands will discover that the key to success involves creating human+machine digital advertising teams where artificial intelligence handles massive multivariate testing and repetitive data-driven adjustments required to deliver KPIs. At the same time, marketers will begin to collaborate with the tech to create learning agendas designed to answer ongoing strategic, creative, intuition and emotion-related questions that the machine alone cannot. Results will prove that these hybrid teams produce exponentially better outcomes than neither human nor machine could produce on their own.

Innovation in marketing will come from humans, not tech

As increasing numbers of marketers adopt autonomous AI, they will discover that that the road to digital transformation is paved with questions. In the face of consistent positive results, marketers will become more comfortable with machine colleagues pulling the levers of execution and will let go of concerns about why the machine did one thing and not another. This shift will open the door to questions about audience, creative and budget insights gleaned by the machine. Newly empowered marketers will begin to think about ways to have their autonomous AI colleagues test out hypotheses through efficient exploration. Insights will start to come back faster than ever before, creating a cycle of questions, tests, and results that will fuel innovation with a reach far beyond paid digital advertising teams. Human insights supported by machine learning will drive enterprise-wide insights and help marketers reclaim the greater and original function of marketing: building powerful brand and audience connections.

State of In-Housing Today

Thinking about taking your advertising and marketing in-house? You aren’t alone. Brands are increasingly turning in-house for their marketing, advertising, and media needs and are benefitting from it. A recent ANA survey of 412 client-side marketers showed that 78% of respondents have an in-house agency, which is up from 58% in 2013. Much of this increase has happened within the past five years.

How In-Housing is Changing Advertising

While brands are taking different approaches to in-housing, ANA’s report uncovered certain trends about which tasks are being insourced. Brands often begin their in-housing journey with a hybrid structure, as shown in a recent IAB study that found 47% of brands have partially moved programmatic functions in house.

More brands are also moving media planning and buying in-house; in the past 3 years, 26% of programmatic buying shifted in-house. 

In-house agencies often provide strategy, creative, and media planning/buying, but in the past five years, significantly more companies are moving even more functions in-house: content marketing, creative strategy, data/marketing analytics, media strategy, programmatic media, and social.

Benefits of In-Housing

Brands are seeing a number of advantages by insourcing their advertising and marketing. 38% of advertisers polled by ANA cited cost-efficiencies as the number one benefit. Brands are also benefiting from greater transparency, as well as more ownership and control of data. For others, relying on dedicated staff rather than an external agency means faster turnaround times, and a better knowledge of the company’s brand.

Biggest Challenges

Despite these benefits, the road to in-housing isn’t always easy, and many brands who decide to shift resources internally face a number of bumps along the way. As recorded in ANA’s The Continued Rise Of The In-house Agency, the biggest challenges teams experience relate to managing an increased workload and scaling efficiently. This is consistent with a commissioned study conducted by Forrester on behalf of Albert, in which 35% of marketers surveyed who are moving in-house are struggling to scale efforts due to a lack of resources, and 21% are finding it difficult to find and retain talent for their in-house agency. 

75% of marketers surveyed in a Digiday study stated that taking programmatic ad buying in-house was the most difficult. Marketers surveyed also reported search and creative production among the hardest to take in-house. 

AI is Helping Marketers Take Control

Artificial intelligence can solve for these challenges to make in-housing possible. Marketing AI solutions can handle functions related to campaign planning, optimization, and management in paid search, social, and programmatic digital campaigns. Autonomous AI solutions are able to make audience and creative optimizations 24/7, conduct ongoing bid adjustments, and test thousands of creative/keywords. When an AI takes control of digital ad operations, marketers are freed up from rote, mechanical tasks that are better suited for machines and discover they are empowered to return to the greater purpose of marketing – building powerful brand and audience connections. The result? More scalable, efficient, and effective digital ad campaigns, all while having control, ownership, and transparency over customer data. 

For companies considering a transition to in-house, it’s the perfect time to incorporate a technology assessment. Brands in this transitional phase have an opportunity to review current tech capabilities and needs, identify missing resources and new roles required, and uncover knowledge gaps. During this assessment, they may find that AI will be the right tool that can alleviate concerns about transparency, scalability, efficiency, and effectiveness, all while improving results. 

To learn how one brand used AI to help them shift their marketing and advertising in-house, read this case study.

This post is the fourth and final tenet in our series The New Tenets of Digital Marketing.

We began this series with the goal of uncovering the key challenges marketers face in their quest to deliver the ultimate customer experience in the post-digital era. The divergence of digital channels, the fracturing of platforms and data, and the disruption of marketing processes caused by evolving technologies put new demands on marketing teams, exhausting bandwidth and sidelining marketers’ ability to focus on the function of marketing: building a powerful brand and audience connection. The marketer’s field has fundamentally changed, and yet, many are approaching these new challenges with an obsolete playbook. 

At Albert, we make it our mission to uncover what is driving change for our clients and to identify the path forward. We’ve found that while mass platforms are reaching saturation and audiences are moving to smaller, endemic platforms, brands are racing to keep up. As audiences fragment across channels and devices, we see a significant opportunity in the long-tail and, inevitably, an increasingly complex and diverse customer journey to conquer. 

In this new environment, marketers need to understand micro customer segments, develop targeted brand stories, and effectively execute cross-channel campaigns. They also need the ability to collect and analyze immense amounts of data in order to make informed decisions, and then take action faster than ever before. 

Taking a look at the overwhelming new demands on marketers, it was clear that the answer would be a new way of doing things – a transformation that would enable marketing teams to increase bandwidth and mind space without having to increase the number of hours worked or the size of the team. We found that opportunity in new autonomous technologies.

Autonomous technologies can take on the heavy lifting of rote, mechanical tasks in order to free-up marketers bandwidth for more strategic projects, such as developing compelling and creative brand ideas, or making small but mighty adjustments to the customer experience, and even pursuing game-changing innovation derived from micro insights. But, from our experience guiding brands, we know that the adoption and integration of new technologies into existing teams and processes is no easy task. It can mean disrupting current models – that were designed to meet current challenges – in order to reimagine the organization of the future and restore the function of marketing. Our roadmap – The New Tenets of Digital Marketing – was borne out of this desire to reimagine; a guide for marketers as they begin this transformation.

In our first tenet, Recruit for Human Intelligence, we address the impact of reallocating responsibilities across humans and machines as well as the greater value such AI collaboration allows. More specifically, we share the crucial skills that will be most impactful to hire for when partnering with autonomous AI platforms. In our second tenet, Winning at Every Stage of the Customer Journey, we outline how AI technologies are being leveraged to knock down data silos and collect, analyze and inform decisions in near real-time. We dive into how connected data enable marketers to better anticipate what customers want, where to reach them, and what message to deliver – for greater return. In our third and final tenet, Unlocking Cross-Platform Insights, we uncover how marketers are effectively leveraging AI technology in an increasingly crowded ecosystem, to tap into new segments, uncover opportunities for growth, and to deliver better end-to-end customer experiences than ever before – pushing the boundaries of creativity and innovation within their organizations. 

The New Tenets of Digital Marketing aims to deliver both a vision for change and the direction to achieve it. And ultimately, to better equip marketers in the face of an evolving digital ecosystem. Following these tenets, marketers can deploy the right teams to win the machine + human opportunity and will learn how to leverage autonomous AI to maximize data use and application, boosting the pursuit of customer obsession. Most importantly in our view, they will be able to reclaim the greater and original function of marketing: building a powerful brand and audience connection. 

This post is the third tenet in our series The New Tenets of Digital Marketing.

The emergence of digital channels, the development of advertising technology, and the evolving role of data have changed the marketers’ playbook. At Albert, we set out to define the new tenets of digital marketing in order to help guide marketers across this developing landscape.

In the first tenet, we addressed the key marketing roles that CMOs need to retain and develop in order to thrive. In the second tenet, we looked at how marketers need to think about data and technology in order to deliver across an increasingly complex and divergent customer journey. In our third and final tenet, we’ll look at how marketers are leveraging new, autonomous technologies in order to overcome the challenges and silos endemic to today’s advertising ecosystem.

In this post, we’ll share three ways we’ve begun to unlock the potential of AI-insights to improve the customer experience.

Uncovering New Audiences
One of the most difficult challenges for established brands is finding new audiences and tapping into incremental revenue streams. Given the unbounded reach of digital channels, paid advertising provides access to untapped markets, but at what cost?

Historically, attempting to leverage ad spend to uncover new audiences has proven to be both a costly and time-consuming undertaking. Even with new ‘look-alike’ tools, the process continues to be a resource drain. Marketers spend significant amounts of time manually building out custom audiences across different platforms and considerable spend testing against each audience.

Sophisticated, autonomous solutions are now taking on the heavy lifting – analyzing data, identifying new opportunities, and executing – in a way never before possible. The impact to marketers – new and powerful insights that are changing how marketers identify, connect, and build relationships with their customers.

In a recent engagement, a company that offers gifting experiences in Australia – such as dinner for two with a private chef, or scaling the Sydney bridge – worked with Albert to run targeted ads across global audiences. Leveraging its ability to collect and analyze mass amounts of data and an unbiased, performance-focused approach, Albert located well-converting audiences in the U.S. This was a market the Australia-based company had never considered viable. Albert continued to spend against these new audiences in order to optimize conversions and drive incremental revenue for the company, but the impact of these insights went beyond executing efficient ad spend.

After discovering the new segment – U.S. gifters to Australian recipients – the company’s marketing team tailored creative to target and attract the U.S. audiences. Combining the AI-driven insights with the marketers’ creativity, the new creative built on the momentum of the spend and the company was able to improve the audiences’ customer experience with the brand and drive conversion for the new audience.

Identifying Cross-Sell Opportunities
A deep understanding of the audience and needs is required in order for marketers to deliver more personalized, relevant and impactful customer experiences. Marketers have long looked to data for insights and direction, but, to date, this vision has not played out as hoped. Recently, however, more robust platforms have been able to leverage sophisticated data analytics to uncover new customer insights to inform not just efficient ad spend, but targeting, messaging, and digital marketing efforts.

Running paid advertising for a major department store, Albert discovered that the highest performing segment was young parents. Given that the department store sold products for babies, children, and young adults, this insight did not appear to be particularly revolutionary. However, upon further analyses with the retail merchandising team, the company discovered that these young parents were only buying for themselves at the department store and they were going elsewhere to buy products for their children. This represented a significant upsell opportunity.

In order to capture the full opportunity, the company’s marketing team pursued a more holistic marketing approach. They developed a targeted upsell campaign to highlight children’s clothing, toys, and other parenting-related products. Beyond targeted ads for this segment, Albert suggested more personalized product recommendations on the company’s website, a restructuring of key landing pages to drive awareness of kids’ products, and a revamp of the brand’s content strategy on organic social to reflect kids’ products in key seasonal moments.

The impact went beyond driving conversions from paid advertising and enabled the company’s marketing team to build an integrated marketing strategy for this high-value segment.

Informing Product-Market Fit
The market is forever evolving and staying competitive means being connected to a constant stream of new market information. Historically, companies have looked to market research, focus groups, customer surveys, and other time-consuming tools to gather insights on product-market fit. Today marketers are accessing new data insights from autonomous platforms and these insights are providing the ability to drive significant brand transformation.

Working with a major telecom company on their paid digital advertising initiatives, Albert uncovered an interesting data point – ‘live advisors’ appeared to be one of the most compelling features for the small business owners segment. Intrigued by this insight, the telecom company decided to perform additional market research and through the development of a health brand study and information gathered from on-the-ground salespeople, they confirmed the potential competitive advantage that ‘live advisors’ represented. In response, the company revamped its product messaging and promoted ‘live advisors’ as a hero feature, even developing a larger-scale program around this feature to entice first-time customers with a discounted rate.

Taking the insight beyond online initiatives, Albert and the brand team developed the idea to launch pop-up programs in high foot traffic areas in order to provide offer small business owners with free advice on matters inside and outside telecom services. In addition to developing greater brand awareness and consideration for the telecom company, it differentiated the brand from the competition as an on-the-ground and in-the-know small-business telecom provider.

Untapped Potential
The ability for marketers to leverage autonomous AI in order to better identify and understand their audience has profound implications. From uncovering new audiences and revealing brand growth opportunities to empowering marketers with the insights to better understand and deliver on customer needs.

As demonstrated in the case studies above, the progression of autonomous technologies has begun to transform not only the digital advertising landscape but has proven its ability to drive impact in the offline world as well. Pairing autonomous platforms together with human intelligence has enabled marketers to expand their understanding of customer and audience.

We have only just begun to tap into the potential of autonomous technologies and vast, uncharted opportunities are still out there and waiting to be unlocked. As marketers continue to integrate autonomous technologies into their marketing processes and to leverage its apparent limitless ability to produce meaningful insights, we will see marketers’ ability to deliver on the customer experience and drive brand affinity improve.