Nov 19 2018 Stefanie Hoffman Blog Search Automation How ML Will Transform the Advertising Industry It’s no secret that words like artificial intelligence (AI) and machine learning (ML) carry a lot of weight these days. No doubt, these technologies have changed industries for the better with the ability to process high volumes of data rapidly and accurately, providing analysis and actionable insights that can be leveraged in myriad strategic business decisions. Perhaps not surprisingly, ML is especially relevant to digital advertising. With its ability to not only compile consumer data, but analyze and predict future consumer behavior, the technology has significantly transformed the space and will continue to open up new doors for the industry going forward. Yet few people really understanding its specific role and its vast potential for advertisers. First, we’ll distinguish between AI and ML. Artificial Intelligence (AI) is essentially a computer system that can perform tasks that would normally require human intelligence, such as making decisions or understanding speech language. AI has applications to a wide variety of industries, including advertising. Machine Learning (ML) is a type of AI that uses statistical techniques to learn from data. When an AI computer system is set to perform a specific task, machine learning allows it to learn from new information as it comes in. It can then make changes to improve performance at a task without being programmed to do so. In advertising, AI and ML elevate the ability to make buying decisions that emulate human decision making. However, with the right AI technologies and data insights, it’s actually possible to significantly improve decision-making processes beyond human capabilities. Tackling the Data Dilemma The digital footprint consumers leave is huge, and it’s only continuing to grow. On Google alone, 40,000 search queries are performed per second. To put that in perspective, that’s 3.46 million searches per day. In order to create the most relevant advertising message and deliver it at the right time to the right audience, marketers need as many data insights as they can get. But, in light of the enormous amount of consumer data generated on a daily basis, advertisers are struggling to analyze and act on the information available to them. By the time they manage to derive insights and adjust their campaign strategy accordingly, a new, more relevant data set emerges. Only the largest and most competitive global corporations could hope to build an advertising team with enough data specialists to effectively tackle this kind of undertaking. While many advertisers don’t yet realize it, ML is the only viable solution to this growing problem. Among other things, the technology makes it possible to analyze and gain insights from vastly more data than a human ever could ever process in their lifetimes. What’s more, it also automates advertising decisions based on these insights, making ads more efficient and effective, and driving business revenue in the process. Building Better Audience Profiles Among other things, ML has the capacity to draw on data from a wide variety of sources, including search engines, social media, and other third-party platforms. It can then draw connections and make sense of it in a way that a normal human brain isn’t equipped to do. Consider social media, for example, where individuals discuss their interests and pain points, follow topics that interest them, and check in at various locations. In what’s known as cognitive intelligence, ML can use this information to build complex personality profiles. In addition to better understanding customer demographic information, marketers can then leverage these comprehensive profiles for insights into users’ thinking, behavior and even their next probable move. Thus, building better audience profiles makes it possible to create more relevant and targeted ads for potential customers — a capability that has become increasingly critical in recent years as ads have become more irrelevant to, and thus ignored by, consumers. Having a deeper understanding of the needs, pain points, and even your audience’s train of thought makes it possible to develop truly meaningful and relevant ads aimed at addressing their toughest challenges. Discovering New Audiences The same processes that make it possible to build better audience profiles can also help advertisers discover new audiences that they never before thought to target. ML can help uncover unlikely but valuable correlations between consumer demographics, interests, and online behavior that reveal new potential target audiences. For example, an AI system has the ability to analyze a large set of Facebook data and discover that older women who are interested in green living and like horror movies are more likely to buy a certain category of digital products. The data might not explain why this unlikely correlation exists, but advertisers can test out the opportunity by showing ads to them and accurately gauging their response. AI also makes it possible to develop a template of target audience characteristics that you can search for, identify and cross-reference across the web. As with Google’s similar audiences or Facebook’s lookalike audiences, you can discover new leads to target across the entirety of the internet, as opposed to just one platform. Using vast amounts of third-party intent data, ML can process this consumer information and make sense of it in new ways that lend themselves to better and more strategic decisions for your business. ML Saves Time, Creates Efficiencies Historically, most advertisers have relied on spreadsheets and basic analytics software to track their data and generate insights to optimize their campaigns. All too often, this becomes a tedious, error-prone, and laborious strategy that generates only marginally valuable insights. Artificial intelligence and ML, on the other hand, can automate this process, freeing advertisers to focus on discovery, growth, and other initiatives to improve the media-buying process. Because ML does this more rapidly and accurately than humans, it also results in more valuable insights. Depending on what kind of AI technology is being leveraged, it’s possible to improve insights even more by incorporating your own previous ad performance, as well as that of other advertisers, into your analysis. ML is also a worthwhile investment because it vastly broadens your dataset and analysis capabilities beyond that of advertisers. What’s more, it continues to grow in value over time as it learns from the performance of its own choices and uses this information to improve future campaigns. In short, the longer you use machine learning technologies, the smarter it becomes, thus increasingly emerging an essential tool for obtaining more ambitious advertising goals and metrics. Supporting Human Teams Employees tend to get nervous when their employer starts implementing AI and ML technologies — largely because they assume AI will supplant their jobs and minimize their value to the business. But in reality, the best way to use AI and ML is by allowing them to support internal teams, not replace them. In fact, using ML effectively can make advertising teams more competitive and relevant in the industry, which can actually increase their overall value to the organization. Also, because ML has the ability to predict the outcomes of campaigns, media buyers can leverage the technology as a check to help them avoid mistakes before they occur. If, for example, they accidentally clicked the wrong campaign setting or forgot to adjust their budget to match seasonal bid costs, these outcomes could easily be displayed in the predicted results, enabling the business to course-correct before a bad decision is officially executed. As previously mentioned, ML frees media buyers of tedious tasks and saves them time, providing more opportunities to focus on strategy and development. Applying ML to routine, administrative tasks means that advertising teams spend less time doing busy work, and more time working on creative, innovative projects within their department. While some businesses might envision a future in which advertising is 100 percent automated by machines, it’s more likely that ML will become an essential tool that advertisers will regularly rely on to support their internal teams and streamline workflows. Reducing Wasted Ad Spend ML can derive and help businesses act on new data insights faster than a human, or even a team of humans. At the same time, it can use advanced statistical algorithms to identify trends and predict future outcomes. That means it can help advertisers see around corners and make necessary bid adjustments to maximize their return on ad spend. Its ability to make changes quickly also helps businesses cut costs and reduce wasted ad spend, as it can identify unnecessary spending quickly that will enable you to lower your bids accordingly. Leveraging ML for more accurate and timely insights mean better ad targeting, making it possible to reach advertising goals with a significantly lower budget. The right advertising software will utilize an advanced decision engine to predict the best ad investments. By using important buying signals, data, and quick bidding options, these solutions can ensure you always effectively leverage your advertising budget to increase ROI. The Bottom Line ML is transforming the digital advertising industry on a daily basis, and as such, has the ability to dramatically improve the direction — and ROI — of your campaigns. Currently, AI has the ability to discover new audiences, build comprehensive user profiles, save time, reduces ad spend, and supports advertising teams in countless ways. That said, it’s only effective as the solution in which it’s being used. Advertising software can pay for itself and then some by improving the efficiency and effectiveness of your campaigns. And looking ahead, ML will become an increasingly necessary tool to add to the advertising arsenal, further accelerating the evolution of the digital advertising industry, as businesses find new and innovative ways to realize its true potential.