了解印度尼西亚公共话语中的服务质量问题,国家电力公司
Indra Ardhanayudha Aditya1, Fajar Nurrohman Haryadi1, Indri Haryani1
1Research of Power Plant and Primary Energy PT PLN (Persero) Research Institute Jakarta, Jakarta, Indonesia.
Heliyon
|August 9, 2023
概括
印度尼西亚国家电力公司 (PLN) 使用自然语言处理 (NLP) 和先进模型来分析社交媒体,识别客户关键问题以改善服务和数据驱动的决策.
科学领域:
- 业务流程转型 业务流程转型
- 数字经济 客户体验 客户体验
- 社交媒体分析
背景情况:
- 数字经济需要了解受社交媒体影响的复杂客户期望.
- 印度尼西亚国家电力公司 (PLN) 旨在通过现代化提高效率和服务质量.
- 从社交媒体分析大规模的公众意见至关重要,但具有挑战性.
研究的目的:
- 开发一种方法来分析社交媒体上的公共话语,以确定关键的客户问题.
- 根据客户反,提出数据驱动的解决方案,以改进PLN的服务.
- 加速数据驱动的决策,促进PLN内部的可持续性.
主要方法:
- 隐性迪里克莱特分配 (LDA) 是一种自然语言处理 (NLP) 技术,用于识别主导主题.
- 应用了技术接受和使用的统一理论2 (UTAUT2) 模型来分析关键问题.
- 使用修改后的e-ServQual和e-RecServQual方法来提出个性化解决方案.
主要成果:
- LDA有效地过了大规模的社交媒体数据,以揭示关键的公共话语主题.
- UTAUT2的分析指出了影响客户认知的关键问题.
- 该研究提出了量身定制的解决方案,以解决通过方法识别的特定客户问题.
结论:
- 综合的方法使得PLN能够有效地倾听和响应来自社交媒体的客户声音.
- 该方法支持数据驱动的决策,以提高服务质量和运营效率.
- 这些发现有助于通过以客户为中心的改进来实现可持续的业务实践.
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