NICE today unveiled NICE Enlighten XM (Experience Memory), the latest advancement its Enlighten artificial intelligence solution that leverages large language models' deep data memory to hyper-personalize customer journeys.
NICE Enlighten XM introduces fully contextualized journey management to help companies understand, remember, and dynamically adapt to each customer's unique needs and preferences.
NICE Enlighten XM enables NICE CXone users to deliver context-rich interactions that resume where previous interactions concluded, given its advanced capability to hyper-personalize the conversation in real time for both self-service and employee-assisted interactions.
The AI-powered technology constructs an all-encompassing memory of each customer journey, including sentiment, behaviors, and interaction history across all touchpoints.
"Personalized experiences have been a holy grail for organizations, but most have struggled to deliver due to technological limitations," said Barry Cooper, president of the CX Division at NICE, in a statement. "NICE Enlighten XM shatters those barriers, empowering brands to deliver on the promise of true personalization finally. With NICE Enlighten XM, brands can build stronger relationships, drive unparalleled customer satisfaction, and lead the way in the future of customer experience. We're excited to make this vision a reality for our NICE CXone customers."
Key benefits and capabilities of NICE Enlighten XM include the following:
- 360-degree customer insight that combines interaction data, metadata, and analytics from the entire CX ecosystem to provide an understanding of customers' history and preferences.
- An individualized memory graph that creates a unique memory graph for each customer, ensuring every interaction is tailored to their specific needs with full context based on a multidimensional historical record.
- Continuous conversational sync that resumes interactions exactly where they left off, regardless of channel or device, .
- An adaptive personalization engine that dynamically determines the optimal next action, response, or activity in real time based on past performance, leveraging LLMs' deep data memory and companies' knowledge bases to create an evolving, customer-centric interface.
"Enterprise decision makers are overwhelmed with options to apply large language models and Generative AI to improve their customers' experience," said Dan Miller, lead analyst at Opus Research, in a statement. "Instead of approaches that employ an ocean of undifferentiated data, they need CX AI that is trained on company and customer-specific data that can drive personalized, effective outcomes. Investing in this approach now is foundational to any strategy that differentiates their brand in the conversational AI era and future-proofs their business operations for years to come."