I recently finished a great course on artificial intelligence offered by the UC Berkeley Haas School of Management. It was an eight week course that was mostly directed at the business of AI, rather than coding or technology. Really good stuff.
The first class focused on how AI is used in analytics. There are three types of analytics:
- Descriptive
- Predictive
- Prescriptive
Descriptive analytics describe something that happened in the past. For AI to be really useful, it is best to collect a lot of data and then sort through it. The most popular way to sort through the data uses clustering. When you cluster, you essentially group similar things. This allows you to better see patterns as well as outliers. Descriptive analytics are very common – for example, a company report. However, they are moderately useful on their own.
Descriptive analytics answers the question, “What did happen?”
Predictive analytics describe something that will happen in the future. Predictive analytics take descriptive analytics and carry them a step further. Data is mined and sorted, and predictions are made. One common use of predictive analytics is predicting buying behavior. If someone purchased a washing machine, you can predict with pretty high confidence that they will buy detergent.
Predictive analytics answers the question, “What could happen?”
Prescriptive analytics describe something that should happen in the future. Given a set of data, what decisions should be made in the future?
Prescriptive analytics answers the question, “What should happen?”
One of the most famous examples of prescriptive analytics is the UPS ORION program. ORION standards for On-Road Integrated Optimization and Navigation. It is an advanced routing system that takes many factors into account and predicts the best route for a UPS driver to take.
ORION evaluates all kinds of data to make this prediction, including the number of stops, the start time, the committed delivery time, pickup windows, and customer requests. ORION is famous for ensuring that UPS drivers never make left turns. They always turn right.
AI analytics have been fueled by the enormous growth of big data, machine learning, and advanced algorithms. Through AI, we are increasingly adept at analyzing the past to predict and prescribe the future.
We are seeing tremendous interest in AI analytics among enterprises that need to scale the creation and delivery of vast amounts of content. Pharma companies are exploring ways to harness AI analytics to enable greater automation in how they create data-rich content and submit that content to health authorities. Medical content providers are using AI analytics to improve delivery of content to health care providers and patients at the point of care.
Content marketers across all industries are already using AI analytics. According to the fifth edition of the Salesforce “State of Marketing” report, marketers are using all three types of AI analytics:
- Descriptive analytics to improve customer segmentation
- Predictive analytics to personalize the customer journey
- Prescriptive analytics to provide real-time, next-best offers and to automate social interactions
You might not know when you’ll encounter AI analytics next. But you can bet that the AI does.
Val is on the Advisory Board for the Technical Communications Program at the University of North Texas. When not working with customers or students, Val can be found sitting behind her sewing machine working on her latest quilt. She also makes a mean hummus.
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