The main goal of this study is to investigate whether social media, as a recent communication channel, has an impact on customer lifetime value (CLV). No studies have been done in Turkey with similar purposes in the telecommunication sector. To reach this goal, there has been an attempt to develop both artificial neural network models and sector-specific applicable models. Four years of data between 2011 and 2014 belonging to customers in the telecommunication sector who have a Twitter account are used in this study. The CLV is modeled through radial basis function (RBF), multilayer perceptron (MLP), and Elman neural network approaches, and the performance of such models is compared. According to the findings, calculated CLV error values are at an acceptable range in all formed models. Additionally, it is determined that the CLV was calculated with a lower error value in models where social media variables were used. The Elman neural network is determined to perform better compared to RBF and MLP.

中文翻译：

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社交媒体互动对电信行业 CLV 的影响

本研究的主要目标是调查社交媒体作为最近的沟通渠道是否对客户生命周期价值 (CLV) 产生影响。土耳其尚未在电信部门进行过类似目的的研究。为了实现这一目标，已经尝试开发人工神经网络模型和特定行业的适用模型。本研究使用了 2011 年至 2014 年间属于拥有 Twitter 帐户的电信行业客户的四年数据。CLV 通过径向基函数 (RBF)、多层感知器 (MLP) 和 Elman 神经网络方法进行建模，并比较了这些模型的性能。根据调查结果，计算出的 CLV 误差值在所有形成的模型中都在可接受的范围内。此外，可以确定，在使用社交媒体变量的模型中，CLV 的计算误差值较低。与 RBF 和 MLP 相比，Elman 神经网络被确定为表现更好。