Machine Learning

Continuously Improve with Each Customer Interaction

Each customer journey is unique. So being able to quickly extract and apply insights about your customers’ experiences in real time helps ensure that every future interaction—even the very next click—is fulfilling, fruitful and profitable, both for your customers and your bottom line.

Machine Learning from NICE is a patent-pending self-learning, automated modeling technique that employs Temporal Difference Learning (a type of Reinforcement Learning technology) to improve predictive analytics and decisions at the moment that interactions occur. It leverages our Big Data infrastructure to develop and continuously refine predictive customer profiles from volumes of raw multi-channel customer interactions. It learns rapidly from the digital behavior of customers in parallel, rather than aggregating outcomes after interactions end, enabling your organization to not only make better predictions, but impact decisions now.

Technology highlights:

• Can be deployed across multiple channels at enterprise scale
• Designed to be configurable by the marketer or analyst, making it open, flexible
• Requires less data to render data models productive and less expense to implement
• Works online, in parallel and in real time
• Multivariate testing and reporting illustrates performance compared to other approaches using only predictive analytics or business rules
• Differentiated by patent-pending real-time adaptive binning processes, learning from incomplete customer histories and distributed scalable incrementally updated machine learning models

Benefits:

• Organizes raw multi-channel events by customer and uses them to compute predictive customer profiles
• Learns the value of taking actions that may carry a short-term cost, like offering a customer a discount, but yield long-term benefits, such as increased wallet-share over time
• Enables faster response to changes in customer behavior
• Efficiently optimizes complex goals, such as improving lifetime value
• Prevents the need to constantly build new predictive models to accommodate for model drift
• Begins the learning process from the first offer; the system does not wait for the end of a campaign or program