It didn’t take long for the use of Artificial Intelligence in marketing and paid media campaigns to catch on as the new shiny buzzword in the ad tech and mar tech industries. Like many of the trends that consume these industries, most were excited about the potential implications – enhanced personalization at scale, real-time adjustments, smarter media buys etc. – followed by an inevitable shadow of doubt once people began to realize that it was a bit more complicated than they first anticipated.
Skepticism around Artificial Intelligence
Before we dig into why artificial intelligence in marketing actually does deliver on its promise, we first need to take a step back. Artificial Intelligence is not the first thing to complicate digital advertising. Programmatic advertising – the automated buying and selling of digital media – introduced incredible complexities. Even though it is predicted that 82.5% of all US digital display ads will be bought via automated channels in 2018, 10 years after it emerged, many people in the industry are still trying to get a solid grasp on it. Nevermind regular consumers.
So it makes sense that when another extremely complex technology, Artificial Intelligence, emerged as the next game-changer in digital advertising, many were excited at its promise, but confused at how it will actually work and then eventually skeptical if it works at all. For the skeptics, I am not going to explain how it is already being leveraged every day (more on that here), but why it really will deliver on its promise of optimization.
Programmatic decisions are made in real-time and are based on the trillions of data points in the digital ecosystem. It is similar to how software developers make use of application intelligence to see all the connection points between the app and database. For marketers, it is really important to extract the maximum amount of value and understanding. However, when it comes to data, it is yet another code that marketers have had a tough time cracking. It is one of a brand’s greatest assets, but also presents the most challenges, ranging from aggregation, organization, and utilization:
- Siloed data across all marketing channels (email, CRM, paid media etc.)
- Analyzing this data and applying insights to make customers feel like each experience is unique and personalized to them
- Using 1st and 3rd party data to scale content while maintaining relevant, personalized experiences for consumers
High consumer expectations for tailored experiences
Content on demand has instilled a need for instant gratification in consumers. Brands want customers to feel like they understand them and what content they require in any given moment. The best way to create this feeling is to tell personalized stories and create tailored experiences for individuals, and then use the best digital marketing strategies to increase the reach of these stories and Grow Your Business as a result.
Before the concept of “real-time” became part of every marketer’s plan, personalization was achieved through batch processing. Data scientists would analyze weeks or months worth of consumer behavior data, derive relevant insights and then activate paid media and marketing campaigns some time later. However, as discussed earlier, consumers have high standards for the content they consume and for the experiences that brands provide them, so data that is weeks or months old, more often than not won’t lead to an optimized experience.
So while there is an element of “learning” that occurred here, it was nowhere near as efficient, effective or impactful as identifying even deeper insights and then using them for real-time media buying decisions.
Personalization made possible by advanced technologies
As human beings, our brains are perhaps the most obvious example of Artificial Intelligence in action. As we go through life, meet more people and experience new things, we are constantly learning and becoming smarter, retaining knowledge that will help us make more insightful decisions in the future.
Now think about this same process, but apply it to the computer that is processing billions of data points per second and automating media campaigns. Consumer actions are expressions of their wants and needs. Might seem insignificant, but every single action taken by a consumer is remembered and made actionable for future decisions.
AI promises to deliver this personalization, but in real-time and with much more relevant data. This is probably why AI has so much value in business and also health analytics. The latter has gained much from machine learning (part of AI), with respect to finding misdiagnoses and clinical decision-making. It is always learning and improving over time, becoming an all-knowing entity. Anyway, AI is driving exponential value out of data, ad tech, and mar technology, extracting insights in real-time and delivering personalized messages.
More than just tailored consumer experiences
Personalized marketing messages at scale is the glaring opportunity that harnessing Artificial Intelligence offers brands: through real time adjustment and learning, personalized customization can happen in real time. But there are a lot of other benefits. The first is greater ROI: marketers can truly make the most out of every dollar by making decisions based on data from the decision before, thus maximizing marketing budget and performance. The second is innovation. Marketers can optimize media in new ways by taking unstructured, unused data and making them relevant to paid media campaigns. The third is ad tech: AI can optimize data within and across paid media channels in real-time, driving a seamless consumer experience.
AI joins programmatic, mar tech, and ad tech efforts into a unified approach that maximizes data and assets to offer a smoother consumer experience and greater ROI. While skepticism still exists, advertisers, marketers, and brands should learn quickly the power of Artificial Intelligence and harness its capabilities, or risk lagging behind.
Bidtellect now offers the most advance Forecasting technology on the market – by harnessing the power AI. Read more: Forecasting 2.0: The Future of Contextual Targeting
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