Automation + AI: The Chocolate & Peanut Butter of Ad Tech


chocolate peanut butter adtech

Artificial intelligence (AI) is sometimes confused with automation, and the terms are often used interchangeably. When talking to vendors, it’s critical for digital marketers to decipher exactly what’s being offered so you know it will truly address your needs. Today, let’s demystify these terms and discover what happens when the two are combined.

Differences Between Automation and AI:

Robotic Process Automation (RPA) software is great for simple activities and repetitive tasks that follow instructions or workflows set by individuals. It is best suited for highly repetitive and predictable tasks. Automated tools require manual configuration and human supervision to effectively execute campaigns. The trick with RPA is for humans to anticipate every permutation so the machine is programmed to behave the right way every time. This is why constant vigilance is required. If the environment changes, marketers must manually step in and make the necessary adjustments.

AI refers to how computer systems can use huge amounts of data to imitate human intelligence and reasoning, allowing the system to learn, predict and recommend what to do next. An AI capable of understanding marketing KPIs can use various algorithms that act in concert to find signal in the noise of data and find paths to solutions that no human would be capable of. Most AI today works in an assistive fashion, providing next best action recommendations to humans who then decide whether to trust them or not and then manually make adjustments.

Combining AI & Automation: Sweet Treat!

When robotic process automation is combined with elements of AI such as machine learning, the result is known as intelligent process automation (IPA). An IPA tool is powerful because it allows us to reap both the benefits of automation – increased speed, efficiency, time-savings, and ability to scale – with the insights, flexibility, and processing power of AI.

Marketers who use IPA are able to augment their capabilities, while off-loading repetitive campaign management tasks to the machine. It’s different from pure robotic automation in that the AI can start, stop or even alter what it is doing based on the environment in which it operates. What’s more, because the best AI systems allow marketers to set guardrails, there’s no chance of unforeseen events taking outcomes too far astray. For marketers, this means faster, more personalized execution and processes, greater use and accuracy in data, and improvements in overall customer experience. Marketers shift from fussing over bid adjustments and budget allocations to higher value add, human-centric contributions like, “how do we evolve our value proposition to drive more business?” Because of these clear benefits, Forrester predicts that by 2020, 25% of Fortune 500 companies will report hundreds of examples of IPA use cases.

Benefits of AI + Automation for Marketers

IPA technologies not only surface insights for marketers but actually turn insights into action. For example, Albert can synthesize historical digital campaign data across channels, craft strategies for execution, and explore different combinations of messages, creatives, and frequency across audiences. Evolving relentlessly over time, the intelligent machine’s autonomous capabilities allow it to actually shift budgets, adjust bids, audiences and optimize campaigns 24×7 in relentless pursuit of KPIs that a marketer has set.

This is especially important as customers continue to demand more from brands; Salesforce’s Fifth Annual State of Marketing Report revealed that 53% of customers now expect personalized offers, and 62% expect businesses to anticipate their needs. IPA technologies are becoming the only way to deliver personalized touchpoints for an optimal customer experience across paid digital channels.

Key Takeaways

Implementing intelligent process automation can help marketers achieve better paid digital campaign results while surfacing customer, media and market insights that inform not just marketers, but the overarching business strategy. In the face of potential economic slowdowns or other unforeseen external market conditions, marketers are finding that new technologies like IPAs can help them innovate, scale and increase efficiencies to stay competitive.

To learn more about how Albert plans, executes and optimizes paid digital marketing campaigns, contact us.

by Mark Kirschner


What is Machine Learning?


machine learning abstract

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. 

by Diana DeMallie
Marketing Manager


Meet AI, Your New Creative Teammate


AI creative partner

Creativity has always been the crux of advertising. So while artificial intelligence (AI) has been routinely used across digital media buying and ad campaign automation, why haven’t marketers fully embraced AI in the creative process? A recent report by Forrester Research reveals why AI is a critical piece of the creative puzzle and offers steps brands can take to inject AI into their creative processes.

AI Will Enhance Creativity 

Using AI for creative purposes doesn’t mean robots will be writing poetic prose or designing ads. AI’s role as a key player in creative teams is to provide the insights that stimulate thinking and inspire human creativity, enhancing a creative professional’s ability to develop and execute creative ideas.

According to Forrester’s survey, 74% of creative respondents reported spending more than half of their time on tedious tasks. Leaving mundane tasks to AI will allow them to shift their focus to making creative breakthroughs by experimenting with new methods and emerging formats.

Likewise, AI’s involvement can solve bandwidth challenges. Consumers are typically exposed to hundreds of digital ads in a given day. It’s crucial to optimize content and adapt creatives for different formats to effectively connect with target audiences at every journey stage. However, marketing and advertising teams simply don’t have the time or capacity to create large volumes of content. Enter AI as a new teammate, pitching in to customize and automate the creation and distribution of mass amounts of content that marketers can then use to effectively personalize their digital campaigns at scale.

Using AI in the Creative Process

Implementing AI early on adds more data-driven analysis and measurement throughout the rest of the process. When AI is incorporated in earlier stages, CMOs can improve the creative brief and reduce guesswork for their teams.

Brands need a deep understanding of the customer journey to deliver personalized experiences for customers. This requires two things: customer journey analytics and correct signals. Armed with these two tools, marketing teams can create always-on creative content across channels, formats, and devices. CMOs who rely on powerful data and technology to fuel their insights will have more opportunities to surprise and delight their customers – especially during critical moments.

AI Surfaces Real-Time Insights

Ads with an emotional pull tend to drive greater conversions, and creativity spurs the development of engaging, emotional ads in a saturated digital ecosystem. Because the rate of creative fatigue is so high, the ability to quickly see new creative insights – and learn what is or isn’t working – can make or break digital advertising campaigns. 

For Telenor, a global telecommunications company, this ability to glean creative performance insights in real-time was a key reason why they achieved a 423% increase in ROAS. Telenor worked with Albert to run multivariate creative and media testing across its digital campaigns. Telenor’s team could continually see fresh insights from Albert to become better informed about their audiences, understand what creatives were working, and learn strategies to incorporate in future campaigns.

Deliver on Your Brand Promise

By including AI in the creative functions of marketing and advertising, CMOs can leverage AI to foster better connections with consumers, surface previously hidden customer pain points, and deliver on their brand promise. Download Forrester’s report to get recommendations for applying AI into your creative process. 

by Mark Kirschner


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.

by Or Shani
CEO & Founder


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.

by Diana DeMallie
Marketing Manager


2020 Marketing AI Predictions


road to the future

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.

by Or Shani
CEO & Founder


What We’ve Discovered About Implementing Autonomous AI


People Learning from AI

At Albert, we can already see that in the very near future, all paid digital campaigns will be executed by autonomous artificial intelligence (AI) guided by marketers. Through our experience helping Fortune 500 clients on their journey toward overall business transformation, we’ve discovered the following insights that might be of interest to brands who are thinking about implementing an autonomous, cross-channel AI marketing solution like Albert.

Why Collaborate With A Machine?

People have unique strengths. We can empathize, be creative, or be inspired to come up with ideas seemingly out of thin air. On the other hand, an AI-powered machine can transact, iterate, predict, and adapt to changing conditions at a pace beyond our comprehension. When marketers collaborate with an intelligent machine, the AI empowers them to expand their reach far beyond the capability of traditional advertising technology. Today, a machine acting intelligently on behalf of marketers can paradoxically enable a brand to present itself as more human, personalized, and flexible. 

When marketers implement autonomous AI, it means that instead of operating the technology, they start to collaborate with it. This often involves shifts in a team’s mindset — especially for those with sophisticated domain expertise. Machines take a different path than humans would, because they can focus on everything at once, as opposed to the way we work, balancing just a few variables in order to find performance. As a result, it is important for brands adopting AI tools to ensure that implementation not only integrates technology but people as well.

Digital Marketers Get To Shift Their Focus

Marketers who adopt autonomous AI are not always prepared for subsequent shifts in thinking required by the human team. Insights gleaned from an AI tool may surface learnings that shed light on previously undiscovered patterns or insights that change a team’s priorities or strategy. For instance, what does it mean that the machine uncovered a new audience the marketers didn’t know they had? Or what are the implications of hundreds or thousands of long-tail terms that the machine is able to make perform profitably? When brands adopt AI to take on rote, repetitive campaign management chores, marketers find they are able to use their traditional marketing strategy and creative skills to unearth the meaning of the AI’s discoveries, find stories in the data, and craft compelling value propositions from them.

Surprise Becomes a Way of Life

Brands often encounter startling revelations when first implementing autonomous AI. For example, rather than crunching campaign data, more energy starts to be spent on making decisions related to what creative elements should be provided to the AI as well as thinking about overall campaign direction. Because the AI tool’s cross-channel capabilities deliver messages to the right person at the right time, human expertise can be redirected from a narrow focus on bid, audience, and budget allocation tactics to asking more strategic questions such as: are my messages resonating? And, is my overall marketing working?

Additionally, the rate of creative fatigue can be eye-opening for brands. Because the machine offers scaled capacity to efficiently test creative iterations at a pace that was never before possible, teams get to test as many variations and ideas as they can create. Marketers find suddenly they can test and learn more quickly, understand when to use a rational or an emotional proposition, and develop ever more learning agendas to feed the intelligent machine. Unlocking actionable insights and discoveries often steers teams to try new entirely creative techniques and tactics.

Similarly, the process of reviewing campaign performance speeds up to real-time from once a week (or month). And suddenly, those reports are actionable. In pursuit of performance, the AI may request more budget in one place while limiting spend in another. Or, the system may inform that engagement with one audience is increasing far beyond another, so it begins looking for more of the same in other ways. Marketers are on point to think about what opportunities this creates for other parts of their business.

AI Implementation Takeaways:

Overall, we’ve found that implementing an AI creates new vistas for marketers to return to their roots: storytelling, using creativity, and building value propositions that resonate with consumers.

While the implementation is fast, learning how to fully utilize an autonomous AI to deliver on the promise of business transformation takes time. But, with adequate planning and preparation of staff, companies can speed up their path toward successful human+machine collaboration.

To learn more tips about autonomous AI, download this commissioned report conducted by Forrester.

by Or Shani
CEO & Founder


How to Achieve Cross-Channel Advertising Success


woman crossing street- iphone

What is Cross-Channel Marketing?

Hubspot defines cross-channel marketing as “blending together your various marketing channels in a way that creates logical progression for your target audience to progress from one stage to the next.” In other words, cross-channel marketing involves using different channels to make sure consistent, holistic, and seamless messaging is reaching your target audience across devices, technologies, and platforms.

The Importance of Personalized Marketing

Cross-channel marketing is the best way to reach more people, build better audience connections, and drive ROI. According to last year’s Salesforce State of Marketing report, 84% of customers say being treated like a person instead of a number is very important to winning their business. It’s so important that 69% of buyers even expect Amazon-like buying experiences – like very personalized recommendations – that only a truly cross-channel approach can deliver. Marketers have realized this need by noting it as their #1 priority; however, it’s also their top challenge. Only 49% of marketing leaders report providing an experience that completely aligns with customer expectations. High performing marketers stand out by delivering personalized messages to the right people at the right time on the right channels.

Solving Cross-Channel Complexities

Delivering personalized messaging is daunting, so many marketers avoid coordinating across paid search, social and programmatic channels due to the complexity and speed required to execute. With so many moving parts to keep track of, it can be nearly impossible for even the most sophisticated marketing teams or agencies to break down silos, sync up, and maintain consistency across audiences, campaigns and promotions.

So, how can marketers execute effective cross-channel campaigns to keep up with the rapidly changing demands of consumers? Intelligent, cross-channel marketing AI software is proving to be the only way.

Machine Learning: The Cross-Channel Answer

There are inherent limitations when people manually optimize campaigns, try to align messaging across paid channels, and aim to inform other teams about insights gleaned from campaigns. Artificial intelligence tools can manage the complexities of personalized marketing involved in cross-channel campaigns at a pace and scale not humanly possible.

Autonomous AI software can conduct time-consuming, data-intensive tasks required to compete in today’s digital advertising channels. The technology can ingest and precisely measure mass amounts of structured and unstructured data from multiple touchpoints and interactions across channels. It is technology that can learn on the fly and execute on marketers’ behalf in ways that were previously impossible. Marketers suddenly discover that it is possible to master the challenging digital advertising landscape.

Successes With AI

Albert, the world’s first autonomous marketing AI, manages cross-channel, paid digital campaigns by using a complex, multivariate approach to relentlessly evolve and optimize towards the goals marketers provide.

Within the guardrails set by an agency or brand, Albert will optimize bids, shift budgets, and test creative combinations 24/7. The intelligent machine gathers valuable insights about the customer experience in a way that has never been possible before. For example, AI can uncover ad variations that are resonating with segments of consumers at a certain stage, identify which creatives are outperforming, and unveil prospects that brands can target to increase conversions in other channels.

Additionally, Albert’s technology aligns to each brand’s source of truth to inform marketers about the contribution that each channel delivers during different customer journeys, as well as impact on total conversions. Ultimately, an AI tool like Albert helps marketers understand the most efficient paths to conversion, achieve greater visibility, and drive greater ROI.

Marketing AI isn’t just the future of digital advertising; it’s already here and is proving to be the ideal way for marketing and advertising teams to achieve cross-channel success and stay ahead of the competition.

To learn how one marketing team tied real-time insights to cross-channel digital ad campaign orchestration using Albert, read this case study.

by Diana DeMallie
Marketing Manager


Moving Digital Advertising In-House? AI Can Help


agency inhousing meeting

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.

by Diana DeMallie
Marketing Manager


The New Tenets of Digital Marketing #4: Restoring the Greater Function of Marketing


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. 

To download The New Tenets of Digital Marketing Ebook, visit this page.

by Ashna Shah
Sr. Director of Strategy