As we usher in a new decade, I can state with confidence that 2020 will be the year that Artificial Intelligence (AI) will become the preeminent technology for digital marketing. Just as AI is already transforming our experiences in our homes and cars, and on our mobile devices, AI will increasingly be embraced and effectively used in marketing and advertising to drive real world outcomes for marketers.
AI and Machine Learning
I will use both terms here pretty much interchangeably, but to be clear, AI is the intelligence demonstrated by machines that mimics the cognitive functions that we associate with human minds, such as learning and problem-solving. Machine learning is a subset of AI focused on developing algorithms that can learn from data and adapt in real-time.
Using Machine Learning to Drive Business Outcomes
Clicks, completes, views, and likes are not marketers’ true goals, but are baseline measurements of ad interaction. Many marketers use these metrics as measures of ad campaign success or failure. However, if you look at what marketers are ultimately trying to achieve for their brands, it is much broader. There are overarching business outcomes that marketers are working toward. These vary by vertical, but marketers ultimately want consumers to build awareness, buy their products, sign up for their services, visit their locations, and engage with their brands. For example, if you’re a marketer for a QSR brand, while engagement with ads does indicate success from an advertising perspective, tying ad exposure to a restaurant visit or sale is a much stronger indication of success.
Marketers who embrace AI can achieve these real-world business outcomes in ways never seen before. Using AI and machine learning, marketers can tie campaign exposure to a specific business outcome such as visitation, sales, prescription fill and more.
As marketers strive to understand how to best use AI and machine learning to achieve real world outcomes for their brand, IAB formed an AI Working Group, that I — and over 100 other members — joined. The collective result was our Artificial Intelligence in Marketing Report, the first guide of its kind. This vital report was created to offer a full picture of the benefits of AI in marketing, real world use cases, best practices, and key takeaways for marketers looking to leverage AI to better engage with customers at scale.
When it comes to marketing and advertising, AI is extremely pervasive. As we noted in the report, at least 80% of the digital media market is likely to be using AI in advertising this coming year. Below are four significant ways that AI drives improved business outcomes for brands and marketers:
Before machine learning, if you wanted to drive new customers to your website you would buy third-party data to determine which people would be most likely to try your product. Then you would run ads blindly and retarget anyone who visited your site. Machine learning enables marketers to target smarter by combining billions of data attributes into precise and accurate, adaptive models for the purpose of identifying optimal audiences.
Personalization and Segmentation
To personalize ads, we often incorporate rule-based personalization, which is based on an if-then scenario. An example would be: if it’s sunny, serve the ad, and if it’s raining, do not serve the ad. The decision to serve an ad is based on human assumption and this can result in doing things like serving the same ad to everyone in the same zip code. But not all users who live in the same zip code have the same needs, or need to see the same ad. If-then is a type of personalization, but it’s often not very personal.
When your goal is to reach new customers and drive them to perform your KPI, you need to employ segmentation to identify those new customers. Segmentation done with machine learning can take the available data, run it through a model, and determine the best approach for reaching net new customers.
Dynamic AI-Based Ad Creation
AI systems can be used to automate the process of creating ads. For instance, you can use machine learning to assemble ad creative on the fly. Every element in an ad makes a contribution to an outcome: from the background, headline and hero shot, to the offer and call to action, and can be used to create thousands of ad variations based on what we know about the target from our data. This can take into account variables such as the time of day, the weather, and a target’s past shopping behavior. The result is more interesting to the consumer because it is created specifically for them, and more effective for the marketer because it drives much better results.
As marketers see the benefit of machine learning in advertising and in other areas, they will start to think differently about how they are using data, and how it can drive better overall outcomes. That’s why 2020 will be the year we move from clicks and completes to metrics that move the needle, and why machine learning will define the next decade of smart marketing.
Real world examples, best practices, key takeaways, additional resources and more are all available in the Artificial Intelligence in Marketing Report. This is a complimentary download and is yours to share with colleagues.