45% load surge! Cloopen AI boosts first-time resolution for top bathroom group

For manufacturing brands, "the post-sale experience at critical moments" becomes the life-or-death line for user loyalty.

Imagine this: When a user anxiously reports that the heating function of the smart toilet seat is malfunctioning, the customer service representative is still frantically searching through the knowledge base, and the call frequently gets interrupted and restarted, causing the customer's dissatisfaction to soar... 

This was precisely the problem that the call center of a leading bathroom products group once faced:

• Slow response: The information about thousands of product models was fragmented, and the agents had to frequently search through the knowledge base. The situation was particularly severe during peak hours.
• Poor experience: The business processing procedures for issues like repairs were complex, and the error rate of the agents' operations was high, resulting in additional services.
• Low efficiency: Manual form filling took a long time and the information organization was cumbersome. 

Breaking Through: Developing a Dedicated Large Model Assistant for the Bathroom Industry
Facing the challenge of handling millions of SKUs in after-sales inquiries, this bathroom group collaborated with Cloopen Ai to develop a "dedicated large model assistant for the bathroom industry".

Through the use of large model technology, it provides intelligent support throughout the entire customer service call process: 

• Large model script mining: By analyzing over 100,000 historical call recordings, the gold experience of front-line agents is transformed into a structured knowledge base, generating an exclusive strategy library covering high-frequency scenarios such as smart toilet installation and faucet leakage after-sales.
• Large model document script: Analyze internal product manuals and other data to generate a "model - solution" knowledge graph.
• Large model gold strategy: Extract the "customer needs - agent response strategy - business goal achievement" golden relationship chain, customize a standardized process tree, and trigger real-time node script prompts during agent calls. 

• Dynamic script recommendation: Real-time identification of customer intentions, and the front-end agent interface immediately pushes top-notch scripts and the best strategies.
• Dynamic navigation guidance: Visual guidance to help agents follow standardized processes such as "repair registration - scheme confirmation - service appointment".
• Dynamic emotion monitoring: Real-time identification of customer emotions (such as anger, anxiety) during the call, triggering soothing strategy pop-up windows.
• Real-time quality inspection: Customized rules based on the characteristics of bathroom services, real-time identification of agent service norms and potential risks, and pop-up reminders. 

• Intelligent form filling: Automatically captures key information (model, fault, appointment time) during the call. The time for filling out the work order is reduced from 3 minutes to 20 seconds.
• Intelligent summary: Automatically generates a summary report of the call, saving 90% of the information entry time. It facilitates quick understanding of the situation and precise follow-up by cross-departmental collaboration personnel. 

Highlight: Accurately addresses the four major pain points in bathroom after-sales service

PART.01
Door-to-door installation reservation
Challenges: This bathroom products group has a wide range of products with various installation details. It is difficult to store a large amount of SKU information for the seats, which leads to errors and long installation times.

• The large model call center assistant deeply analyzes and learns from historical successful conversations and the enterprise product manual, extracting expert responses and standard operation procedures for different models.
• When users provide the product model, it recommends the installation key points, accessory list, and automatically fills in the reservation form in real time, effectively avoiding delays in on-site installation or the need for secondary services. The first solution rate increases by 50%, and customer satisfaction improves by 45%. 

PART.02
Product usage consultation and guidance
Challenges: The functions of bathroom products (such as smart toilets, thermostatic shower heads) are complex, and customers may have difficulty describing problems clearly. The seats can be easily confused; when new products are launched, the knowledge of the seats is not updated in time, and the answers are not accurate.

• The large model seat assistant, based on the latest documents of the enterprise, quickly builds a product knowledge system. Through semantic understanding and analysis of customers' ambiguous questions, it automatically pushes the operation guidelines and knowledge cards for the corresponding products, providing precise guidance to the seats in real time. 

PART.03
Product quality issue repair reporting
Challenges: The repair process for bathroom products is cumbersome and prone to errors, and customers' emotions are easily agitated. Improper handling can easily undermine customer trust.

• When a customer reports a problem, the large model call center assistant automatically understands the issue, immediately provides solutions (repair steps, part replacement suggestions, etc.) for the agent, and generates a standard work order.
• An internal customer emotion recognition model is built-in. When it detects that the customer's emotions are agitated, it provides real-time recommendation strategies for the agent, effectively reducing customer complaints by 30%. 

PART.04
Promotion Activity Consultation
Challenges: The promotion policies of this bathroom group are numerous and vary significantly across different regions. It is difficult for the agents to obtain all the information in real time, resulting in poor customer experience and loss of business opportunities.

• Through the large model agent assistant, import a large amount of promotion description documents in one click to build a knowledge base.
• When the customer mentions the promotion plan they want to know, the large model agent assistant can understand the customer's needs in real time, accurately recommend the matching promotion policies, applicable conditions, and operation procedures, helping the agents provide quick and accurate answers.