Japan : Fujitsu Technology Uses Conversational Speech to Identify Customer Satisfaction.
For this reason, based on word content alone, it was impossible to fully grasp the customer's sense of satisfaction. Now, using a method that takes into account not only the average pitch of the voice and the degree of variation, but also the characteristic changes at relative points within voice data that covers multiple words - namely at the start or end of speech - Fujitsu has succeeded in a highly accurate quantification of voice cheerfulness. In addition, using machine learning in conjunction with customer-service evaluations, Fujitsu has developed a technology that can automatically identify times in a conversation when a customer is satisfied or dissatisfied with roughly 70% accuracy, as compared with results of a determination made by the human ear.
In a field trial using this technology in Fujitsu Limited and Fujitsu FSAS Inc. call centers, Fujitsu confirmed increases in the efficiency of training, such as monitoring and evaluation of support personnel and feedback on the results, reducing the time required by about 30%. Moreover, there was a greater degree of acceptance by both the evaluator and the person being evaluated due to increased objectivity. Going forward, Fujitsu Laboratories intends to incorporate this technology into Human Centric AI Zinrai, the AI technology of Fujitsu Limited, and offer it as a product for use in customer-service evaluations and in the training of service personnel in a variety of enterprises that emphasize communication, such as banks and retail stores.
In communication, both remotely, as in call centers and distance learning services, as well as in such face-to-face situations as bank teller windows, the service provided to customers by service personnel is directly related to the company's image. This means that training for service personnel is seen as extremely important. Until now, information was sometimes gathered through customer surveys and used in training, but in many cases, the only data that could be obtained was the overall evaluation of the service. This made it difficult to determine what service personnel did wrong in the conversation. Also, in addition analyzing purchasing history for individual customers, surveys and other marketing activities, there is a need to discern, based on the customer's actual voice, such as can be heard in call centers, what the customer wants with regard to specific products or services, for example, what the customer's requirements are or what points need improvement. In so doing it is hoped that data from conversations with customers, gathered from service situations where personnel interact directly with customers, could be used in such areas as evaluating the quality of service, training employees, and marketing strategies.
Previously, there were efforts to grasp customer feelings by converting conversations with customers to text using voice recognition software, but not only do actual conversations not always follow ordinary grammar, but they can also be impacted by surrounding noise. As a result, there were many technological difficulties in converting conversations to text through voice recognition, such as many instances where the conversion was incorrect. In addition, even if people use the same words, the meaning may change in a variety of ways depending on their emotions, so it was difficult to correctly interpret the customer's emotions using methods that analyze speech that has been converted to text.
Fujitsu has now developed technology to automatically determine times in a conversation when customers feel satisfied or dissatisfied based on features of their voices in relation to the way they are speaking with service personnel. With this technology, customer service personnel and service providers can rapidly evaluate service content and improve support methods. Features of the newly developed technology are as follows.
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|Date:||Oct 19, 2016|
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