Hear from CIOs, CTOs, and other C-level and senior execs on data and AI strategies at the Future of Work Summit this January 12, 2022. Learn more
Nice CXone, one of the more pleasantly named IT software providers around, is hooking up with Google Cloud, aiming to create more effective customer self-service systems that integrate with traditional contact centers.
Hoboken, New Jersey-based Nice announced last week that it is connecting its cloud-native, AI-powered CXone customer experience platform with Google Cloud Contact Center Artificial Intelligence (CCAI), a group of APIs that engage Google AI for contact-center use cases. The combination is designed to provide businesses with more efficient ways to engage and help customers navigate digital and voice touchpoints.
CX (customer experience) and UX (user experience) are all about reducing online friction so customers and potential customers don’t consider switching to another vendor. Nice CXone and Google AI endeavor to enhance CX and UX behind the scenes.
What sets Nice CXone apart
“We connect the dots from the end-to-end self-service experience to the more traditional agent-assisted service support and sales,” Chris Bauserman, Nice’s chief marketing officer, told VentureBeat. “When you think of bridging chatbots, voice bots, and other digital self-service experiences with the contact center, or a call center — either online or voice channels — what that does is provide all-in-one ease of management and visibility for organizations.
Bauserman said that CXone “allows us to connect the customer query to the customer experience. Most queries start with a Google search or a mobile app, and they may involve different attempts to self-serve. They may involve agents multiple times. And so by putting it all together in one orchestration layer, one set of options, and being able to move between them without the customer ever having to repeat themselves or start over, is really powerful for reducing friction and keeping CX loyalty.”
Faster answers to queries
Nice CXone claims to provide faster answers at the beginning of customer queries, thanks to its AI engine — sometimes even before a conversation with an agent starts, according to Bauserman. He also claims that customized next-best-action guidance capabilities and up-to-date FAQs allow agents to be better equipped to deliver improved service quality.
The Nice platform provides no-code/low-code integration and consolidated software orchestration with Google Cloud CCAI to enable intelligent natural-language capabilities through various stages of customer interactions.
CXone’s Virtual Agent Hub, included in CXone, enables businesses to use conversational bots for voice and chat, leveraging Google Cloud’s Contact Center AI. Businesses can integrate Google Cloud Dialogflow self-service bots without any coding while retaining control of the customer experience, Bauserman said.
Deployed in combination with CXone Agent Assist Hub, companies can use Google Cloud Agent Assist to empower their customer service reps with real-time, automated knowledge support during live chat interactions. Google Cloud reports that contact centers using Agent Assist have seen their agents respond up to 15% faster to chats, reducing chat abandonment rates and solving more customer problems.
The competitive landscape
Five9 and Genesys Cloud CX stand out as Nice CXone’s top competitors, based on similarity, popularity, and user reviews, according to Getapp.com. When comparing Nice CXone to its top 100 alternatives, Salesforce Sales Cloud has the highest rating, with Freshdesk as the runner-up, and Nice CXone ranking in 12th place.
How Nice implements AI
Andy Traba, Nice’s director of product marketing, offered VentureBeat readers some insight into how Nice CXone uses AI in its implementation:
VentureBeat: What AI and ML tools are you using specifically?
Andy Traba: Nice leverages an extensive AI, ML, and data analysis toolbox, but our go-to platforms and applications include TensorFlow, PyTorch, Python, and R.
VentureBeat: Are you using models and algorithms out of a box — for example, from DataRobot or other sources?
Traba: Yes, we draw from the above, but most of our models and algorithms are customized and purpose-built for our customer experience use-cases by our team of data scientists and ML engineers.
VentureBeat: What cloud service are you using mainly?
Traba: Amazon Web Services mostly, along with some Azure options.
VentureBeat: Are you using a lot of the AI workflow tools that come with that cloud?
Traba: We customize our workflows using the platform’s core capabilities, but we don’t rely much on the “with a few clicks”-type offerings.
VentureBeat: How much do you do yourselves?
Traba: We do 100% [of] everything ourselves.
VentureBeat: How are you labeling data for the ML and AI workflows?
Traba: Our key differentiator for labeling data is applying the robust speech and text analytics from the Nice acquisitions of Nexidia and Mattersight, where we also continue to innovate to ensure we have the best ML training datasets. Also, outcomes and metadata from CXone applications like Omnichannel Routing or Workforce Management enrich our datasets.
VentureBeat: Can you give us a ballpark estimate on how much data you are processing?
Traba: Nice analyzes billions of conversations across a wide range of channels for various industries that serve as the foundation for all our AI and ML work.
- up-to-date information on the subjects of interest to you
- our newsletters
- gated thought-leader content and discounted access to our prized events, such as Transform 2021: Learn More
- networking features, and more
Source: Read Full Article