Machine learning (ML) is a type of Artificial intelligence (AI) that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. In other words, machine learning receives input data and uses statistical analysis to predict an output value.
That analysis is a collection of techniques that are used to discover hidden patterns in data to explain, group, find outliers, classify or predict observations. In the past decade, machine learning has given us self-driving cars, basic speech recognition, effective web search, and computational biology. With the rise in big data, machine learning has become a key technique for solving problems in areas such as finance, image processing, energy production and natural language processing.
Machine learning is improving the work of IVR (Interactive Voice Response) application developers. Customers can speak in their own words and the IVR will be able to understand the customer need. The customer experience is enhanced by routing to the correct agent or menu option. The quicker the need is resolved, the better the perceived customer service. Also, machine learning improves customer analytics; it will allow you to identify and contact prospects before they contact your agent, or predict a high-volume of calls and provide an action plan to solve the issue customers are calling about. Remembering and learning from past experiences is possible with machine learning, these features allow to improve customer personalization and identify fraud.
You don’t have to be a software expert to use machine learning, thanks to many cloud provides such as Amazon Web Services, Google Cloud Services or IBM’s Watson. If you want to explore deeper and you have Python programming experience machine learning frameworks (scikit-learn, Tensorflow by Google, and CNTK by Microsoft Research) are a viable alternative. The objective of those frameworks is to get application developers interested in using machine learning without having to understand the deep science behind the algorithms. There is never a perfect solution, and as the demand for machine learning solutions increase, more low-level algorithms will be needed. Even more high-level platforms are in demand to lower barriers to entry into the artificial intelligence space. A platform that provides a complex machine learning environment is Matlab, a computing environment that provides prebuilt functions, toolboxes, and specialized applications for machine learning. Matlab is an environment engineers and scientists use to convert algorithms to C/C++.
Machine learning resources became ubiquitous and provide multiple ways to interface either using a high- or low-level programming languages. Always consider machine learning when hand-written rules or equations are too complex, the rules of a task are constantly changing, or the nature of the data keeps changing and the program needs to adapt.
Machine learning has been actively deployed in multiple applications, it has been only a few years ago since it started showing up in customer experience applications. In the future will help us to create targeted and personalized customer experiences. Let us know what you think by tweeting us at @Dialogic.