Introduction
Consolidating internal customer data helps to combine data from multiple systems into a more comprehensive view of your customers. This consolidated view provides your organization with an excellent starting point toward understanding who your customers are. However, this information doesn't often provide a complete picture of your customers. Occasionally, it can be beneficial to use AI and machine learning to use historical trends to make predictions that your organization can act on.
As your organization conducts business with your customers, you collect increasingly more historical data. As a result, you have records that define who customers are, the types of purchases they make, the subscriptions they have, and more. Organizations can use this available data to start making predictions to help drive activities.
For example, customers who purchase memberships or subscriptions with a company aren't likely to renew if they don't see value in continuing with it. By using AI and machine learning on your organization's historical data, you can make predictions on how likely customers are to cancel by comparing their activity to the historical records that you have.
If your assessment identifies that the customer isn't likely to continue, you can initiate activities with the customer to try reducing the likelihood of cancellation, such as sending coupons or adding them to a nurturing program.
Microsoft Dynamics 365 Customer Insights - Data provides multiple ways for organizations to use AI to understand their data and make predictions based on what's happening. These methods could be as simple as predicting the value of a missing field, such as a record state, customer gender, or likelihood of a specific outcome. You could also use Customer Insights - Data to represent more advanced scenarios that use custom Microsoft Azure Machine Learning models from within Customer Insights - Data.
The rest of this module examines the different prediction options that are available.