基于社交网络分析的流量红包客户挖掘与传播模式

徐海勇; 陶涛; 黄岩; 唐崔巍; 张兆静; 吴晶

doi:10.11959/j.issn.1000-0801.2020128

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基于社交网络分析的流量红包客户挖掘与传播模式

运营技术广角 | 更新时间：2024-06-05

- 基于社交网络分析的流量红包客户挖掘与传播模式
- Data red envelope clients mining and communication model based on social network analysis
- 电信科学 2020年36卷第8期页码：139-150
- 作者机构：
- 作者简介：
  
  [ "徐海勇（1970- ），男，中移动信息技术有限公司总经理、高级工程师，主要研究方向为移动通信、互联网、大数据" ]
  [ "陶涛（1972- ），男，博士，中移动信息技术有限公司副总经理、高级工程师，主要研究方向为大数据、通信技术" ]
  [ "黄岩（1976- ），男，中移动信息技术有限公司电子渠道运营中心总经理、高级工程师，主要研究方向为大数据、能力开放" ]
  [ "唐崔巍（1992- ），男，中移动信息技术有限公司工程师，主要研究方向为社交网络分析、机器学习算法" ]
  [ "张兆静（1990- ），女，中移动信息技术有限公司工程师，主要研究方向为流量产品运营、数据分析" ]
  [ "吴晶（1979- ），女，博士，中移动信息技术有限公司高级工程师，主要研究方向为计算机应用技术、数据挖掘、用户行为分析" ]
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2020128
  中图分类号： TP301
- 网络出版日期：2020-08，
  
  纸质出版日期：2020-08-20
- 稿件说明：
移动端阅览
徐海勇, 陶涛, 黄岩, 等. 基于社交网络分析的流量红包客户挖掘与传播模式[J]. 电信科学, 2020,36(8):139-150.

Haiyong XU, Tao TAO, Yan HUANG, et al. Data red envelope clients mining and communication model based on social network analysis[J]. Telecommunications science, 2020, 36(8): 139-150.
徐海勇, 陶涛, 黄岩, 等. 基于社交网络分析的流量红包客户挖掘与传播模式[J]. 电信科学, 2020,36(8):139-150. DOI： 10.11959/j.issn.1000-0801.2020128.

Haiyong XU, Tao TAO, Yan HUANG, et al. Data red envelope clients mining and communication model based on social network analysis[J]. Telecommunications science, 2020, 36(8): 139-150. DOI： 10.11959/j.issn.1000-0801.2020128.

摘要

电信运营商持续创新流量经营模式与手段，将流量营销与社交化的红包活动相结合，创新推出流量红包活动，激发客户流量使用兴趣。面对社交化流量红包客户特征与传播模式的研究痛点及当前社群建模算法较单一的技术现状，详细研究、对比 6 种社群建模算法的应用效果，筛选出适合流量红包的最优算法，并定位分析核心价值客户群的特征。仿真结果显示，Multi-Level 算法在流量红包场景中表现更好，基于该算法挖掘种子客户、高价值客户、低价值客户和沉默客户 4 种特征客户群的社交网络结构。社交网络分群结论为运营商精准营销、精准推送等营销活动以及沉默客户促活、流失客户挽回等客户运营管理提供了有效指导。

Abstract

Telecom operators innovate continually in data management modes and combine data marketing with social red envelope activities

consequently launching data red envelope activities

which stimulates customers’ interest of data using.Faced with the research pain points of customer characteristics and propagation modes of socialized traffic red envelopes and the current status of the single community modeling algorithm

the detailed research and comparison of the application effects of the six community modeling algorithms were conducted to select the optimal traffic envelope algorithm

and the characteristics of the core value customer groups were located and analyzed.Results show that Multi-Level algorithm performs better in data red-envelope scenario.Based on this algorithm

four characteristic customer groups were mined

namely seed customer

high-value customer

low-value customer and silent customer.The social network clustering conclusion provided effective guidance for operators’ precise marketing

precise push and other marketing activities

as well as other customer operation management such as silent customer activation

lost customer recovery and so on.

关键词

Keywords

references

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