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.

What is Machine Learning?

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

Machine Learning is Misunderstood

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

Best Tasks for Machine Learning 

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

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

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

Machine Learning is not a Black Box

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

Propel Your Business Towards Growth

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

Why We’re Not Panicked About The Future Of Third-Party Cookies

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

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

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

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

The Future of Creativity is Atomic

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

Meet Your Customers Where They Are

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

What is Atomic Creative?

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

How to Develop Atomic Creative

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

Get by With a Little Help From AI

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

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

Surprise: AI Makes Your Creative More Human

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

2020 Marketing AI Predictions

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

Marketers stop putting up with AI-washing

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

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

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

In-house marketers embrace human + machine teams 

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

Innovation in marketing will come from humans, not tech

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

Moving Digital Advertising In-House? AI Can Help

State of In-Housing Today

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

How In-Housing is Changing Advertising

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

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

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

Benefits of In-Housing

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

Biggest Challenges

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

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

AI is Helping Marketers Take Control

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

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

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

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.

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

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

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

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.