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Gompertz function based different network user number predicting method

A prediction method, a technology for the number of users, applied in the fields of electrical components, wireless communication, etc.

Inactive Publication Date: 2012-12-26
BEIJING UNIV OF POSTS & TELECOMM
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0013] (2) When 0t)′>0, (lgyt)″t is an increasing function of t, but (lgyt)′is a decreasing function of t, and the growth curve yt is convex, that is, the index value yt maintains an increasing trend with the increase of t, but the growth rate is declining
In the initial stage of the new mobile service launch, the number of general users is small, and then the number of users continues to increase; when the market reaches saturation, the growth of the number of users slows down significantly; finally, with the intensification of market competition, users may find other more satisfactory The product is off-grid, resulting in a decrease in the number of users, that is, the original retained users will be affected and there is a risk of off-grid

Method used

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  • Gompertz function based different network user number predicting method
  • Gompertz function based different network user number predicting method
  • Gompertz function based different network user number predicting method

Examples

Experimental program
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Embodiment 1

[0033]This embodiment provides a method for predicting the number of users of different networks based on the Gompertz function. This method uses data mining technology to organically combine the customer life cycle and the Gompertz function, and through in-depth analysis of the inter-network call data of the mobile service users of the local network and the different network And mining, it can identify the life cycle of different network users, and dynamically and accurately predict the number of different network users.

[0034] Let the Gompertz function be Where k is the limit value of the index value, a is the growth rate of the index value, and b is the time point when the index reaches the maximum growth rate. According to the different values ​​of parameters a and b, there are four different types of Gompertz curves. According to the function segmentation characteristics, four stages of the life cycle of mobile business customers can be fitted. By observing the change...

Embodiment 2

[0086] This embodiment has described the computer program flow that realizes the method for predicting the number of different network users based on the Gompertz function. In this example, M=200, N=30, and the specific steps are as follows:

[0087] Step 201: Obtain the following data:

[0088] 1) The number of mobile service users of the local network who have made calls with mobile service users of other networks in a province in the last M consecutive months;

[0089] 2) The number of mobile service users of different networks who have made calls with mobile service users of the local network in a certain province for M consecutive months.

[0090] Step 202: Calculate the lg(lgy t -lgy t-1 );

[0091] If the results are roughly equal, that is, close to a constant, the Gompertz function is applied; otherwise, the Gompertz function is not applied and the program exits.

[0092] Step 203: After selecting the N groups of samples, divide them into three parts on average, wh...

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Abstract

A Gompertz function based different network user number predicting method belongs to the field of data mining. The invention aims to not only identify the life cycle of the different network mobile service users but also dynamically predict the different network mobile service user number. The method specifically comprises the following steps: obtaining the data of internetwork conversation between the home network and different network mobile service users; then using the Gompertz function to fit the life cycle curve of the different network mobile service users and identifying the specific stages of the life cycle of the different network mobile service users according to the fitting result; and finally dynamically predicting the different network mobile service user number. By the method, the life cycle of the customers and the Gompertz function are organically fused by utilizing the data mining technology, and not only the life cycle of the different network mobile service users can be identified, but also the different network user number can be dynamically and accurately predicted by deeply analyzing and mining the data of internetwork conversation between the home network and different network mobile service users.

Description

technical field [0001] The invention relates to the field of data mining, in particular to a method for predicting the number of different network users based on Gompertz function. Background technique [0002] With the reorganization of China's telecom industry and the advent of the 3G era, the competition among major domestic telecom operators is becoming increasingly fierce. In order to remain invincible, it is necessary to "know yourself and the enemy", obtain and predict competitors' intelligence (such as market share and number of users, etc.) One of the pressing issues facing business decision makers. Therefore, the predictive analysis of competitors has become one of the indispensable basis for making their business decisions. [0003] The customer life cycle, also known as the customer relationship life cycle, refers to the development track of the level of customer relationship over time, that is, from the beginning of a customer's understanding of the company or...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04W24/00
Inventor 郑岩
Owner BEIJING UNIV OF POSTS & TELECOMM