Whether it be facial recognition on your phone, that cool new VR experience, or your Alexa device, AI and its many applications have become integral to our society. When talking about AI, we typically refer to narrow AI, which is AI focused on one task — in contrast to general AI, which has the ability to apply intelligence to any problem. Alexa is a good example of narrow intelligence: It operates within a limited, predefined range of functions and, while being extremely sophisticated, it has no self-awareness.
When it comes to the advertising ecosystem, AI is ubiquitous. From chatbots, to forecasting, to hyper-targeted advertising, AI has numerous applications that help make marketers’ lives easier. In this blog post, I’m going to focus on two of the most attractive applications of AI for marketers: predictive and prescriptive analytics.
What Are Predictive and Prescriptive Analytics?
Let’s start by defining predictive analytics, for those who may not be familiar with the term. Predictive analytics is the use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data.
An application of predictive analytics for marketers, for example, is a measurement solution that’s able to go beyond real-time campaign monitoring to proactively anticipate outcomes, such as understanding campaign incrementality and predicting if and how a campaign is changing consumer behavior for each individual consumer. With predictive analytics, marketers gain access to valuable KPIs such as incremental visits, campaign effect on consumer behavior, cost per incremental visit, and customer acquisition cost.
Over time, predictive analytics can inform prescriptive analytics, which go a step further to recommend one or more possible courses of action based on a given outcome. An application of prescriptive analytics, for example, is a measurement solution that provides budget allocation recommendations based on past and current campaign performance.
How Predictive and Prescriptive Analytics Benefit the Advertising Ecosystem
The reason predictive and prescriptive analytics are so attractive to the advertising ecosystem is due to the massive potential they hold. Let’s take a real-life example: AI’s capability to automate and optimize media and consumer acquisition strategies. By leveraging data from first, second, and third parties, an AI-driven prescriptive-analytics marketing assistant can help solve challenges such as:
1. Choosing the Right Media Mix Allocation
By continuously issuing recommendations on how to refine the media mix, AI can help advertisers better determine the optimal media mix strategy. This in turn enables them to increase their digital advertising ROI, since it consistently evaluates the media mix and reallocates dollars accordingly. In this way, the marketing mix allocation can be completely automated — and save brands and agencies valuable time.
2. Optimizing Audience Strategy by Channel
Within each channel, advertisers can then use prescriptive analytics to identify audiences of consumers who will have a high lifetime value for their brand. This in turn enables them to optimize their audience strategy by channel. For example, a movie studio could move away from targeting just “moviegoers” to targeting the specific audience that wants to see a given movie.
How does this work? The AI can identify the right audience to optimize a certain function, taking into account not only short-term attribution — whether the consumer will convert right away — but also including long-term effects such as churn and consumer lifetime value. Over time, it can learn consumer patterns and identify those consumers who will be loyal to the brand, and have high lifetime value. Marketers can then optimize their audience strategy accordingly.
Stay tuned for the next installment in our series to learn about best practices for an AI-driven ecosystem. In the meantime, you can check out this blog post by our Chief Product Offer for more on campaign incrementality.
