Organizations that offer conversational interfaces, integrated omni-channel experiences, automated transactions and intelligent self-service will win both market share and mindshare in the future. Artificial Intelligence (AI) is the new competitive battleground for delivering superior Customer Experience (CX). AI seamlessly integrates into the customer service value chain with self-service tools, virtual assistants, content search and discovery, and sentiment analysis.
At Solvvy, we enjoy building cutting edge technology that has practical applications in an omni-channel world. We’re putting together a world class team of machine learning engineers and data scientists to deliver on our mission of providing intelligent self-service to customers. Designed for the modern enterprise, our platform makes use of advanced AI, Machine Learning (ML) and Natural Language Processing (NLP) technologies to unlock the power of enterprise knowledge.
We hope to transform how businesses interact with customers through AI but the implementation of AI comes with its own unique set of challenges. The first challenge is to build sophisticated machine learning algorithms that can parse noisy data from relevant data. Low signal-to-noise ratios indicative of a high level of false or irrelevant information in a conversation or an exchange often poses an engineering challenge. In a support context this translates to confusing ticket histories, inconsistent data across different support channels or incoming tickets with a bunch of redundant information or misleading language.
The other problem is having inconsistent data across your client portfolio. Different companies often use diverse configurations, alternate CRM platforms and distinct environments. In a support context, one of your customer could be using Salesforce while another might have its own in-house solution; one could be using email as the primary support channel while another might have heavy dependence on chat and so on. Incorporating these disparate form factors to build generalizable models that make accurate predictions also forms an integral part of building a scalable model.
Finally, when these factors combine with the lack of data, it further complicates the equation. Traditionally, machine learning algorithms are trained to work with huge data sets. They continuously learn and improve with every customer interaction. But when the data is sparse, it becomes tricky to train complex ML algorithms to deliver in a manner that they quickly update to provide a speedy resolution and maintain a smooth conversational flow.
The secret sauce to countering these many challenges lies in navigating both classical machine learning algorithms, and more modern deep learning architectures. When should we use classical approaches? When do deep neural networks make more sense? How can we benefit from both of these trends? How can we successfully apply machine learning to achieve these goals? We will answer these fundamental questions in our presentation at the AI Assistant Summit this week, and a video of the presentation will soon be available here. We will explain how these approaches can be used to build conversational agents which are capable of helping users to solve their problems.