The notion of using analytics to improve customer experience has changed the landscape and thought process of businesses over the past several years. As machine learning becomes further democratized, or more pervasively available, it is making its way into many enterprise software applications – including the Earnix software suite. Machine learning is impacting everyday analytical activities for our customers (segmentation, modeling, and optimization), and is improving very specific marketing program results. Often the ultimate goal of these marketing programs are to improve the customer experience – which requires a faster, more accurate, and more contextual customer interaction – which is better for both consumer and brand. I'll give a few examples of how machine learning is improving the customer experience through next best offer personalization, customer behavior analytics with new data sources, and analytical optimization.

In my first blog post on the topic – Customer Experience and Machine Learning: Practical Applications – I discussed how machine learning techniques are being used today by financial services organizations to achieve business benefit. Insurers and retail banks are using machine learning to improve personalization by being able to better analyze and predict customer behavior, and deliver the optimal marketing offer, message, or price. But what is coming in the future? Based on the research we are doing – we are seeing a few capabilities come to forefront. These include augmented analytics, collaborative machine learning, and the introduction of decision trees and neural networks within deep learning.