Why are machine learning rational agents so important to retail applications?

Q:

Why are machine learning rational agents so important to retail applications?

A:

Rational agents serve various purposes in machine learning and artificial intelligence projects, but they are particularly useful in retail applications as important aspects of game theory and predictive modeling.

In retail, machine learning models are often used to try to predict optimal outcomes. Companies are trying to take big data about customers and assess it through the lens of human emotion and motivations – to look at human behavior on a collective basis. In other words, they're studying masses of customers, and making models of their collective behavior, trying to figure out how all of those individual choices combine to inform their business intelligence.


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With that in mind, rational agents play a useful role in game theory or other behavior modeling. Retailers will use rational agents and models to try to figure out how to best serve customers.

For instance, take a machine learning model that's evaluating drive-through service. In this case, the rational actors would be individual drivers. A machine learning model would take in big data – for example, it would examine real-time data about speed of service, how drivers navigate the drive-through area, how they choose to move their vehicles, and how that influences other decisions, down to a very detailed behavioral level.

This is just one example – rational agents in machine learning models can simulate human choices about seating, standing in line for products or services, shopping online, shopping in an open-air mall or series of stores, or just about anything else that business leaders want to measure.

Essentially, the use of machine learning models builds intelligence that companies can use to market and sell better. Rational agents play that particular role in the models in order to show decision-makers more about how their business decisions might play out in the real world.

A secondary use of rational agents in retail involves creating autonomous machines that can make their own decisions. It's likely that we'll see more of this kind of marketing as machine learning and artificial intelligence progress takes off. You might have a digital spider that crawls the web, or some other network or interaction with smartphone devices to market items individually to customers – think of the futuristic holograms in science fiction movies of the 1980s and 1990s that aggressively marketed products to individual people by name. That's the kind of thing that retail rational agents can do in today's evolving artificial intelligence environment.

In summary, there are specific ways in which retail stands to benefit a great deal from machine learning. Machine learning models involving rational agents and other elements can take much of the guesswork out of business decisions. Companies that aren't using these advanced models to drive business intelligence will be left behind as companies get smarter about serving their target audiences.

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Written by Justin Stoltzfus
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Justin Stoltzfus is a freelance writer for various Web and print publications. His work has appeared in online magazines including Preservation Online, a project of the National Historic Trust, and many other venues.
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