Predictive form calling-out algorithm based on dynamic statistics process, switch dialing method and device

Abstract

The invention relates to a predictive form calling-out algorithm based on a dynamic statistics process, a switch dialing method and device. The device includes a data input module, a predictive form calling-out algorithm module, a switch, a dynamic statistics module, a data storage module, and a data output display module. The data input module inputs initialization parameter data to the predictive form calling-out algorithm module. The predictive form calling-out algorithm module is connected to the switch for information interaction. The dynamic statistics module is connected to the data storage module and receives the data of the data storage module. The dynamic statistics module counts the statistics data with different frequencies and transmits the data to the predictive form calling-out algorithm module. At the same time the data display is performed through a data output interface. According the invention, statistics and prediction are carried out according to dialed historical data, the dialing number next moment is dynamically determined, the service utilization rate is improved when a target phone call loss rate is not exceeded as far as possible, and optimizing balance is reached and the artificial dialing is completely replaced.

Description

technical field [0001] The invention relates to the technical field of telephone outgoing calls, in particular to a predictive outgoing call algorithm based on a dynamic statistical process, an exchange dialing method and a device. Statistics, data mining, queuing theory, Markov chain, birth and death theory, and algorithm design and analysis technologies are used, especially the technologies of real-time resource allocation scheduling and feedback control optimization. Background technique [0002] Call centers can be divided into two types: inbound and outbound. The inbound call center provides passive services, while the outbound call center provides a way to actively serve customers, and provides an efficient way for enterprises to carry out marketing, questionnaires and other businesses. With the development of computer and communication technology, the call center system based on traditional technology has been difficult to meet the changing needs of the market, so au...

AI Technical Summary

Problems solved by technology

The prediction algorithm is summarized as two related problems to be solved: 1. In the case of maximizing the agent busy factor (agent utilization rate), the call loss rate should not be greater than an acceptable maximum value ARmax

In addition, the service time obeys the general distribution. It is found in the research that the service time will be concentrated in two stages, one is 3-10s, and the other is between 140-160s. It is not a standard general distribution, which will cause the model The calculation results have a large deviation

In addition, the algorithm does not take the current state of the agent into consideration, which will cause calls to be made according to the number of original data calculations when all the agents are suddenly busy, until the average time T is recalculated ( takes a long time), so there will be a long period of turmoil

In the case of a sudden decrease in service time for a period of time, it will also cause fluctuations in the sudden decrease in seat utilization

[0007] From the above analysis, it can be seen that most of the existing predictive outbound algorithms are coupled with the dynamic statistical part, or even have no dynamic statistical part

The most important determinant of the predictive outbound algorithm is the dynamic statistics part. Some of the existing outbound algorithms are calculated using theoretical formulas, requiring a mathematical model to be established in advance, and the subsequent parameter values ​​​​are all calculated through theoretical formulas. ;Although this method does not use or seldom access the database, the establishment of the model is relatively fixed. For some businesses, this model no longer holds true, or the adaptability of this model is poor

Some of the existing outbound algorithms are also implemented through dynamic statistics, but this method requires a large amount of access to historical data for machine learning, training data or neural networks, etc. This method frequently accesses the database and requires a large number of data, wastes a lot of time, causes performance degradation, and is difficult to make reasonable predictions without a large amount of data initially

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