Data red envelope clients mining and communication model based on social network analysis

Haiyong XU; Tao TAO; Yan HUANG; Cuiwei TANG; Zhaojing ZHANG; Jing WU

doi:10.11959/j.issn.1000-0801.2020128

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Data red envelope clients mining and communication model based on social network analysis

Operation Technology | 更新时间：2024-06-05

- Data red envelope clients mining and communication model based on social network analysis
- Telecommunications Science Vol. 36, Issue 8, Pages: 139-150(2020)
- 作者机构：
- 作者简介：
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- DOI：10.11959/j.issn.1000-0801.2020128
  CLC： TP301
- Published Online：2020-08，
  
  Published：20 August 2020
- 稿件说明：
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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：

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.

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Keywords

references

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