Get Your Team Ready: Designing The Future Human-Machine Workplace
All digital marketers will soon be working alongside AI. Is your team ready? A report from Forrester Research reveals how people and machines can thrive together in the workplace. Here’s a brief look at some key takeaways:
Design an Inclusive Workplace
While we might enjoy imagining a world in which technology has taken on our boring, repetitive tasks, the reality is many of us don’t feel ready for this revolution. In fact, Pew Research found that 65% of employees in the US fear being replaced by robots in the next 50 years. To make sure their teams feel empowered by, rather than fearful of, technology, leaders need to strategically design a workplace that supports both humans and machines. This means showing consideration for employee experiences when implementing technology, giving employees authority on what gets automated, and clearly defining roles for people and machines.
Free Employees for More Important Work
AI technologies can significantly lower cognitive load and free up time for more strategic work by taking rote, mechanical tasks off of employees’ plates. To harness machine learning for success, leaders must communicate time and cognitive load savings with employees, outline what new tools are doing, and explain why technology is making certain decisions. Building trust and transparency between individuals and machines will allow people to do what they do best, and leave machines to handle rote, time-consuming tasks.
Adapt AI to People, Not The Other Way Around
When the learning curve is high, people struggle to adapt to new technology. A mere 23% of global information workers report easily understanding how specific technologies work. The good news? 48% are open to receiving training to improve their skillsets at work. To support employees during a technology transition, organizational leaders should not only try to layer AI into existing software, but also offer ongoing training to increase understanding of new tools.
Leave Humans In Charge
To create an optimal environment for human-machine collaboration, we need to preserve space for human judgment, creativity, and cross-domain expertise. Companies can play to those inherent strengths by investing in workers whose duties require emotional awareness, leaving collaborative tasks for humans, and keeping people in the loop on automated business processes.
A lot can go wrong when companies integrate AI into their business processes, but the benefits are enormous when implemented well. Download the report to learn how people and technology can successfully collaborate.
The New Tenets of Digital Marketing #4: Restoring the Greater Function of Marketing
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.
New Tenets of Digital Marketing: #3 Unlocking Cross-Platform Insights
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.
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.
New Tenets of Digital Marketing: #2 Winning at Every Stage of the Customer Journey
This post is the second tenet in our series The New Tenets of Digital Marketing.
Marketers have long sought to better understand audiences’ needs in pursuit of deeper brand affinity and customer connection. And as the customer journey flexes to account for droves of online and offline touchpoints, it has become profoundly more complex for marketers to find, connect and win audiences at each stage of an iterative customer journey.
Considering offline touchpoints alone – we have struggled to understand the influence of offline media on our customers’ behavior and particularly, the impact of offline marketing initiatives on sales – both online and offline sales. The arrival of a digital ecosystem appeared to offer some relief and hope for clarity; the promise of digital ad technology was always that it was trackable and accountable. However, the reality of digital ecosystems manifested a little differently. As our largest digital channels grew and evolved separately, guided by no unifying principles of measurement, the promise of cross-channel trackability continued to elude marketers. Meaning, understanding our customers’ full digital journeys is now a discipline with its own complexity. And marketers, handcuffed by a lack of cross-channel insights, limited resources, and inadequate tools to match the demands of our customers’ non-linear and omnichannel journeys, find mastering the customer journey to be a continuing challenge. Today, new intelligent automation technologies bridge substantial steps toward a solution. Intelligent automation platforms with cross-channel planning and execution capabilities offer holistic, cross-channel insights and most importantly, accelerate the ability to take action on those insights instantaneously. While not solving for walled gardens, these intelligent automation platforms allow marketers to better see the nuances of the online journey and to execute dynamic, personalized strategies effectively across channels. For marketers focused on achieving the optimal customer experience, intelligent automation partners allow them to power a customer-obsessed culture in fruitful ways.
Customer Obsession: Getting It Right
Marketers succeed only when they are able to anticipate customers’ needs and deliver along the customer journey when and where customers want them to. To this end, brands today are pursuing a customer-obsessed culture with renewed intensity. But, what does it take to be customer-obsessed?
Forrester defines customer obsession as: “Customer-led, insights-driven, fast, and connected.” In unpacking this and thinking about what our marketing teams need to have in order to achieve each, it quickly becomes apparent that customer-obsession requires bandwidth and the ability to track, analyze and execute on audience data at a volume and rate beyond average capability today. It is no surprise that effective customer obsession has been so difficult to achieve thus far.
Marketers will have to tackle a number of challenges in order to deliver on customer obsession, namely, leveraging data to draw meaningful and actionable customer insights, threading those insights through a cumbersome media planning process, and executing across an incongruous landscape.
Moving Beyond Silos
In the typical media planning process today, marketers divide the customer journey into discrete phases, such as awareness, consideration, and intent. Within each phase, we identify the channels to invest in, targeting tactics to employ, the audiences to prioritize, the formats to use, and the messages to serve for the duration of the campaign. Inevitably, this presents a problem as it is an inflexible and fixed approach to channels and audiences which are by nature dynamic and iterative. Not only is it a labor-intensive strategy, but it is also one that relies on manual execution and thus leaves tremendous value on the table.
This siloed approach not only impedes marketers’ ability to make holistic decisions, but it also ignores opportunities to optimize across channels and audience phases. Some campaign management AI vendors have been able to offer minor improvements. For example, some ad tech vendors provide algorithmically driven capabilities to identify and adjust optimal spend for poor and well-performing audiences. However, these kinds of tools either hand off the actual spend adjustments to human teams or remain disconnected from other channels where this same insight could provide an advantage to better connect with audiences at other important touchpoints. On the creative side, some vendors who promise multivariate testing employ algorithms to determine ads that are ‘winners and losers,’ but are unable to then shift spend to winners in real-time, instead, once again, handing off action for manual execution.
In order to achieve productive customer obsession, marketers need to move away from siloed channel execution and from relying on human action in order to take advantage of algorithmic learnings. Instead, we will discover the powerful ability of autonomous AI to identify and execute within the flexible and dynamic requirements of today’s digital media landscape.
Achieving Fast and Connected
How are customers interacting with the brand on and off-line? How can we create a more cohesive and impactful experience across both? Understanding the customer journey begins with actionable data. To answer these central questions, marketers will need to collect and synthesize data across channels. Today, this is a manual and time-consuming undertaking, but with new advanced technologies, this process can be largely automated.
Machine learning-driven technologies are able to analyze and execute not only across digital channels, but they are able to knock down barriers between the online and offline worlds as well. They can ingest and analyze not only digital advertising data, but also have the ability to collect and integrate data from the off-line world, such as inventory levels, call-center volume, and other supply-side data points. With autonomous AI platforms powering digital activity and informed by inventory levels in real-time, retail organizations can regulate – by geography – the amount of ad spend they place behind key low or high inventory products.
The ability to deliver on customer obsession will require marketers to understand some key truths. First, it requires the acceptance that humans are ill-equipped to predict when and where to meet each audience within their customer journey. Second, it takes the realization that machine-grade algorithmic technologies, and their multivariate testing and predictive modeling abilities, are necessary to meet and match customer needs in every moment.
Marketers will need to step back from channel-specific views and look to cross-channel strategies in order to capture insights and deliver value across the customer journey. Additionally, they will need to leverage more robust technologies, namely, intelligent automation, to achieve faster learning and more-informed execution. Ultimately, it is this improved learning and the ability to take action that will enable marketers to be more thoughtful and effective in their pursuit of customer obsession.
Read more in our third post of the series – Tenet #3: Unlocking Cross-Platform Insights.
New Tenets of Digital Marketing: #1 Recruit for Human Intelligence
This post is the first tenet in our series The New Tenets of Digital Marketing.
The growth in digital marketing channels and the rapid evolution of consumer technology has forever transformed the marketing landscape. With the transformation that has taken place, where does this leave the role and responsibilities of marketers today? More importantly, what are the skills and expertise needed for the modern marketing org? Understanding the evolving role and capabilities of technology will be crucial in order for marketing leaders to define and develop effective teams to meet today’s challenges.
To date, the expansion of digital marketing and the adoption of new technologies has translated into more work for marketing teams. Due to these new demands on bandwidth, marketers have been pulled away from their core purpose of customer centricity to manage time-consuming tasks such as bid adjustments, keyword expansion, rotating creatives, and more.
However, with more sophisticated technologies, like autonomous AI-powered ad tech, coming to market, we are beginning to see a tipping point. These robust technologies are ushering in a new tier of benefits for marketers – taking on the heavy lifting of time-consuming, rote tasks, allowing faster and more informed decision-making, and enabling the ability to modify ad spend and execute targeted marketing campaigns in real-time.
In this post, we’ll uncover how these new technologies are elevating the role of marketing organizations and marketers, and what skills CMOs and marketing leaders should be developing and hiring for in 2019 and beyond.
How New Tech is Powering Marketing Organizations
Today, informed marketing leaders are adopting innovative technologies, specifically autonomous AI capabilities, and tapping into newfound advantages over the market. These include:
- ‘In-sourcing’ rote, mechanical tasks. By leveraging autonomous AI, marketers can boost productivity with an ‘incremental’ workforce, without multiplying the size of their teams. By letting the machines take on manual, time-consuming tasks, technology is helping to increase bandwidth and allow marketing teams to perform beyond their size.
- Making more and better decisions. New autonomous AI technologies arrive at optimal spend thresholds by analyzing hundreds of targeting and creative variables and making decisions in real-time – a task that would take human counterparts many months and advanced skill sets to run as effectively, let alone take action on. Leveraging autonomous AI enables marketing teams to make more confident decisions based on more conclusive data, multiplying their productivity.
- Unifying Silos. One of the least addressed benefits of autonomous AI technologies is its ability to enable marketers to see through silos of data and to access a more complete view of the advertising landscape. By applying the power of AI across channels, marketers gain access to holistic insights into channel performance, audience segments, and return-on-investment, informing initiatives across the marketing org.
How Machines are Elevating the Role of Marketers
Today, we are seeing emerging technologies, specifically, new AI technologies, start to turn the tide and improve marketing bandwidth. The first phase of AI introduced assisted AI technologies, which helped to accelerate data processing and optimization suggestions, enabling marketers to see where campaign efficiencies could be gained, much more quickly than before. However, the responsibility to take meaningful action on the analysis still required human time and bandwidth.
With Autonomous AI, the role of the machine becomes greater and the technology moves beyond the operational tasks of number crunching and data analysis to decision-making and execution. By moving monotonous, time-consuming tasks to machines, the marketers’ role can become more strategic.
What the Modern Marketing Org Should Look Like
How do new technological capabilities impact the division and collaboration of marketing responsibilities between human and machine? What are the pivotal marketing skills to hire for today? How should CMOs and marketing leaders think about the allocation of resources across their marketing organizations?
As machines take on more of the mechanical tasks, marketing leaders can focus on developing and expanding their capabilities. For organizations that are employing autonomous AI capabilities, we have identified four key areas that will drive significant impact within marketing organizations in 2019 and beyond:
- Data Analysis. With more data being collected than ever before and greater machine-powered analytics capabilities, marketing teams will need individuals who can identify the story and insights behind the data for the purpose of taking meaningful action.
- Media Strategy. As the interplay of all channels and devices online and offline continue to evolve and the customer journey becomes more complex, teams will need individuals who know how, where and with what messages to connect with audiences to draw them closer to the brand.
- Creative Storytelling. Increased competition for audience attention demands compelling campaigns that attract, pique, and engage audiences. Teams will – more than ever – need creative minds who understand the role of original thought and the art of advertising.
- Connectors. As campaign strategies address more channels and customer segments, marketers’ ability to connect learnings across brands, portfolios and their organization will become the key to meaningful and sustainable change.
As we’ve uncovered, today’s digital marketing landscape is complex and rife with competition for customer attention. In order to be effective, marketers will need to focus on their core function and what they do best – delivering on human empathy, creativity, and innovation – here’s a before and after visualization. Meanwhile, machines will be pivotal in helping marketers to achieve this, by managing, analyzing, and making optimizations off data in real-time. This transformation will not happen on its own, but rather marketing leaders will need to be strategic in both the adoption of key technologies and in developing and hiring for the roles and skills needed for the modern marketing org.
Read more in our second post of the series – Tenet #2: Winning at Every Stage of the Customer Journey.
Introducing the New Tenets of Digital Marketing
Marketers looking out at today’s digital advertising landscape see a tidal wave of change, with new rules to play by and an increasingly elusive path to success. Many are frustrated by the sheer number and complexity of data sources, overwhelmed by the deluge of tech tools, and suspect that somewhere along the way, they’ve lost sight of the fundamental marketing goals of brand building and audience connection.
Struggling to keep pace with innovation and to meet resource constraints, marketers say they have been more focused on defending ROI than on pursuing brand transformation. In place of time spent on strategy, category disruption, messaging creativity, and the customer experience, marketing leadership is focused on how to stretch resources and often finds their teams bent on daily, rote tasks like creative rotations and bid adjustments.
As pioneers in autonomous AI, Albert’s aim is to help marketers regain their true north. We are paving the path forward in the post-digital era to unravel the evolving rules of digital advertising, the role of autonomous AI, and the new tenets that will enable marketers to drive transformative business impact.
Before we share our path forward, we’ll take a look at the critical developments accelerating change and compelling innovation in the market today:
#1 From Mass to Hyper Niche
For more than a decade, marketers have relied on paid advertising on major digital platforms such as Facebook and Google as a primary tactic to drive brand awareness, customer acquisition, and conversion. As these platforms reach advertiser and audience saturation, new platform adoption slows, and audiences split their attention across platforms and devices, we find increased spend doesn’t necessarily translate to increased return.
Marketers in this ecosystem are realizing they can no longer win by simply increasing budgets, but that they need to deploy many micro-optimizations (at greater frequency) in order to achieve macro gains again. Winning strategies today are those that lean on actionable learnings to guide media optimizations at each phase of the customer journey, uncovering optimal and tailored methods of catching attention amidst advertising chaos, piquing interest with the right messaging shown to the right audience, and delivering utility where it’s most appreciated. It is a movement away from blunt strokes in media execution to hyper niche targeting and creative messaging.
In order to maintain gains in a saturated marketplace, marketers have moved to invest heavily in niche approaches – and subsequently overhauled the way media operations, partner relationships, and investments look to match.
#2 Reaching for the Longtail
As audiences have shifted their attention and time spent to smaller, endemic platforms, they created a new and significant opportunity for reach and revenue generation that brands could not ignore. However, trying to reach customers in the longtail presented additional, complex challenges for marketers – now compelled to consider the pros and cons of investing in programmatic, dynamic creative, and the tradeoffs that follow. Ultimately, digital marketers betting on the promise of programmatic to deliver efficient reach against dynamic and niche audiences, have often been disappointed. In an effort to maximize efficiency, effectiveness often took a backseat. Marketers learned that substantial investment (both time and money) is required to move ad budgets manually against valuable micro audience groups, often missing the true opportunity of incremental audiences.
Digital marketers have begun to demand more sophisticated tools to help plan, build, optimize, and measure audience activity in the face of growing platform complexity and a returning focus to effectiveness, not just efficiency.
#3 Managing Data Complexity
The customer journey continues to move toward greater complexity and marketers find they need to do more – just to stay in the game. To supplement the hard work of developing traditional and dynamic creative, creating compelling media plans and relevant buys, they insist on more robust measurement and analysis to see their audiences in full view and take more meaningful action.
In an effort to solve for this, marketers look to new tech partners to fill in the gaps in the story. Data vendors offer reams of data on web behavior, mobile insights, shopping propensities, influencer affinity, credit card purchases – resulting in many partial views. And campaign management tools bring to market specialized capabilities that offer another snapshot of the full picture, such as measuring discrete phases of the journey, providing siloed optimization, or calculating ROI by single channel. Such additions to the tech stack often compound complexity, despite the intention to simplify. And still, holistic views such as full funnel ROI, cross-channel optimizations, and comprehensive analyses of the customer journey remain critically absent.
Marketers continue to crave actionable insights across their efforts. This coveted state remains out of reach due, in part, to the inability to assess and take action from the data that lives across their tech stack.
The Path Forward
The way we see it at Albert, marketers must develop innovative strategies fit for this new landscape in order to create and sustain gains. By doing so, we reclaim the greater and original function of marketing: building a powerful brand and audience connection.
In this series, we’ve mapped the key tenets of digital marketing as they apply to brands today, with the aim of guiding advertisers through tumultuous change in the post-digital era. We’ll touch on how to hire and deploy the right teams to win the machine + human opportunity, we’ll share our approach to pursuing customer obsession successfully, and unveil methods of maximizing your data use and application. In each post, we will share our view on how marketers can employ autonomous AI to achieve renewed marketing impact in today’s landscape and ultimately, restore its greater purpose.
Read more in our first post of the series – Tenet #1: Recruit for Human Intelligence.
Three Ways Brands Can Bridge The AI Opportunity Gap
Major retailers like Amazon have been using advanced technologies like artificial intelligence (AI) seemingly since the beginning. So when brands set out to go the direct-to-consumer route, they have to quickly master cutting-edge retail tactics and adopt new technologies to challenge, or at least keep up with, these established competitors as quickly as possible.
There’s an especially steep learning curve for brands that choose to execute on these new strategies in-house, using artificial intelligence to scale their team and reach rather than relying solely on an agency.
This shift is not limited to retail brands. Financial institutions, telecommunications companies and other traditional industries are also assuming the role of seller — and increasingly, of agency — to take a more direct approach to customer experience and acquisition.
My company recently commissioned Forrester Consulting to understand brands’ increasing adoption of artificial intelligence in this changing climate. Forrester spoke with 156 marketing decision-makers in retail, CPG, food and beverage, financial services, telecommunications, software and travel and hospitality to take a closer look at their use and applications of the technology.
Their research revealed an opportunity gap between how marketers are currently using AI versus how they could and should be using it. Here are three ways brands can bridge that gap:
1. Redefine the division of labor between humans and machines
In 2016, Forrester conducted a similar study (via MarTech Today) that revealed over 40% of marketers were using AI. This number has now more than doubled, with 88% using either assistive AI or autonomous AI technologies. The distinction between these two types is important as it highlights an important part of the AI opportunity gap — or, where brands are versus where they have the potential to be.
Where brands are: Seventy-four percent of respondents report using assistive AI technology, which surfaces insights for marketers to consider during manual decision making. The remaining 26% are using autonomous AI, which can act on its own insights and work collaboratively with marketers.
Marketers using AI in an assistive capacity are experiencing similar complexities in their processes and operations as they were before AI adoption. While an assistive approach may speed up certain campaign-oriented tasks, it’s limited by its reliance on humans to make decisions and manually complete tasks.
Where they have potential to be: AI has far more to contribute if we start thinking about it as a collaborative and autonomous system for scaling marketing campaigns rather than cool technology that exists solely to help brands make semi-informed decisions. Even equipped with machine-generated insights, marketers don’t have all the information they need to make the same number of decisions with the same level of clarity and act on them at the same scale.
Marketers must redefine the division of labor between humans and intelligent technology. Humans will tackle all things creative, strategic, intuitive and emotional. AI will take on all things data gathering and analysis and then act autonomously on the insights it surfaces.
This shift is inevitable. According to a global study by Pegasystems and Marketforce, “Sixty-nine percent [of marketers] said they expect the term ‘workforce’ to eventually encapsulate both human employees and intelligent machines. “
2. Treat AI as a fundamentally different kind of technology
Today’s direct-to-consumer marketing is all about creating personalized, high-touch experiences for consumers. As the lines between our digital and physical worlds blur, brands need to work at the speed required to provide consumers with the experiences they want, at the cadence they need, across online and offline channels, wherever they are in their journeys.
Brands need to understand where they are in their own AI journey as well. Based on our report, only 43% of marketers said that their technology was helping improve customer experience. Thirty-nine percent said it’s helping increase customer retention, and only 33% credited it with increasing customer acquisition.
These are not just nice-to-haves; they’re make-or-break brand and business objectives. If brands are using AI but not winning in these places, it’s often because they’re hanging onto manual approaches or are struggling with the idea of relinquishing control to a “robot.”
Though some marketers fear working with an autonomous machine, AI can’t do its job on its own. A machine will never have a marketer’s deep knowledge of a particular brand, nor understand the subjective factors that influence a customer. Its job is to translate customer insights into actionable marketing outcomes; collect, integrate and manage data; and operate fast enough to keep up with the rapid pace of interactions.
This shifts the role of humans so that we do what we do best while pulling us out of the data weeds once and for all.
3. Consider whether it makes sense for a brand to ‘become an agency’
Marketers’ relationships with their agencies are changing. In 2016, 37% of marketers reported feeling overly reliant on their agencies for driving marketing strategy. This year we’re seeing a complete shift, with 42% of marketers saying that they’re exploring the potential of “in-sourcing,” or taking part of their digital media and/or creative in-house, and 24% already intending to do so.
Brands like Unilever are leading this new trend toward in-sourcing select marketing functions and making it look very appealing by sharing numbers such as the €500 million it saved in 2018. As Unilever discovered, there are, of course, challenges that come along with replicating agency teams — namely, hiring, retaining and organizing staff, and scaling operations.
AI is one of the strategies brands are deploying to make in-sourcing possible. Insourcing is not just about adopting AI, though; it’s about restructuring internal and external teams, bringing in an AI operator to act as a go-between between the machine and creative teams, and collaborating with the technology in an autonomous capacity rather than simply using it.
Moving from an assistive solution to an autonomous one is the first step in bridging the AI opportunity gap. From there, it’s about rethinking the division of responsibilities between human and machine, adopting a forward-thinking approach to working with this new technology, and deciding which capabilities are absolutely strategic to in-source and which are better left outsourced.
Article previously featured in Forbes, Three Ways Brands Can Bridge The Opportunity Gap, June 24, 2019
Infographic: Why Is AI Disappointing Marketers?
A new study, conducted by Forrester Consulting on behalf of Albert, revealed that 88% of marketers today have adopted – or are in the planning stages of adopting—artificial intelligence, and that the benefits vary according to both the type of AI they have adopted and their application of AI in their marketing programs.
Of those that have adopted an AI-driven marketing solution, 74% of respondents reported using assisted AI technology, which surfaces insights for marketers to consider during manual decision making. Only 26% of marketers reported using autonomous AI, which can act on its own insights and work collaboratively with marketers.
Check out the infographic for more findings about marketers’ journey with AI.
New Report: The State of AI-Driven Marketing Tech in 2019
The state of AI in marketing technology is transforming at a rapid pace. In just three short years the adoption of AI-driven solutions among marketers has more than doubled – from 43% in 2016 to 88% in 2019. With a steep adoption curve, substantial capital investments, and big expectations on the potential to impact marketing efforts, many marketers want to know where AI-led marketing technologies stand today, what opportunities exist for the future, and how best to implement and integrate these technologies in order to leverage their full potential.
A recent commissioned study conducted by Forrester Consulting on behalf of Albert set out to uncover the sought-after answers to these critical questions. Here’s a brief look at some of the highlights:
AI in Marketing Tech: Expectations Versus Reality
How impactful is AI in helping marketers to address the complex needs of today’s digital ecosystem? While adoption of AI solutions among marketers is high, the study shows that the implementation and impact of these solutions to date have not met marketers’ expectations. In fact, according to survey results, more than 50% of marketers leveraging AI technologies are still struggling to solve many of the problems they hired AI to do, such as improving the effectiveness of marketing campaigns, improving the customer experience, and increasing customer retention. This study addresses some of the current challenges and why the future for AI may be brighter than it looks.
What are the Secrets to Success with AI?
What is more interesting is why some marketers have had more success than others. The study points to the methodology marketers are employing to leverage AI as a key factor in their success. In fact, of the 88% of marketers using AI technologies today, only a small minority are leveraging a collaborative, autonomous AI approach, while the majority of marketers are employing a more limited, assisted AI methodology (see Figure 2.1 below). Read the study to learn how autonomous AI marketing is driving results.
Tips to Leveraging AI’s Full Potential
As early adopters have learned, tapping the full potential of AI requires more than simply adopting the technology; it requires a new approach to the marketing organization and its processes. Marketers will need to rethink the impact of AI-led technologies and how these new capabilities will inform strategy, decision-making and execution across the organization. This report includes insights and best-practices for marketers today.
Want to learn more about the state of AI Marketing in 2019 and how to leverage AI most effectively? Download our report to find out more.
What D2C Retail Brands Need to Know Before Using AI For Customer Acquisition
The once-clear boundaries between brands and retailers have faded. Brands have learned how to be retailers, and retailers find themselves in competition with the very brands they carry, which are now selling directly to consumers.
This has resulted in an e-commerce marketplace with twice as many sellers as there were just 10 years ago. Consumers are overwhelmed with choice. The price of customer acquisition has skyrocketed. And with so many touch points across devices and channels, brands and retailers can barely keep up.
This complexity and the resulting deluge of data is too daunting for humans to handle alone, but it presents an ideal environment for machines. Having worked with pretty much every kind of direct-to-consumer (D2C) brand as they turn to artificial intelligence (AI) to handle different aspects of their customer acquisition and paid digital marketing efforts, I’ve identified three distinct learning curves that marketers inevitably encounter along the way.
1. AI thinks in terms of micro-personas, not buyer personas
Not all AIs are created equal, nor do they all do the same thing. Some AIs surface insights for humans to act on, while some act on those insights autonomously. But none of them act like a know-it-all. Successful D2C brands and e-commerce marketers have often done a ton of work to identify and get to know their audiences, which AI will likely confirm, but in seeking growth, they shouldn’t limit AI to their knowledge alone.
Given the chance, an autonomous marketing AI will explore every nook and cranny of the channels it’s working in to uncover opportunities and audiences in its own way. While brands might define their audiences in the form of several distinct buyer personas, complete with psychographics, AIs generally creates thousands of micro-personas. Examples of this might include “people who search for diamonds online respond to motorcycle ads at a disproportionately high rate,” “men who identify as engineers on Facebook are engaging 300% more with jeans ads than those in other professions,” or “men over the age 65 in Sydney, Australia, who like to go flying are likely to buy experiences as gifts for friends and family.”
Often times, these micro-personas don’t fall neatly into existing buyer personas, which presents brands with an opportunity to gain an understanding of the long tail of their customers. While it can be tempting to reject these new audience types as inherently “wrong,” AI reaches statistical confidence much quicker than a human does.
Marketers can use these insights in many ways. The most immediate would be to introduce micro-campaigns for high-potential micro-personas. Such a campaign might consist of highly targeted visuals and copy tailored to resonate with these niche audiences on a granular level. Upon clicking on a promotion, these consumers could be taken to a dedicated landing page, designed and populated with content specifically for them, resulting in a consistent experience and narrative, from initial ad engagement to the brand’s site.
2. Different rates of creative fatigue mean that testing and learning take on new importance
Direct-to-consumer marketers are constantly surprised to learn how quickly consumers suffer from ad fatigue. In other cases, they’re surprised to discover creative exceptions that continue to work profitably long after brand managers think the ads should be retired.
If you’re a part of a retailer or brand that’s using AI in digital advertising, then you need to make sure you have strategists on the team who can understand what’s working, what’s not, and why, and provide appropriate direction to creative resources who can then produce content (images, videos, headlines and captions). And since AI targets hundreds to thousands of micro-personas, rather than only a handful of buyer personas, brands are able to test every idea they can come up with to personalize their efforts to a far more varied consumer mix.
3. Autonomous doesn’t mean … autonomous. Don’t forget to implement your humans
Left to its own devices, AI will focus purely on making data and technology decisions that lead it to the goals it’s given. Brands are great at setting revenue or customer acquisition targets, but it is important to have an AI that can accept various kinds of guardrails. Without established boundaries, some of the decisions an AI will make in pursuit of meeting its goals might be off-brand, appear ill-informed or just seem odd to the consumer on the receiving end.
Take, for instance, brands targeting very specific markets (such as those selling warm-weather clothing during the winter), buyer types (e.g., luxury shoppers) or demographics (baby products or men’s shoes). In the case of a luxury marketer, the AI might discover that a discount offer is performing particularly well and therefore scale the use of the promotion to meet its acquisition goals even quicker. But the brand, not wanting to be viewed as a discounter, will likely see this as a brand positioning fail.
AI also doesn’t know it should be showing a dedicated creative set on holidays; it only knows what creative is converting. It doesn’t know which topics are sensitive; it only knows that some topics attract more visitors than others.
There are also more nuanced kinds of considerations that human strategists must share with AI. For example, a customer of ours, a direct-to-consumer sustainable meat and fish grocer, finds that when there is mention of a food recall in the news, consumers become more aware of their food choices. AI isn’t tuned into the news and wouldn’t know about a salmonella outbreak, so it relies on its human colleagues to equip it with specific messages that cater to consumers looking for information about what they’re putting in their bodies.
Long story short, even artificial intelligence needs human insights and strategic guidance to be successful. And while AI might discover new high-potential audiences, it’s up to the brand to determine its approach to interacting with and engaging those audiences.
Brands that set up guardrails and master the art of collaborating with a machine rather than simply operating it can then let the machine run free to do its job.
Article previously featured in Forbes, What D2C Retail Brands Need to Know Before Using AI For Customer Acquisition, April 17, 2019