The Applications and Challenges of AI in Digital Marketing

Marketing, at its rawest form, is about people communicating with other people. But it’s not that simple.

Humans have the ability to produce successful advertising campaigns, measure said advertising, and improve their ads based on what they’ve learned, but digital advertising across search, content, and social media channels gives us an almost unlimited ability to generate data on what works and what doesn’t. All of these disparate data sets"measurable impressions, click-through rates, bid levels, demographics, and more"make advertising very difficult to scale.

Consumers are now in the driver’s seat of what content they want to engage with, and also the time and format they want it in. These options not only increase the complexity of an already complicated media ecosystem but also increase the expectation of consumers. People want relevant and engaging content, not top-down advertising.

Artificial intelligence (AI) is a major industry talking point as of late as platforms like Facebook and Google have steadily refined its machine learning to offer extremely targeted placements with high ROI. It’s no surprise that the vast amount of data collected by these players enables such targeted advertising, but it also reveals two separate challenges. The first is a heavy reliance on big networks like Facebook and Google, especially during a time where its power as a monopoly is in question by policymakers and regulators. The second—​it’s clear that AI’s utility in advertising is primarily relegated to a biddable, programmatic environment.

But why is this the case? AI’s use in media buying is often difficult to convey to marketers due to the sheer complexity of how it operates. That’s another reason why marketers primarily rely on Facebook and Google’s AI, rather than building out a machine learning platform of their own.

Times are-a-changin', though and for the first time, it feels as if the industry is finally wrapping its head around the future of artificial intelligence. High performing marketing teams are averaging seven different uses of AI and machine learning today and just over half (52%) plan on increasing their adoption this year. Even IBM is taking the initiative to establish a set of standards for artificial intelligence in hopes of making its applications more transparent.

The real challenge, however, comes with figuring out the most optimal combination of AI technologies, and thus, requires creative thought-leaders who can synthesize these disparate technologies to create a more robust framework for digital advertising. The future of AI Isn’t just reliant on its technology, but the way that humans leverage it. With the right approach, humans can be both the drivers and beneficiaries of technological change, helping us reinvent our organization and realize our full potential.

To accomplish this, you must ensure that you are meeting the needs of your real customers, and the most-cutting edge technology simply isn’t a lasting solution. That’s why we’ve decided to explore the ways in which AI will completely change the marketing landscape to hopefully give you a few ideas for what’s to come.

Budget and Campaign Optimization

The most common use of AI currently leveraged in digital advertising is budget and bid optimization. AI systems can automatically manage ad performance and spend optimization, making autonomous decisions about how best to reach your advertising KPIs. While this application may not be anything new, the growing number of channels and platforms that allow for advertising opportunities means that there is a massive opportunity for AI platforms to come in to manage spend across these different channels.

These tools can analyze how your ads perform across platforms and offer recommendations on how to improve performance and allocate budget. These processes can often be automated if one chooses. For example, GeistM’s Blackfire technology enables our strategists to understand how each channel affects one another through a comprehensive "stories" report that gives visibility in terms of where attribution occurred.

Ad Targeting

For years, advancements in AI concerning ad targeting were few. This was mostly due to Facebook and Google’s access to massive datasets that sprawled the Internet. However, with more testing across different platforms and channels, marketers can now aggregate data sets to enable more predictive targeting. This is especially pertinent as Google Chrome plans to phase out 3rd party cookies by 2022. After aggregating your data across publishers, partners, and platforms, you can use AI to examine these audiences and identify new audiences with high purchase intent. With privacy becoming a fundamental problem in the industry, AI can help personalize advertising while mitigating this issue.

Content Recommendations and Ad Personalization

Personalization is often a challenge for marketers as every consumer’s experiences should ideally be slightly different. One of the most helpful uses for AI comes in the forms of its natural-language processing (NLP) capabilities. These processes can help create ad experiences based on previous content experiences to engage one-on-one with the consumer. Natural Language Processing works best performing sentiment and contextual analysis on past content to identify what language, syntax, and (obviously) content will perform the best. Such insights will allow content creators to construct more engaging headlines, and more importantly, allow for more testing opportunities when it comes to branded content.

And while AI algorithms may not be advanced enough to write long-form compelling copy, that doesn’t mean that it can’t write at all. Natural language generation (NLG) is a subset of NLP and uses the same data from sentiment analyses to nab and deliver relevant and personalized experiences. While this technology may be in its infancy compared to the other two, its potential is seemingly endless.

Summary

It’s clear that we’ve really only scratched the surface and that’s not to say that AI’s applications within advertising are simple to implement. Considering the numerous ways people consume content, it is incredibly difficult to train machines to anticipate where the next dollar should go. Right now, we’re not really seeing as much of a change in output as much as a better way of doing things, but as AI standards continue to be developed, it’s almost certain that the future of online advertising rests in the hands of artificial intelligence.

More importantly, it’s obvious that the transformative effect of combining emerging technologies will be far more instrumental to business innovation than a single technology. Technological advancements aren’t typically derived from isolated moments of success, but build off each other in an iterative process—​combining existing ideas in new and unique ways. That’s where humans come into play. While many people anticipate that automation will hinder the availability of jobs, there is still a need for creative thinkers who can synthesize multiple technologies and processes to achieve a desired outcome.

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