With artificial intelligence and related technologies, we can predict the future behavior of someone or something and determine how we should best respond to those insights.
This is very valuable in many industries, for example in marketing.
We can predict the probability of specific events; for example, if a blog post topic will perform well, the likelihood someone will buy a particular product, or whether a customer will defect or become loyal to the brand.
Or if a machine is likely to break down next month.
With artificial intelligence and related technologies, we can predict the future behavior of someone or something and determine how we should best respond to those insights.
Algorithms that predict the likelihood of something are called classification models in machine learning lingo.
We can also predict numeric values in any range, such as the predicted lifetime value of a particular customer, the best price for a product at a particular time, or the number of days before someone will make their next purchase. Algorithms that predict the amount of something are called regression models.
With clustering algorithms, we can predict group association. For example, which groups of customers spend most money, and what similarities do the customers in those groups have between each other?
This is called auto-segmentation and can be used to create lookalike audiences as well, meaning groups who share similarities in demographics, behavior, or other characteristics.
In fact, artificial intelligence—or more accurately, machine learning—is mostly about making predictions.
Artificial intelligence—or more accurately, machine learning—is mostly about making predictions.
We do this by analyzing information we already have to determine something we don’t know. In effect, we let the data speak to us.
This is why we sometimes call marketing with AI predictive marketing, or precision marketing. Making accurate predictions is great and can greatly improve your marketing efforts. However, we can take this one step further.
After predictions come prescriptions.
With prescriptive systems, we not only get insights about the likelihood of future events, we also get recommendations on how to best respond to those insights.
With prescriptive systems, we not only get insights about the likelihood of future events, we also get recommendations on how to best respond to those insights.
In other words, prescriptive systems tell us what to do next in a given situation.
A prescriptive algorithm can, for example, chose which email content to send to a particular customer, and at what time the message should to be sent to maximize the chance of it being opened and read.
We can use predictive marketing to determine the most likely outcome of something, and similar technologies to determine the best response to those insights.
