NICE has brought its artificial intelligence (AI) technologies to the heart of the contact centre, with the addition of Enlighten AI to the NICE Satmetrix Voice of the Customer.

According to the company, the AI is able to help contact centre agents identify at-risk customers and turn them into ‘loyal net promoters’  through the automated deployment of real-time, hyper-personalised surveys that are contextual and conversational.

According to NICE, consumer habits within digital environments are creating more demand for contact centres. Consumers will engage contact centre services for everything from research to purchases, as well as post-purchase enquiries and renewal.

The company also points to a recent NICE survey, which showed that 92% of contact centre decision makers have noticed increased interaction volumes. Further 84% of consumers are likely to switch brands after one poor customer experience.

Aberdeen vice president and principal analyst Omer Minkara says that organisations typically learn what causes customer churn only after they lose their clients.

“Modern contact centres don’t need to pay such a high price of frustrating or losing their clientele. Contact centres using AI capabilities achieve 2.3 times greater improvement in first contact resolution rates, 3.5 times greater annual increase in customer satisfaction rates, and an 11.5 times greater annual improvement (decrease) in service costs.”

NICE aims to Bring Enlighten AI to Satmetrix Voice of the Customer to help organisations provide impactful customer experiences that improve satisfaction and customer loyalty, and to reduce churn.

NICE workforce and customer experience group president Barry Cooper explains,  “As part of a holistic Voice of the Customer program, Enlighten AI Feedback identifies customer experience blind spots and enables organisational agility to close the loop. With that, it delivers what customers want and expect: informed conversations that are frictionless, hyper-personalised – and help resolve their problems.”

Enlighten AI with NICE Satmetrix Voice of the customer:

• Listens to and interprets 100per cent of interactions to transform subjective behaviours into objective insights without bias, using no human effort.
• Eliminates the need for human listening to understand agent performance and identify customer churn threats while providing more precise information.
• Based on the context of the interaction, deploys real-time hyper-personalised, contextual, conversational surveys that expand closed loop efforts to improve customer satisfaction and increase lifetime value.
• Uses pre-trained models that drive proactive, predictive, and prescriptive action to improve the operational efficiency of closed loop efforts at scale.
• Accelerates agile actions to resolve customer issues and prevent churn.

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Xilinx today introduced the latest evolution of its Vivado Design Suite software, Vivado ML Editions, to help developers in the electronic design automation (EDA) sector leverage machine learning algorithms and techniques to bring greater efficiency to the design process.

The EDA industry is dealing with trends toward lower-nanometer architecture with increasinging complexity and numbers of transistors. It has “decades of data and engineer know-how” on design processes, but processes can be slowed by the massive numbers of iterations required to get the designs right and the lack of Quality of Results that come through those iterations, according to Nick Ni, director of marketing, Software and AI Solutions at Xilinx.

Companies in EDA also don’t have much of their own expertise in machine learning (ML), a technology that could help these processes become faster and more efficient. That’s where Xilinx can step in, having built up a lot of its own ML expertise in recent years, in part through the 2018 acquisition of China’s DeePhi Tech, a company focused on ML and optimization of neural networks, Ni said.

Jim McGregor, principal analyst at Tirias Research, agreed, telling Fierce Electronics via email, “Xilinx is in a unique position in that it has a large history of information and works with most of its customers on designs. Combined with Xilinx’s expertise in ML, the company has a unique ability to implement ML for the benefit of customers using its technology.”

Vivado ML Editions, available now, leverages machine learning-based algorithms to accelerate design closure, Ni said, and software features such as logic optimization, delay estimation and intelligent design runs, which drastically reduces timing closure iterations. ML Editions also includes an Abstract Shell feature which decouples compilations from platform interfaces. Ni said that’s important because it helps speed up compilation, while also allowing EDA companies to keep their intellectual property under wraps as they expose some design aspects to customers and partners during the design process.

These capabilities combine to allow ML Editions to deliver 5x to 17x faster compile time, and average breakthrough quality of results (QoR) improvements of 10% on average and all the way up to 50% on complex designs, compared to the company’s existing Vivado software.

“Without something like intelligent design runs, you are doing a lot of manual runs,” Ni said. “With IDR it’s a push-button process,” which allows those iterations to be completed, for example, in a day instead of a week or more with manual processes.

National Instruments is one of the first users of ML Editions, and in a statement, Robert Atkinson, the company’s principal hardware engineer, described the IDR functionality as “a game changer. By offering a push-button method for aggressively improving timing results, it generates QoR suggestions that bring maximum impact and deliver expert quality results with a reduction in user analysis – especially for tough-to-close designs.”

Kevin Krewell, also principal analyst at Tirias, called ML Editions a “really good example of applied machine learning in design tools. Using machine learning, the tools can converge on an optimal solution many times faster than traditional heuristics and with a better result.”

McGregor added that while the EDA sector is just beginning to use ML technology, usage will continue to grow. “You will eventually see ML throughout the semiconductor design, manufacturing, and even lifecycle management process,” he said. “Even though it is piecemeal now, adding ML to both hardware and software tools will provide a significant benefit and provide a competitive advantage for both the technology provider and the customer.”

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