Solutions for Call Center

Emotibot allows AI customer service bots to accurately identify the content and underlying intent of customer inquiries and to give the appropriate response through its natural language understanding, knowledge engine, functional engine and other technologies, thereby fundamentally reducing the strenuousness of artificial customer service, improving service efficiency and reducing cost.In addition, Emotibot’s call center quality inspection system can use AI technology in complex calls, quickly identify the conversation topic and voice emotions, and generate quality of service warning, so as to solve the problem of small quality inspection coverage and high cost by traditional manual labor, and holistically improve service quality.

Need and Pain Points

  • Encountering personnel recruitment and training bottlenecks, difficult to provide adequate high-quality customer service staff for enterprises and users
  • The surge in labor cost, high cost of operation and management in customer service centers
  • Human quality control can be expensive, with small coverage, and difficult to measure customer service quality.
  • It’s difficult to avoid human customer service mood swings; it is difficult to provide users with stable, standardized customer service
  • It is difficult to identify the mood of the speaker or give an anthropomorphic answer. As a result, the smart bots look cold and less attractive.
  • Customer service data is difficult to use, can not transform customer service data into industry insight

Technologies / Modules

Bot Factory AI Open Platform
Customer Service Center AI quality inspection system
Intelligent service bot
User memory module
Multi-round dialogue system
User Satisfaction Public Opinion Analysis System
Multimodal emotion recognition model (face, text, voice)

Value to Business

  • Replacing human labor customer service to complete the basic business consulting, reduce the user's wait time, so that human customer service staffs can focus on high-valued customer service.
  • Give AI customer service emotions. Based on the customer's current mood and intent, adjust the customer service strategy, effectively resolve conflicts, and enhance customer satisfaction
  • Detect emotional information from the massive data, improve the quality of coverage, achieve full screening
  • Machine-assisted manual quality inspection, improve quality control efficiency, effective focus on the sample
  • Conducting big data analysis among the customer service data, extract industry insight from it, and enhance business development

Partnership Cases

Let the sleeping audio data "speak" Voice emotion quality reaching 90% accuracy, quality coverage increased by 5 times, equivalent to saving 400 million RMB / year for a telecommunications business

How did we do it?