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. A bit more about each in the paragraphs below.

  1. The Use of Augmented Analytics

Augmented analytics combines the data preparation, business intelligence, predictive analytics, and machine learning capabilities that exist today into a single, more automatic and streamlined process than we have seen to date. In the near future, augmented analytics capabilities will help organizations prepare and cleanse data, find key insights and hidden patterns, and report out on findings in an automated fashion. Having capabilities condensed into single software solutions will improve collaboration, time to market, and accuracy of insight. Today’s data volumes have created millions of variable combinations that are nearly impossible to process manually, thus automation via augmented analytics will be needed. The result will be quicker time-to-insight as well as better collaboration between the expert and citizen data scientist – which will affect the customer experience through continual improvement or processes, delivery, and offer content. Customer frustration levels will decrease and delight will be on the rise. Rita Sallam of Gartner has co-authored a great article on the topic here.

  1. Collaborative Machine Learning replaces Collaborative Filtering.

Many of the open source machine learning libraries, algorithms, and frameworks of today will join forces to become stronger and thus replace some of the simple, lower level personalization we see today, namely the approach of collaborative filtering. Collaborative filtering is the technique most commonly used in product and service recommender systems, such as Amazon and Netflix. These systems use basic data around purchase history, browsing behavior, and customer profile information to deliver an offer.  In the future we will see this information augmented with detailed insights from large internal and external customer data volumes – behavioral data such as device consumption (what you view) and interaction patterns (when you view) to provide an even higher degree of contextualization to an offer. Better music, food, movie, travel, product and purchase recommendations than ever before.

  1. Neural Networks and Decision Trees to the forefront.

Deep Learning relies heavily on neural networks and decision trees, and I believe these will become even more prevalent as use cases which require these techniques are incorporated into business operations. Neural networks will support better segmentation, classification and forecasting – especially when very complex customer journeys are involved. Decision trees will support more complex rule and relationship-based customer experience strategies and programs.  Understanding the importance of certain data variables, how to better interpret complex analytical models, customer product purchase sequences, and the overall price effect are just a few ways deep learning is being used today. Being able to use a deep learning techniques – such as decision trees and neural networks – to better move a customer through a complex lifecycle or journey in the future will benefit both brand and customer.

Machine Learning: The Future Impact

Sure, machine learning will increase automation in industries like manufacturing and medicine.  But from a customer experience perspective, we will see a continual embedding of machine learning into customer experience programs.  Business process automation of these programs will continue to increase – from both an operational and executional perspective – as has been discussed over the past several years. The use cases for where and how machine learning can be applied will continue to expand – into areas previously considered impossible.

Today, Earnix is already helping companies automate business processes using our software – to speed time to market, rapidly deploy analytical based changes to the market, and better fit analytical model insight to customer offers and interactions. This improves an organization’s ability to support complex decisions, forecasts, and optimizations.  As the organizational understanding of the customer continues to improve into the future, the delivery of offers and interactions will be further and further contextualized. The beneficiary – customer experience programs and the metrics that track their success – customer lifetime value, net promoter score, etc. Based on the progression I have seen over just the past few years, I can hardly wait to see how organizations will delight customers – with machine learning behind the scenes – in the very near future.

The Evolution Revolution

The digital consumer of today expects personalized, transparent, consumer-friendly interactions with brands. Brands are dealing with unparalleled new data sources and volumes, increasing regulations, and new entrants with unique business models disrupting their industries. As the market changes rapidly for banks and insurance companies, they must use analytically-based decision making processes to evolve. Earnix is helping organizations evolve to meet the market revolution of today. Using advanced analytics, machine learning, and other new data sources and technologies – Earnix is ready to provide the analytical pricing and product solutions designed for the financial services industry. Using Earnix, brands can deliver significant results by integrating data-driven decision-making into the business process – and win the evolution revolution.