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.
Are Marketers Prepared to Make the Most of AI?
AI-based tools have the potential to revolutionize critical marketing tasks like content personalization, but only if marketers learn to use them properly.
While much of the early conversation about commercial applications of AI has been shaped by concerns about automation-driven job loss, the consensus is that these concerns are largely unfounded. McKinsey reports that fewer than 5% of jobs could be fully automated using current technology. Meanwhile, Gartner Research Director Manjunath Bhat declares that “robots are not here to take away our jobs, they’re here to give us a promotion.”
As countless experts have made clear, AI tools are not designed to replace human workers, but to augment them. This is especially true in fields like marketing, where creativity and complex data analysis are in constant conversation with one another.
Despite this clear alignment of interests, a recent Conductor survey found that a significant portion of marketers (34%) rank AI as the 2018 industry trend for which they feel most unprepared. Much of this uncertainty stems from marketers’ lack of clarity about what an AI tool can and cannot do, and how adopting one will affect the particulars of their day-to-day work.
AIs Need Onboarding Too!
The most important thing for a marketer to understand is that an AI tool must be trained before it can really contribute to an organization’s marketing efforts. Most AI marketing tools are powered by machine learning algorithms — strings of code that aren’t programmed to execute an explicit series of commands, but rather, “learn” from the datasets they’re provided.
It’s therefore a marketer’s responsibility to feed their AI tool not just large quantities of data, but large quantities of high-quality data — especially during the early stages of integration. This can be a big ask for less data-literate marketers (over half of marketers admit to being overwhelmed by the amount of data in their marketing stack), but the best AI tools are designed to be incredibly user-friendly.
For instance, with Albert™, the world’s first fully-autonomous AI marketing platform, marketers are able to create a straightforward rule set that shapes how the platform learns and adjusts in the future. Albert takes established KPIs like overall budget, daily spending limits, and frequency caps and develops a nuanced understanding of when and where to cut off and/or redirect spending for various campaigns to maximize results while remaining within those boundaries.
Revolutionizing Personalization in Marketing
Once a machine learning algorithm has been properly trained, personalization is arguably the first task marketers should delegate to their AI tool. Many modern consumers demand highly tailored ad experiences, but just over one in ten marketers are either “very” or “extremely” satisfied with their current level of personalization.
Traditionally, marketers have approached digital personalization in a way that reinforces existing silos within their department. If a marketer is crafting a campaign for Facebook, for example, they typically rely on lookalike audiences assembled by the social media giant’s internal teams. Tracking the quality of this lookalike audience requires a great deal of intensive cross-referencing between datasets, making it effectively impossible to accomplish in real time.
An AI tool, however, can seamlessly process immense volumes of data drawn from any number of marketplaces in a matter of minutes. This not only delivers truly 1:1 experiences for all targets in cross-channel campaigns, but helps break down long-standing silos within a marketing team, as well.
A New Way Forward
When it comes to AI’s potential impact on marketing, these improved personalization capabilities are just the tip of the iceberg. Tools like Albert make marketers better at their craft and free them up to work with their human colleagues on higher-level, more creative projects and processes — provided that these marketers train their AI partners with the proper care.
As von Hollen concludes, when an organization effectively integrates AI into its operations, “what you get is a completely new working environment.” At least in marketing, this environment is not only new, but indisputably better.
What Google’s Canceled Pentagon Contract Says About AI and Morality
Google has decided against renewing its contract with the Department of Defense after employees expressed concerns about the ethical implications of militarized AI.
In a wide-ranging 2016 discussion with WIRED Editor-in-Chief Scott Dadich and President Obama, MIT Media Lab Director Joi Ito observed, “What’s important is to find the people who want to use AI for good — communities and leaders — and figure out how to help them use it.”
President Obama echoed Ito’s sentiment, but pointed out that it remains difficult to delineate between “good” and “bad” deployments of AI. “There’s no doubt that developing international norms, protocols, and verification mechanisms around cybersecurity generally, and AI in particular, [are] in [their] infancy,” he argued. “Part of what makes this an interesting problem is that the line between offense and defense is pretty blurred.”
Over the course of the last six months, this linear drama has been thrust into the public sphere, as Google has attempted to navigate the controversy generated by its work with the US Department of Defense (DoD).
Supercharging DoD Analyses with AI
In an April 2017 memorandum, the DoD announced the establishment of the Algorithmic Warfare Cross-Functional Team (AWCFT), or Project Maven. According to the memo, “The AWCFT’s objective is to turn the enormous volume of data available to [the] DoD into actionable intelligence and insights at speed.”
Project Maven was conceived primarily as a way to integrate “advanced computer vision” into the analyses of video footage collected by military drones like the ScanEagle, MQ-1C Gray Eagle, and MQ-9 Reaper.
“A single drone…produces many terabytes of data every day,” writes Air Force Lieutenant General Jack Shanahan in an article published by the Bulletin of the Atomic Scientists in December 2017. “Before AI was incorporated into analysis of this data, it took a team of analysts working 24 hours a day to exploit only a fraction of one drone’s sensor data.”
By training Project Maven’s underlying algorithms with hundreds of thousands of human-labeled images, the DoD managed to deploy Project Maven in the military conflict against the Islamic State just eight months after the initiative was announced.
This “frankly incredible success” notwithstanding, Lt. Gen. Shanahan is well aware that the continued militarization of AI will inevitably raise a number of questions. “As US military and intelligence agencies implement modern AI technology across a much more diverse set of missions, they will face wrenching strategic, ethical, and legal challenges.”
Resistance from Within
According to a vocal minority of Google employees, however, Project Maven’s “narrow focus” is an insufficient safeguard against improper, unethical deployments of AI. Although many of the details were kept under wraps, Google quietly signed an 18-month contract with the DoD late in the summer of 2017.
After news of the tech giant’s military entanglement leaked this past March, a Google spokesperson told Gizmodo, “This specific project [Project Maven] is a pilot program with the Department of Defense to provide open source TensorFlow APIs that can assist in object recognition on unclassified data. The technology flags images for human review, and is for non-offensive uses only.”
This reassurance proved inadequate for some. In early April, several thousand Google employees — roughly 3% of the company’s workforce — submitted a letter to CEO Sundar Pichai asking that Google immediately withdraw its support from Project Maven and “draft, publicize, and enforce a clear policy stating that neither Google nor its contractors will ever build warfare technology.”
After a month of corporate inaction, around a dozen Google employees resigned in protest in mid-May. This seems to have been the straw that broke the camel’s back, as on June 1, the Washington Post reported that Google has decided against renewing its DoD contract when it expires next March.
A Commitment to Transparency
Like Ito, we at Albert™ believe that AI should be used as a force for good. But as the Google/DoD saga makes clear, defining “the good” — as it pertains to AI or in any other context — is anything but easy.
Ultimately, trust in AI is something that must be earned, and for us, that means a commitment to transparency. That’s why we recently introduced Inside Albert, a new feature that offers marketers a window into the inner workings of our product, making it easier for users to adjust operational parameters and optimize campaign results. Innovation and ethical uncertainty are inextricably tied, but we recognize the added responsibilities incumbent upon us as pioneers of AI in the marketing space.
UK Government Releases Framework for Development of Ethical AI
A recent report issued by the House of Lords lays the groundwork for a regulatory regime governing artificial intelligence in the UK.
On April 16, the House of Lords Select Committee on Artificial Intelligence released a comprehensive report outlining the future of AI in the UK. Entitled AI in the UK: Ready, Willing, and Able?, the report grapples with the ongoing disconnect between technological and legal changes in the AI space.
“AI is not without risks, and the adoption of the principles proposed by the Committee will help mitigate these,” says Committee Chairman Lord Tim Clement-Jones. “An ethical approach ensures the public trusts this technology and sees the benefits of using it. It will also prepare them to challenge its misuse.”
Without prescribing a specific regulatory regime, the report provides a broad ethical framework for AI development. This framework is comprised of five overarching principles:
AI should be developed for the common good and benefit of humanity;
AI should operate on principles of intelligibility and fairness;
AI should not be used to diminish the data rights or privacy of individuals, families, or communities;
All citizens have the right to be educated to enable them to flourish mentally, emotionally, and economically alongside AI; and
The autonomous power to hurt, destroy, or deceive human beings should never be vested in AI.
Grounded in the synthesis of extensive expert testimony, these principles are intended to both shape the future of AI in the UK and act as a corrective to a number of early AI “misuses.”
Building on GDPR
Apropos of any discussion about AI, the Committee dedicates a great many pages to data concepts both old (open data, data protection legislation, etc.) and new (data portability, data trusts, etc.).
For instance, despite its forthcoming “Brexit” from the European Union, the UK has committed itself to domestic enforcement of the EU’s recently implemented General Data Protection Regulation (GDPR). As the Committee points out, this means that many organizations will be required “to provide a user with their personal data in a structured, commonly used, and machine-readable form, free of charge.” GDPR stipulations like these are not uniquely applicable to AI, but moving forward, they will have a significant impact on the development of AI-driven technologies both in the UK and abroad.
Counteracting Hardcoded Bias
On a more AI-specific level, the Committee writes at length about one of AI’s most persistent ethical dilemmas: bias. For example, the UK is home to a number of companies offering machine learning-driven recruitment tech, which uses algorithms to narrow down a candidate pool by leveraging historical data to draw connections among previously successful candidates.
As efficient as such an approach can be, it isn’t without its pitfalls. “While the intention may be to ascertain those who will be capable of doing the job well and fit within the company culture, past interviewers may have consciously or unconsciously weeded out candidates based on protected characteristics (such as age, sexual orientation, gender, or ethnicity)…in a way which would be deeply unacceptable today,” the report reads.
In other words, if bias — or even flat-out (illegal) discrimination — is hardcoded into a company’s data, it will only be amplified by the introduction of AI. This can be a difficult obstacle to overcome, but the Committee is adamant that doing so is necessary to ensure that AI “operates on the principle of fairness.”
Placing a Premium on Transparency
While the testimony provided to the Committee was wide-ranging and, at times, contradictory, nearly every expert “highlighted the importance of making AI understandable to developers, users, and regulators.” According to the Committee, this entails a high degree of “explainability” — that is, “AI systems [that] are developed in such a way that they can explain the information and logic used to arrive at their decisions.”
At Albert™, the world’s first fully-autonomous AI marketing platform, explainability — or, as we call it, transparency — has been at the heart of what we do since day one. Using our Inside Albert feature, marketers can take a peek “behind the scenes” of each and every campaign Albert is managing.
There’s still much to be done to prepare society at large for an AI-driven future, but with a tool like Albert, marketers have the chance to prove that, at least in certain industries, the future is already here.
4 Steps to Transform Your Agency
As digital media finally overtakes traditional media in terms of revenue, ad agencies need to reconsider how they approach nearly every aspect of their work.
As digital media finally overtakes traditional media in terms of revenue, ad agencies need to reconsider how they approach nearly every aspect of their work.
According to Ad Age’s Agency Report 2018, 2017 marked the first year in which digital work accounted for more than half of American ad agencies’ revenues. Digital tasks comprised 51.3% of the work agencies conducted last year — nearly double the share they claimed in 2009.
Digital media’s relatively rapid rise has had countless, dramatic repercussions in the professional world, but perhaps none more disruptive than the democratization of agency work. A recent Research Intelligencer study found that the ad agency holding companies WPP, Omnicom, Publicis, Interpublic, and Dentsu now command only half of all global advertising and marketing revenue, a far cry from the near-total dominance of the industry that first earned them the moniker “the Big 5.”
The Big 5’s collective revenue growth rate has decreased from 4% in 2015 to 3% in 2016 to a mere 0.9% in 2017. And while there are numerous factors driving this stagnation, smaller agencies’ ability to navigate the unpredictable, ever-changing digital landscape is foremost among them.
Of course, smaller doesn’t always mean better — even in the digital age — and non-Big 5 agencies must also make a concerted effort to constantly evolve their businesses to keep up with the times. With that in mind, I’ve drawn up a list of four steps any agency can take to remain competitive in today’s digital-first advertising and marketing landscape.
1. Get Creative with Your KPIs
In many ways, modern digital marketing is remarkably different from traditional print, radio, and out-of-home marketing. As an agency, adapting to this sea change requires a collaborative approach aimed at rethinking long-held industry maxims, including those that linger from the early days of digital marketing. A new marketing world demands new marketing ideas, and agencies shouldn’t hesitate to consider and adopt radical new business propositions.
For instance, click-through rates on banner ads or vague measures of social media engagement (think: “Likes” or “Shares”) might not be the ideal KPIs for measuring a client’s goals, and an agency shouldn’t hesitate to drop such standard metrics in favor of more performance-oriented KPIs like return on ad spend.
2. Reimagine Your Client Relationships
To stand out from competitors, agencies must change not only the way they measure success, but also the very nature of their client relationships. The “client serves brief, agency delivers solution” model isn’t optimized for the digital age, and agencies need to start thinking more in terms of dynamic partnerships and less in terms of assembly line-style production and hourly rates.
This imperative is particularly pressing when it comes to transparency. The Association of National Advertisers’ 2016 Media Transparency Report found that “numerous non-transparent business practices, including cash rebates to media agencies, [are] pervasive in the US media buying ecosystem.” An agency that spends its clients’ ad budgets according to such incentive structures rather than its clients’ best interests undermines the entire purpose of an agency partnership. In other words, this approach is the epitome of what not to do to get ahead.
As stated earlier, a dynamic partnership requires your agency to focus as much on KPIs and creative output as it does on project budgets and timelines.
3. Invest in Sophisticated Data Analytics
Data is the “raw material” with which digital marketing campaigns are built, and a company’s agency partner is often the only party with access to all of the data necessary to accurately assess both specific ad performance and bigger-picture campaign effectiveness. As such, sophisticated data analytics capabilities have become the mark of an effective agency.
True campaign optimization is only possible once an agency is able to surface and act on predictive insights drawn from real-time, dynamic analytics. However, executing such an analytics program at scale, across numerous client accounts, is all but humanly impossible, which leads us to…
4. Adopt an AI Marketing Tool
A cutting-edge AI tool like Albert™, can help agencies lower their operational expenses and dedicate more time to delivering real value to their clients.
Integrating an AI component into an agency’s operations is not only the first step toward a reliable predictive analytics program, but it will also help the agency become more transparent and more tactically creative.
Stepping into the AI marketing era is a big step indeed, but it’s a necessary one for any group angling to assert itself as a truly digital-first agency.
Waymo Blazing the Trail Towards Driverless Dominance
By focusing more on its AI-powered self-driving system than on building its own driverless car, Waymo has established itself as a leader in the autonomous driving market.
Though a few troubling accidents have hindered the development of self-driving technology at both Uber and Tesla, not all autonomous automobile innovators are struggling with safety. Major industry player Waymo (called the Google Self-Driving Car Project until a few years ago) has not only managed to steer clear of serious accidents, but taken major steps towards industry dominance.
Focusing on the “Driver”
Unlike many of its competitors, Waymo has little desire to manufacture its own self-driving vehicles. Instead, the company is pouring all of its resources into perfecting an autonomous “driver” — a software-hardware package that has been in development since 2016 — that can be deployed in any properly-outfitted vehicle.
Whereas initiatives like General Motors’ Cruise division have been focused on manufacturing fleets of self-driving vehicles from the ground up, Waymo has chosen to make investments in both the practical and the posh. It has been testing a fleet of self-driving Chrysler Pacifica minivans in Arizona since early 2017. They also recently announced a partnership with Jaguar aimed at bringing as many as 20,000 self-driving luxury vehicles, called I-PACEs, to the road in the near future.
“While we’ve been focused at Waymo on building the world’s most experienced driver, the team at Jaguar Land Rover has developed an all-new battery-electric platform that looks to set a new standard in safety, design, and capability,” says Waymo CEO John Krafcik.
Impressive Progress Being Made
Since its system is manufacturer-agnostic, Waymo has the flexibility to jump on opportunities like the Jaguar partnership and test its Driver in more places. This approach partly explains why its driverless road tests have been so successful. As Mountain View City Manager Dan Rich points out, “Waymo has done extensive vehicle testing on our local streets with a good safety record.”
According to a report Waymo produced for the California DMV, vehicles equipped with the company’s autonomous driving system drove 352,545 miles on California roads from December 2016 to November 2017. During these road tests, Waymo recorded only 63 “disengagements” — defined by the California DMV as “a deactivation of the autonomous mode when a failure of the autonomous technology is detected” — amounting to one incident every 5,596 miles driven.
The Power of High-Volume Virtual Testing in Machine Learning
Real-life road tests are obviously critical, but as Waymo Lead Software Engineer James Stout explains, the vast majority of Waymo tests are conducted in a virtual environment.
“Each day, as many as 25,000 virtual Waymo self-driving cars drive up to 8 million miles in simulation, testing out new skills and refining old ones,” Stout writes. “With simulation, we can turn a single real-world encounter — such as a flashing yellow left turn [light] — into thousands of opportunities to practice and master a skill.”
All told, Stout estimates that Waymo “drivers” navigated over 2.5 billion virtual miles in 2016 alone, “miles far richer and more densely packed with interesting scenarios than the average mile of [road] driving.”
Both the company’s “drivers” — which are designed around sophisticated machine learning algorithms — and its engineers learn new things each time a test is conducted, making high-volume virtual testing a crucial element of Waymo’s pursuit of driverless perfection.
A Proven Artificial Intelligence System
Ultimately, the underlying mechanics of Waymo’s “driver” improvement process are not unlike those at work in Albert™, the world’s first fully autonomous artificial intelligence marketing platform.
As soon as Albert is fed a company’s historical marketing data and its creative materials for a new campaign, he conducts a wide variety of microtests based around thousands of variables. Like Waymo’s virtual simulations, these microtests help Albert figure out what works, what doesn’t, and what changes need to be made to the company’s marketing practices.
With Albert’s help, a company has the power to execute a marketing campaign across multiple channels and audience segments at superhuman speeds, guaranteeing a higher, more consistent return on ad spend than is possible with a team of human marketers alone.
To learn more about how AI is changing the future across industries download our latest eBook, The Top Movers & Shakers In Autonomous AI Today.