Six application points of data mining in the call center

The current business is transforming from "product-centric" to "user-centric". Many enterprises regard CRM as a key factor for enterprise success, as the most direct channel that affects users, call centers play a vital role. Using data mining technology, we can improve the efficiency of enterprise call centers while increasing customer satisfaction, the following describes the specific application aspects.

 

1. Establish a customer access classification model based on the customer's historical information, call information, customer level, and other information to segment connected customers and adopt different service response policies for different customer groups, this improves customer satisfaction.

2. Call exception behavior analysis model. The exception detection method or time series algorithm is used to identify customers with abnormal call behavior. Under reasonable assumptions, the customer should follow the Poisson distribution, in actual life, there may be a sudden increase in the number of calls that take a long time. Through abnormal call behavior analysis, we can find out these "abnormal" calls and the causes of the calls, and issue early warning information in a timely manner.

3. By analyzing the changes in incoming call volume and the number of incoming call types, you can reasonably arrange the number and shift of incoming call personnel based on the professional level of the agent service personnel and the prices of different service types, reduces operation costs without reducing the call center access rate.
4. Interactive Voice Response Service (IVR), through historical dialing and solutions, can continuously enrich IVR and reduce unnecessary manual access. This reduces labor costs.

5. Find high-satisfaction users, summarize the characteristics of high-satisfaction users, and establish a advertised learning model. This improves overall customer satisfaction.

6. Data tracking of the number of operators in each shift, number of calls and number of orders, response time, and service level standards based on the "hanging rate" (reflecting the customer's disappointment, analyze and identify potential factors for user hanging up, so as to avoid similar hanging up.

In conclusion, the application of data mining in call center can reduce costs, increase profits, improve efficiency, avoid risks, expand impacts, and remove obstacles.

Six application points of data mining in the call center