Why AI Transformation Is Digital Transformation, Fully Realized
When the concept of digital transformation in marketing moved beyond early adoption and hit critical mass earlier this decade, marketers shared a vision of seamless digital technologies that would replace their cumbersome manual processes. Their thinking was simple: “What I’m handling manually right now, technology will digitize for me.”
So they reviewed their channels and tactics from top to bottom to determine where technologies could create efficiencies, and what it would look like operationally and culturally to get their teams to adopt these new systems. Because hindsight is 20/20, we now know that they ultimately swapped their cumbersome manual processes for a series of cumbersome digital systems.
Marketers didn’t know it then, but we’ve since learned that digital marketing technology should not be organized into channel silos the way we’ve traditionally organized teams. We now know that data collected from one channel needs to inform efforts in every other channel and that technologies that were introduced as channel-specific tools now need to work across entire organizations — something even the marketing clouds have trouble with.
Because of the way marketing technology has evolved, marketers are left managing very complicated tech stacks comprised of multiple technologies, stitched together to complete what should be seamless and interconnected marketing processes. It’s no wonder that even though companies have more technology at their disposal than at any other point in history, only 39% of executives today say they feel they have the digital capabilities they need to compete.
As someone who has spent the last decade reimagining how to process, analyze and act on audience, channel and tactic data at scale, I believe the introduction of artificial intelligence (AI) will be the final tipping point for marketing’s digital transformation — despite challenges that remain. Here’s how.
1. Digitization Will Become Intelligent
One fatal obstacle along digital transformation’s journey to uncomplicate interconnected business processes like marketing is the idea that simply digitizing systems or going electronic would be transformational. The goal post has since shifted to reflect reality: Transformation doesn’t result from merely digitizing manual tasks; it comes from automating entire processes and leaving humans to guide strategy rather than execution.
This requires handing over data processing, analysis and pattern discovery to intelligent machines that can autonomously and instantly act on insights. At the same time, organizations must recognize that these remarkable digital systems can only go so far without talented human guidance.
2. Humans Will Apply More Of Their Intelligence
The introduction of artificial intelligence to digital transformation initiatives will result in a dynamic where employees no longer “use” technology, but collaborate with it. Successful AI transformation will be characterized by a symbiosis between man and machine, where each does what they do best and uses their individual strengths to heighten overall levels of performance. Humans, for example, are needed to guide the AI on matters of strategy, brand and customer experience. By doing this, these systems function as true collaborative partners, amplifying their capabilities beyond the limits of a single human or simple automation. When these hybrid human-machine teams learn to interact and experiment, they create entirely new possibilities and outcomes for companies.
3. Technology Will Become Cross-Linked Into Full Processes
Digital transformation efforts have resulted in massive amounts of valuable data, and there are now technologies smart enough to take this data, learn from it and orchestrate campaigns across channels using existing technology stacks. I believe we’ll see many companies fully realize digital transformation by doing exactly this.
The AI will enable marketers to bring together disparate technologies using the ability to ingest and process massive amounts of data and find patterns in the noise that yield unexpected insights and results at lightning speed.
Each of these three steps is, of course, a massive undertaking, and AI transformation certainly won’t happen overnight.
There are a few common challenges we’re seeing that will inevitably delay companies, across all industries and use cases, from realizing full AI transformation.
One such challenge is the seemingly innate human need to tightly control artificial intelligence systems while getting comfortable with them. Giving only half of the control to an AI, however, will only yield half of the learnings. Any AI system requires a certain amount of testing and data accumulation in order to learn and ultimately perform. Though this period of learning can be relatively short, humans tend to become impatient and step in to manipulate the process, rather than letting the machine run. Other times, this is less a matter of lack of patience and more a response to seeing the machine tackle problems in a different way than the human would. That tends to make humans want to control its process.
AI developers must bear some of the responsibility for both of these responses by guiding users through the upfront adoption process. We learned fairly on, for instance, that we cannot be passive technologists — we must be educators, too. Companies’ reaction to this upfront learning curve will set the tone for the rest of their experience, so they must be coached to let the AI go through its process without interruption no matter how tempting it is to step in and guide it.
On the complete opposite side of the spectrum are companies that give the AI too much control without giving it a strategy. When companies view AI as a magic bullet, they often make the mistake of sitting back and not setting parameters that will guide it toward their desired outcomes. Make no mistake: Artificial intelligence is dependent on — and better because of — human intelligence.
Both of these scenarios point to issues that arise when companies don’t recognize that there’s a clear division of labor between man and machine. The first example illustrates a situation where humans are too involved, and the second illustrates a situation where humans aren’t involved at all.
Mastering the fine line of giving up control on everyday execution and retaining control of strategy will be critical to organization-wide AI transformation.
Article previously featured in Forbes,Why AI Transformation Is Digital Transformation, Fully Realized, February 11, 2019
Our New Brand
Today we’re introducing Albert’s new logo and brand identity. This next step marks a significant milestone in our journey, reflecting an evolution of everything that the original Albert brand stood for and all that we aspire to become.
Over the past few years, Albert has experienced tremendous growth. We’re grateful to have become a trusted partner to brands and agencies around the world who agree that there’s greater value in human imagination than the rote processing of information.
Digital marketing teams who adopt Albert discover that the collaboration between man and machine achieves results neither could operating alone. Albert is the leading edge of an entirely new kind of workplace experience. Our customers are pioneers who discover that their partnership with an AI can only be described as having Unprecedented Impact.
That was the inspiration for the new Albert logo and our entirely new look. Inspired by our mission to help digital marketers keep up and win with today’s consumers, this next phase in our journey acknowledges that the AI transformation we are seeing is rapidly moving beyond the early adopters, and that the mainstream is now ready to open their minds to a radically more effective way to plan, allocate, optimize and attribute their digital campaigns.
Our new graphic system is a depiction of impact visualized. Albert is a collection of over 200 skills, all evolving, working in concert, enabling a machine that analyzes the unanalyzable to take purposeful action. We chose a design with elements that permit us to represent this complex collective intelligence and what it can do for marketers.
Behind the new look we’re still the same rapidly growing company and team (of mostly R&D, data scientists and engineers), dedicated to providing our clients with the autonomous AI that becomes their digital marketing ally.
Do You Really Need Artificial Intelligence? How To Decide
There are seemingly thousands of artificial intelligence solutions for marketers — but only four questions marketers need to ask to differentiate between them.
Does this problem really require AI?
Brands should have a defined problem set or desired outcome in mind before considering AI. Their challenge or objective should guide their AI journey — and reveal whether they actually need AI or not.
For example, if creative optimization is the goal, consider whether adding an AI vendor into the mix will change results significantly. For brands with a strict, relatively small creative set, no AI system will help them better understand if changing “click now” to “buy now” will have impact.
On the other hand, for brands with hundreds of thousands of creatives, AI can help sort the performers from non-performers by revealing the signal in the noise.
Vendors should also be able to explain why traditional methods have failed until now, and why AI is necessary to succeed.
What does the machine do — versus what does your marketing team do?
Do you want the AI to step-analyze data and provide you with recommendations you can execute on your own? Or do you want it to take actions on your behalf in pursuit of KPIs?
An AI that helps with decisioning vs. an AI that makes and acts on the decisions in real time operates at two very different altitudes. For organizations that aren’t particularly wedded to one option, an alternative question to ask is, “What level of automation is sufficient to solve our workflow and scaling challenges?”
Will it play well with other systems and data?
AI requires massive data sets to perform best, so marketers will want to be able to integrate it with other systems, giving it access to many datasets created by their efforts. The two questions to ask here are: Can the AI at hand be integrated with your brand’s other systems? And, straight to the point, can this AI be enriched with external data sources?
If the AI can be integrated with existing systems, how long does the vendor’s typical integration take?
Weigh time to market and ability to integrate large datasets against the end benefits among the vendors under consideration.
Who’s building it?
AI is very challenging to build, so it’s important to understand the DNA of the vendor’s team. How much of the team is dedicated to research and development? If, for instance, they have 100 people but only five of them are in R&D, it’s likely they have relatively simple technology.
Also consider who is on the R&D team. Do they have backgrounds from high-profile research universities and have on-the-ground practical AI experience? Or, are even the most senior members of the team learning AI on the job?
AI comes with a price tag, so it’s important for marketers to know exactly what they’re paying for.
Article previously featured in MediaPost’s Marketing Insider, Do You Really Need Artificial Intelligence? How To Decide December 31, 2018
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.
How Agencies, Publishers, and Brands Are All Working to Break Down Programmatic Silos
As ads are bought and sold on an increasingly minute-to-minute basis in an effort to create more personalized experiences for more users, the line dividing programmatic from other types of advertising is blurring. Some companies are even erasing it completely.
This December, the New York Times collapsed its programmatic sales department into the larger sales team as one part of a broader reorganization, Digiday reports. While their team had traditionally consisted of two parts — direct and programmatic sales — the new arrangement renders this distinction completely moot. The Times is only one of the many publishers, agencies, and brands who have begun to recognize programmatic as the de facto channel for the buying and selling of ad inventory.
Buzzfeed is another notable example of this trend. Earlier this year, the content aggregator announced that not only would their programmatic team be joining the larger sales department, but also that their programmatic inventory would be opened to the entire sales team. A long-time programmatic holdout, BuzzFeed’s move into automated ad purchasing indicates the skyrocketing importance of digital media sales. It’s also creating urgency within the industry to break down operational silos that might hinder programmatic performance.
The Factors at Play
While several factors are at play in the push to push to merge programmatic and direct sales teams, the most clear-cut reasons lie in the numbers: over 80% of digital display ads are now being bought and sold programmatically. That’s why the Times’ entire sales team received in-depth training on programmatic advertising after the recent reorganization: specialized expertise in other kinds of ad sales is no longer valued the way it once was.
However, the numbers also hint that the industry’s increasing reliance on programmatic is creating some discord. According to the World Federation of Advertisers, less than half of marketers believe their programmatic partners are sufficiently transparent. Many programmatic buyers offer their services as walled gardens, meaning that many of their methods and decision-making practices are hidden from their clients. Distrust over transparency is likely responsible for the recent shift from agency trading desks to media agencies for programmatic buys.
This new focus on transparency extends beyond the buyer-publisher relationship — eMarketer points to discrepancies in knowledge and planning between different sales teams in the same company as one of the primary culprits behind the programmatic silo breakdown. Dan Davies, Senior Vice President and Director of Media Sciences at Mediahub, says he’s seen programmatic and direct sales teams compete with each other over sales opportunities.
“In some cases, the direct salesperson intentionally left the programmatic salesperson out of the situation,” Davies recalled to Digiday. “I’ve seen it be that internally contentious.” Publishers have thus worked to stem infighting and improve lines of communication by merging the two sides of sales into one cohesive team.
On the buyer side, brands and agencies are seeing frustration in their efforts to amp up their programmatic capabilities, with just 18% of brands and agencies reportedly satisfied with their programmatic training. Clearly, brands need help bridging the gap between programmatic and non-programmatic — and AI may be just the thing.
Streamlining the Process
Trying to match your team’s capabilities to an increasingly automated media buying landscape can seem impossible, but it doesn’t have to be. A tool like Albert™ — the first fully autonomous AI marketing platform — can be an invaluable addition to your brand or agency in the digital age.
Fully equipped to handle media buying and campaign optimization autonomously, Albert is already a programmatic expert — and he’s learning more every day. By partnering with Albert, marketers and publishers can ensure that their media buying teams are ready to face the shifting digital landscape.
How AI Can Help You Operate Your Tech Stack
Most marketing tech stacks are over-complicated and underperforming — AI marketing platforms represent a single, elegant solution to both problems.
The digital revolution has provided marketers with unprecedented access to their target audiences, but it has also made their day-to-day operations dramatically more complicated. In fact, according to Forrester’s AI: The Next Generation of Marketing report, a mere 6% of marketers believe that their current technology stacks are capable of dealing with the complexities presented by modern marketing.
Marketers’ tech stacks aren’t only ineffective, however — they are themselves overly complex. While separate Forrester research indicates that 58% of B2C marketers are looking to reduce the number of tech vendors they use, fewer than 20% of these marketers are confident that they can get all the functionality they require from a single vendor.
Ultimately, marketers need a centralized “brain” designed to operate and orchestrate various tools and functions across solutions in their tech stacks — not more tools, just a smart one to manage what they already have. This is where cutting-edge tools like Albert™, the world’s first autonomous artificial intelligence (AI) marketing platform, come into play.
To get a sense of how a tool like Albert could be integrated into your tech stack, it helps to consider its specific orchestration and optimization capabilities and how they might be used to manage your existing technologies.
Many traditional targeting technologies are designed primarily to identify consumers at the bottom of the sales funnel who only need to be nudged towards a conversion. This final push is obviously important, but the most effective marketers target potential customers far higher up the funnel. AI can be used to identify, test, and optimize countless paths-to-purchase, all of which can be leveraged to increase the precision of ad retargeting and messaging personalization down the line.
Audience targeting point solutions are ideal for management by AI. Such technologies are often limited to data from within the channel for which they were designed, despite the fact that many of these channels are dealing with the same audiences. Giving AI control over these targeting solutions enables organizations to optimize messaging for specific users based on their experiences across channels and devices.
Many available programmatic solutions can make decisions in real-time, but again, only based on data coming from programmatic campaigns. These solutions should also be considered for operation by AI, as an AI platform can not only make real-time decisions based on data from across channels, but autonomously manage challenges like pacing and cross-channel budget allocation.
A good AI marketing platform can sit on top of existing stack components and run them using learnings from other channels like search and social to inform your media buying strategy. “Unintelligent” programmatic media buying solutions can do a passable job given a straightforward set of conditions, but for real-time autonomous media buying that takes not only price and placement, but things like quality and brand safety into account, AI is the only option.
Testing & Optimization
Finally, once marketers have selected their audiences and delivered their pitches, they need to evaluate how it all went — and how they can do better next time. In addition to extensive A/B testing, this entails a great deal of complex multivariate calculations that go beyond probabilistic decision trees. Tools that can only handle dealing with a few factors like copy, image, and timing have limited value today.
The variables that bear upon digital ad campaigns are legion, and only an AI-based platform is capable of sifting through the massive datasets whose nuances constitute the knife-edge on which successful campaigns balance. Albert uses several different forms of sophisticated machine learning to independently assess not only whether a campaign was a success, but will independently take actions to continually optimize future efforts.
A Better Way Forward
Rather than forcing marketers to reconfigure their tech stacks, AI marketing platforms like Albert are placed on top of existing stacks — they then act as virtual super user, ensuring that marketers get the maximum return from their adtech infrastructure and media spend.
Marketers who adopt AI discover that it provides them superhuman capabilities, like the ability to process and analyze huge amounts of data in real time. Working alongside their virtual team member, marketers translate a brand’s business goals into parameters and guidelines against which the AI platform operates.
As the brand’s business goals and strategy shift, marketers find themselves on the front lines of the human/machine frontier, guiding the AI, as well as interpreting the AI’s ongoing customer insights to their colleagues, from creative to product to senior leadership. This represents a substantial leap beyond marketing organizations who remain crippled by the overwhelming number of tools and unwieldy interfaces at their disposal.
Marketers Need AI to Keep Up with Consumers
The ever-present pressure to provide consumers with increasingly personalized messaging has made cutting-edge AI marketing tools all but essential.
According to Forrester’s Predictions 2018: A Year of Reckoning, 51% of companies invested in some sort of artificial intelligence (AI) capabilities in 2017. And while AI has potential uses in fields as diverse as healthcare, education, and software development, it’s particularly well-suited to the world of marketing.
Last year, Salesforce’s fourth-annual State of Marketing report indicated that 72% of “high-performing marketing leaders” had already incorporated an AI component into their operations, often to great effect. Moreover, 64% of users claimed that AI had “greatly or substantially” improved their overall marketing efficiency.
Nearly 70% of the marketers Salesforce surveyed said that the bulk of their AI operations were focused on improving customer experiences. As WiPro’s Head of MarTech Andy Coghlan put it in an interview with MarTech Series at the end of May, “implementing AI technologies allows marketers to predict what customers want, when they want it, and how they want it.”
A Wide Range of AI Applications
Brands and agencies are quickly realizing that AI has redefined the realm of the possible when it comes to marketing personalization. Marketers obviously benefit from being able to serve more relevant ads to the right people at the right time, but consumers also benefit from the kind of hyper-personalized experiences that machine learning is able to create. In other words, AI-powered marketing is a bona fide win-win.
“At Wipro,” Coghlan continues, “we’re currently prioritizing tracking customer trends, optimizing conversion rates throughout the customer journey, contextualizing online marketing bot experiences, and exploring content automation by implementing multiple aspects of AI.”
AI can be applied to a staggering breadth of marketing activities, from interpreting consumer trends to optimizing customer touchpoints, to creating better customer service bots. That wide range of applications is a testament to AI’s remarkable ability to break down longstanding departmental silos and drive truly omnichannel campaigns, aggregating customer data from countless channels spanning the entire digital world. It’s no exaggeration to call such an undertaking humanly impossible.
AI is The Future of Cross-Channel Marketing Orchestration
Coghlan predicts that “in the near future, we’ll see more and more companies using AI/machine learning to augment human marketers’ [ability] to…help customers determine which products are right for them, answer questions and concerns, and much more.”
Coughlan’s marketing team opted to use Albert™, the world’s first fully autonomous AI marketing platform, for all of its paid media optimization needs. As the research from Forrester, Salesforce, and many others makes clear, this kind of AI-driven approach to marketing is a sign of things to come. “Soon,” Coghlan concludes, “these technologies will be so entrenched in everyday life that consumers and marketers alike will wonder how they ever functioned without them.”
Digital Audio Advertising Is Booming: Here’s Why
The rise of digital audio advertising has given marketers a powerful new lever, but it has also made executing effective cross-channel campaigns even more difficult.
In early 2016, Kia became the first company to beta test Spotify’s playlist sponsorship advertising program. The South Korean auto manufacturer paid the music streaming giant an undisclosed sum for the opportunity to use Spotify’s “New Music Friday” playlist — which has a weekly listenership well into the millions — as a launching pad for its 2017 Kia Sportage.
According to the Interactive Advertising Bureau’s (IAB) Digital Audio Buyer’s Guide – 2.0, Kia’s gambit ended up paying immense dividends: “The campaign generated 10.5 million impressions and resulted in a 30% lift in brand awareness, a 100% lift in brand perception, and a 700% increase in brand consideration.”
With results like these, it’s easy to understand marketers’ growing interest in targeting consumers with digital audio advertising served through popular platforms like Spotify and Pandora. But like every other burgeoning format and channel in the digital marketing landscape, it’s also adding yet another layer of complexity that marketers must quickly learn to master.
Listen Up, Advertisers
Research cited in the IAB guide indicates that more than two-thirds of consumers’ digital media minutes are spent on mobile devices. What’s more, listening to digital radio — which includes satellite radio, music streaming through services like Spotify, and downloadable podcasts — accounts for 15% of consumers’ digital media minutes. It’s one of only three activities claiming over a tenth of consumers’ attention.
These findings are echoed by Edison Research, which reports that 57% of Americans over the age of 12 listen to some sort of digital radio. By the IAB’s estimates, 177 million Americans listened to digital radio in 2016, and that audience should exceed 190 million by next year.
As if the scale of these audiences isn’t enough to entice marketers, the IAB reports that digital audio advertising drives remarkable consumer engagement. Nearly 70% of podcast listeners can name “an actual product feature or specific promotion mentioned” in a podcast, and 61% of listeners claim they have purchased a product or service they discovered via a podcast ad.
Podcast ads have also inspired a fair share of listeners to talk to someone they know about the promoted product or service (22%) or change their mind about a brand (13%) — outcomes that have proven tremendously difficult to achieve through other marketing channels.
Pivoting to Programmatic
Until fairly recently, digital audio ad inventories have been bought and sold primarily through traditional publishing relationships. While several of the major streaming services employ in-house sales reps to push their inventories directly to advertisers, most digital audio ads are sold through ad networks built to aggregate ad space from various publishers.
In 2014, however, internet radio platform iHeartRadio launched a private marketplace (PMP) powered by AdsWizz to sell its digital audio ad inventory programmatically — the first time such an attempt had been made. Spotify followed suit two years later, launching a PMP intended to streamline the process of serving audio ads to its 70 million ad-supported global users (an additional 30 million users pay a monthly fee for an ad-free version of the service).
“A buyer [can] target a specific demographic and layer on the playlist or the type of music they’re listening to,” explains Spotify Head of Programmatic Jana Jakovljevic. “They’re also in control of daypart targeting and frequency capping…and they’re doing all of the optimization and geotargeting from their side, which in the US is down to the ZIP code level.”
The Key to Cross-Channel Advertising
As promising as digital audio advertising may be, its rise adds yet another variable to the already overwhelming challenge posed by cross-channel marketing. Modern marketers are tasked with orchestrating ad campaigns that span dozens of devices and channels, and each new consideration makes doing so just a little bit harder.
To excel in this increasingly complex advertising landscape, marketers can benefit from the use of AI-powered tools like Albert™. With AI working to constantly optimize your campaigns across platforms and devices, your team can focus on developing the best possible content for new formats like digital audio, rather than struggling to balance them with your existing marketing channels.