Why Optus Chose Albert For AI Marketing


Optus AI Interview

At a recent iMedia event, Naomi Simson, Co-founder of the Big Red Group, interviewed Angela Greenwood, Director, Acquisition & Customer Marketing at Optus, about her thoughts on:

  • The future of marketing – and our addiction to attribution
  • The role AI will play – and what people fear the most
  • A marketer’s priorities, a melding of analytics, creative and the big idea.

Naomi Simson: At Optus, there was a real sense of urgency driving the implementation of AI technology. What were the commercial drivers behind this strategic direction?

Angela Greenwood: It was a few things. It can be very, very difficult for us to understand what the right level of investment across channels is in digital, and how we move money between these in real-time. Often, it’s very much a siloed approach to how much investment we’re putting into each digital media channel. We wanted to understand if we gave an autonomous AI tool the freedom to move funds between all those channels, and to serve the ‘right’ creative in real-time, what would happen? So there was a healthy amount of curiosity about what we could achieve.

NS: In a marketing environment where the open web is becoming a thing of the past and we’re dealing with the ‘Walled Gardens’ – each of them claiming they ‘own the customer’ – the notion of attribution becomes even more complicated. We as marketers focus so heavily on attribution, so what was that conversation like on your journey to AI

AG: We still have a very healthy interest in attribution. We still put a lot of effort into investigating effectiveness – but you can only ever look at that retrospectively, not future-facing. And for us, the amount of sessions we attribute to one single channel –  when we know that a customer’s journey is way more complex than that – means we can get really tripped up on thinking one channel or piece of creative is more or less effective than it is in reality. What we’ve been able to see with AI is that some of the creative constructs that work today, no longer work tomorrow – so we want to be able to see that in real-time.

NS: Being able to test ideas at scale was one of the driving factors that attracted you to Albert AI. Tell us about that.

AG: AI can take a lot of the heavy lifting away from some of the lower-level tasks around digital media buying. But what it actually creates is a lot more tasks around how you feed this engine with enough creative to be able to personalize at scale to prospecting leads. Because the possibilities are endless – they’re only really limited by what we’re able to put out there.

NS: Let’s talk about the agency relationship, and the human side of AI and what it means for the people on the team and their concerns and challenges.

AG: I was pretty concerned and challenged myself. As performance marketers, we have a bit of a reputation for being just a little controlling, so to be able to go from a very detailed digital marketing plan that’s broken down to the nth degree, to a single line item that says, ‘here are the dollars, go do’, that’s terrifying. It was really important to get the set-up right. We had to get back to basic things like getting our naming conventions right and making sure all of our campaign structures were set up correctly.

NS: I can imagine there was a lot of fear for the individuals involved – but as you’ve noted it’s really the low-hanging fruit that AI takes care of, to really free up the people to focus on those interesting strategies and learning pieces. Tell us about that journey for your people, and your agencies, and how they shifted from the execution to the strategy.

AG: It will never cease to amaze me how much people will cling to low-value tasks. So once you separate your people and your agencies from all that, you free yourselves up to think more strategically – like how we’re going to tailor our value propositions to different audiences, how can we actually do that really personalized creative at scale, and how can we take the insights we’re getting from the engine and do something with it? And that’s the really big step that we’ve been able to take. And also, because this type of tool works very fluidly across all the different digital channels, it has actually opened up opportunities for our internal teams to become more cross-functional, and they’re now thinking much more holistically about the customer journey.

NS: What does success in marketing look like for Optus?

AG: It’s a number of things. We want to be as efficient as possible. Digital still pays a really massive role in driving brand consideration, but it’s also really important for us to keep building the top of the funnel through that activity. It’s really important with an AI solution that you’re actually pointing in the right direction – because if you feed it the wrong signals and optimise towards the wrong thing, it will go really hard after the wrong thing. So that’s been a really important learning – how do we make sure that we’re actually optimizing the right activity to the right result? And that’s never going to be uniform across everything that we do.

NS: Let’s talk about cookies. We didn’t just set this up for now, we wanted to plan for the future – so how does this AI solution deal with privacy issues and knowing who your customers are?

AG: For advertisers, the most important thing we can do is make the most of all our first-party data, and being a telco, that’s a unique position to be in. We do actually have a fair bit of that, so for us, it’s about how we can we utilize that first-party data for the AI to find suitable look-a-like audiences. And then how do we incentivize uses to engage with our own platforms? How do we ensure we get more people using our app so that we are not dependent on the outside world for that view of our customer.

NS: Do you have any particular campaigns you have run that have helped you identify intent to purchase?

AG: Where we see value in Albert and how some of our assumptions have been challenged is around thinking that certain audiences have intent for a certain product, and how Albert then runs through a full range and offers alternatives. We’ve actually found some really interesting crossovers between audiences in terms of intent that we never would have understood before Albert. For example, if someone has intent to buy a post-paid mobile, they actually have a strong intent for accessories as well, so then how do we capture that as part of the ongoing conversation with that customer?

NS: That’s what AI does particularly well – it’s looking for, based on certain previous behaviors, that intention data, and taking all of that information to start predicting ‘what next?’ It’s almost impossible for a human to do that.

AG: You would need a massive team of data scientists to achieve anything similar at scale. And anyone who’s tried to hire a data scientist knows how hard that is. Albert enables us to do all this very rapidly.

“We have had a very established test and learn program at Optus for many years, but the speed now and the scale at which we can get those insights is just so much faster now. We are an incredibly competitive category – telco is a blood sport, and it’s very much about how you gain market share and take market share from competitors. So we will do anything we can to get a competitive advantage.”

NS: What does the future look like for Optus and your AI journey? And I’m talking no more than the next 6-12 months.

AG: For us it’s about how do we get more signals in? How can we get more data in for that to work for us? How do we do a better job of ingesting offline data to optimize towards an omnichannel result, and how do we better leverage our first-party data? And it’s also about the creative side – we have only dipped our toes in terms of what Albert is able to do when it comes to creative optimization, so for us, it’s about how we set up so many different creative variants to be able to really maximize results.

“Don’t fear the machines. It frees us to be more creative and more strategic and that’s a win for the client and agencies.” – Angela Greenwood.

Read how other clients used Albert’s AI capabilities to fuel their digital marketing and advertising

Albert is distributed in Australia by Marketics, a wholly owned subsidiary of the Big Red Group. This interview was originally published by Naomi Simson.

by Or Shani
CEO & Founder


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