02/24
2020

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

02/24/2020

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
CMO

02/18
2020

What is Machine Learning?

02/18/2020

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

02/18
2020

Meet AI, Your New Creative Teammate

02/18/2020

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
CMO

02/12
2020

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

02/12/2020

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