As organizations seek to get closer to their customers, improved ways of leveraging analytics to intuit their needs and actions are becoming more important than ever. New artificial intelligence (AI)—machine learning—technologies have finally advanced enough toward predictive customer experience interactions by accurately mirroring human behavior through the technology of neural networks.
Built on a neural network of this kind, NICE announced its Nexidia Analytics solution, offering interaction analytics via omnichannel "listening" to know what customers and prospects are saying and then analyze those conversations to tailor experiences to their stated needs. The solution is based on the Nexidia platform, which uses deep learning to enable organizations to capitalize on the powerful insights from their omnichannel interactions.
The solution is the first of its kind since NICE acquired Nexidia a year ago.
"The overall market is really what's driving this change to multichannel analytics," says Larry Skowronek, vice president of product management at NICE. "Research shows that 70 percent of interactions today come from calls center audio, leaving 30 percent via other channels, like text or surveys."
Traditional analytics solutions are adept at analyzing written communications and garnering actionable insights developed from gleaned information. By analyzing actual voice interactions, Nexidia aims to give equal weight to the various channels of customer experience, not just for individual customers but across multiple conversations and scenarios. Analysis of words and phrases in both audio and text can also categorize tone as related to outcomes.
Nexidia fully integrates with NICE Engage, Quality Management, and Performance Management, an enterprise level, cross-channel recording platform that captures, records, forwards, and archives conversations in real time. The Nexidia solution aids cross-channel workflow via a single administration tool for phone calls, chats, email, surveys, and other interactions, which reduces the need for siloed analytics applications and databases.
Analysis of customers' preferred contact methods through root cause analysis can aid organizations in determining why one channel is preferred over another, which channels are more consistently engaged, and the success of self-service or chat promotion, according to Skowronek.
Other aspects of the solution include integrated reporting and query building, role-specific interface dashboards configured for both supervisors and agents, and advanced linguistic and statistical text mining for integration with leading Big Data analysis tools.
"Nexidia Analytics is capable of integrating into any number of CRM solutions to ingest data and potentially feed data back into the system," Skowronek says. "Considering how CRMs, especially Salesforce, have evolved and grown beyond a simple sales engagement tool, it's important to recognize the value of the data in the analysis."
Skowronek points out that another component of Nexidia Analytics is its ability to enable sales organizations to make predictive offers and market them appropriately, giving an example in the cable and Internet provider category:
"A potential customer calls in to order cable, and the provider asks what package they want: basic, premium, or ultra-premium. By analyzing thousands of interactions from individuals that have purchased services before, it's possible to create a predictive model that will not only increase the chance individuals will purchase a particular package, but will also identify the package and additional services that generate the greatest amount of revenue," he says. "Then as marketing creates new offers and packages, they can quickly test and analyze the offers against past data, creating much more effective models. In similar fashion, sales organizations can create predictive models for campaigns."