4 ways to use machine learning to improve customer experience
In a digital business environment, providing a quality customer experience — on multiple digital fronts — is not only a crucial aspect in modern business strategies, but it’s also becominga key responsibility of the CIO.
AI and machine learning tools have a significant role to play. According to Gartner, customer experience (CX) represents the majority of AI businessvalue through 2020. AI-driven customer experience projects are still nascent, however. AGartner survey found that 50%of customer experience professionals are using digital analytics or big data in their CRM/CX projects, but only 26% are using AI or machine learning.
How can CIOs best incorporate machine learning to improve customer experience initiatives? At the recentGartner Catalyst 2018event, Bill Delrieu, research director at Gartner, detailed how to use machine learning to improve customer experience by adding AI-rich tools to enterprise analytics platforms.
Bill Delrieu: In traditional analytics, you have a theory and you go through all your data and workloads to either validate that theory or completely disprove it. Then you come up with an insight and repeat that process over and over again. In the augmented or predictive analytics model, machine learning tools can provide insights without your having to repeat that process over and over again. These insights can be really useful in helping you understand what customers are doing and how to best serve up experiences to them. This concept of using machine learning for segmentation is going to be really useful because it allows people to see segments that are not readily visible to humans going through the data.
An example is the Google Analytics Intelligence feature that automatically generates insights. Google Analytics can go through all the different metrics and landing pages and see which performed better than others and give you practical recommendations right away. Adobe Analytics also has a feature called Anomaly Detection, in which a machine learning algorithm goes through your data, creates a trend line and gives you an idea of where you should expect those values to be over time. It automatically points out areas that are anomalies for those expected values and provides specific details about that anomaly. One interesting use case is from a company called Epson America Inc. It recently used this anomaly detection to pinpoint areas that had broken links or broken marketing landing pages and to update its site in real-time to provide a better user experience.
The second use case is segment discovery. This is where you want to use machine learning tools within your customer analytics application to provide insights on who your customers are. This concept of using machine learning for segmentation is going to be really useful because it allows people to see segments that are not readily visible to humans going through the data.
