Call centers are an important part of enterprise operations in many industries, and the importance of call center agents has grown significantly as a result of the Covid-19 pandemic. Customer call centers are now a primary point of contact between many businesses and their customers. Today’s agents are not just problem solvers and order takers but also contributors to sales.
Because of the number of calls these centers process, meaningful assessment of performance may be next to impossible without automation. Artificial intelligence (AI) is emerging as an innovative new tool in tracking the success of call center interactions. NetApp and SFL Scientific have combined their expertise to help enterprises address the implementation of a state-of-the-art deep learning model to detect sentiment in near-real-time during call center interactions, providing insight into the customer’s state of mind, employee performance, and more.
Sentiment analysis uses natural language processing (NLP) to determine whether the sentiment expressed during a customer call is positive, negative, or neutral. Using this approach, your call center can take advantage of vast amounts of previously untapped data. For instance, you could use sentiment analysis to correlate customer sentiment with regard to specific brands or products, track overall customer satisfaction, or monitor the sentiment of individual customers.
NetApp and SFL Scientific have developed an easy-to-implement AI pipeline that captures and displays the sentiment of call center conversations in real time. The joint solution can be quickly deployed on premises, trained, and tailored to your specific requirements to provide a better customer experience and to gain greater insight from every call center interaction. The general methodology implemented is applicable to a broad range of NLP and other AI challenges. For example, the combination of transfer learning, experimentation, iterative fine tuning, intelligent data management, and production deployment with regular retraining can be applied to a wide range of NLP and other AI use cases in your business.
Read this white paper titled “Using AI technology to optimize call center outcomes” to learn how NetApp and SFL Scientific can help you get your AI project to production more quickly with fewer missteps. NetApp and SFL Scientific have combined their expertise on other important AI use cases, like deep learning to identify COVID-19 lesions in lung CT scans, and monitoring face mask usage in healthcare settings. For information on NetApp AI solutions, visit www.netapp.com/ai.
Mike McNamara is a senior leader of product and solution marketing at NetApp with 25 years of data management and data storage marketing experience. Before joining NetApp over 10 years ago, Mike worked at Adaptec, EMC and HP. Mike was a key team leader driving the launch of the industry’s first cloud-connected AI/ML solution (NetApp), unified scale-out and hybrid cloud storage system and software (NetApp), iSCSI and SAS storage system and software (Adaptec), and Fibre Channel storage system (EMC CLARiiON). In addition to his past role as marketing chairperson for the Fibre Channel Industry Association, he is a member of the Ethernet Technology Summit Conference Advisory Board, a member of the Ethernet Alliance, a regular contributor to industry journals, and a frequent speaker at events. Mike also published a book through FriesenPress titled "Scale-Out Storage - The Next Frontier in Enterprise Data Management", and was listed as a top 50 B2B product marketer to watch by Kapos.
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