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客户流失这个术语通常用来描述在特定时间或合同期内停止与公司进行业务往来的客户倾向性[1]。传统上#xff0c;关于客户流失的研究始于客户关系管理#xff08;CRM#xff09;[2]。在运营服务时#xff0c;防止客户流失至关重要。过去#xff0c;客户获取相对于流失…
引言
客户流失这个术语通常用来描述在特定时间或合同期内停止与公司进行业务往来的客户倾向性[1]。传统上关于客户流失的研究始于客户关系管理CRM[2]。在运营服务时防止客户流失至关重要。过去客户获取相对于流失数量的效率是良好的。然而随着市场因服务全球化和激烈竞争而饱和客户获取成本迅速上升[3][4]。
Reinartz, Werner, Jacquelyn S. Thomas 和 Viswanathan Kumar2005表明从长期业务运营来看在CRM方面努力提高所有客户的留存率不如在少数目标客户获取活动上投入更有效[22]。同样Sasser, W. Earl1990建议保留的客户通常比随机获取的新客户带来更高的利润率[23]。此外Mozer, Michael C., 等人2000提出从净投资回报率来看保留现有客户的营销活动比吸引新客户更有效[16]。Reichheld 等人1996表明客户留存率提高5%分别使一家软件公司和一家广告公司的客户净现值增加了35%和95%[29]。因此流失预测可以作为一种方法来提高忠实客户的留存率从而最终增加公司的价值。
在各种服务领域已经提出了关于客户流失的研究。这些流失分析研究试图使用各种指标提前识别或预测客户流失的可能性。客户流失率[5]是典型的客户流失分析指标。这指的是在特定时期内取消服务的订阅者与总订阅者的比例[5][32][41]。流失率是计算大多数服务领域订阅者服务留存期的最广泛使用的指标。由于其重要性和直观性流失已被引入各种服务领域并发展以适应每个领域的特性。因此客户流失分析的研究根据各个研究领域而分散测量标准也各不相同。目前这导致了许多问题。在行业中由于在融合异构服务例如车辆共享服务/保险在线音乐服务/百货商店过程中服务人员之间不同流失标准引起的沟通成本急剧增加。此外由于流失研究同时涉及工程和工商管理两个领域研究人员很难在一篇论文中描述两个独立的专业领域或理解它们。
过去早期的客户流失被用于定义CRM中的客户状态。CRM是一种商业管理方法最初出现是为了提高零售、营销、销售、客户服务和供应链领域的效率以及提高组织的效率和客户价值功能[2]。从那时起从架构角度来看CRM已经演变并分为操作性CRM和分析性CRM。分析性CRM专注于开发包含客户特征和态度的数据库和资源[24][25][30]。分析性CRM最初用于利用客户状态和客户行为数据创建适当的营销策略特别是用于满足客户的个人和独特需求[26]。从这一点开始IT和知识管理相关技术被利用公司开始应用专门的技术来获取、保留、流失和选择客户[27]自从IT领域的技术被实施到CRM中以来各种公司开始在包括数据仓库、网站、电信和银行在内的业务领域使用这些技术[28]。如前所述CRM研究表明提高少数现有客户的留存率比获取新客户更有效流失分析已成为重要的个性化客户管理技术之一[20][28][59]。有一些综述论文收集并总结了电信领域的流失分析技术[6][7][8][9]。然而这些研究仅限于电信领域流失分析使用的日志数据不包括时间序列特征、留存和生存以及关键绩效指标KPI特征。还有一些论文应用了各种基于深度学习模型的流失分析技术在计算机科学领域[10][11]。然而这些研究仅限于深度学习算法缺乏基础模型和参数描述。也有一些关于流失的综述论文但它们不涵盖最新的深度学习技术只涵盖特定行业领域的流失[12][13]。建立流失预测模型的趋势正在改变性能正在迅速提高。然而由于先前研究的分散研究人员在启动新的流失研究时面临许多困难。为了解决这些问题这篇综述论文描述了在工商管理、营销、IT、电信、报纸出版、保险和心理学领域中流失预测算法定义的差异并比较了流失损失和特征工程的差异。此外我分类并解释了基于此的流失预测模型案例。我们的研究提供了比以往综述论文更广泛的流失详细技术的分类信息。我们的研究可以减少在多个行业/学术领域中被分散和使用的流失标准的混淆并在将其应用于预测模型时提供实际帮助。特别是这篇论文介绍了一种深度学习模型这种模型是为了应对随着工业发展而出现的非合同客户流失问题而设计的。本文结构如下。第2章介绍了每个业务领域流失的典型定义及其差异。第3章展示了各种业务领域中的流失应用案例。
流失的定义
流失在多个行业中有不同的定义。在本章中我描述了两种典型类型。如表1所示总结了定义流失的不同标准的典型论文。一般来说字典中流失的定义是长期的不活跃[14]。然而“不活跃”和“长期”的标准在各个研究领域中是不同的。由于现代服务采用了更宽松的订阅条款这种不一致性经常出现因为竞争的原因。在过去客户流失通过合同取消明确发生而在包括互联网和零售服务在内的现代服务中由于客户投资成本低频繁发生客户流失[19][20][21][96]。这些非合同客户流失是由于更换服务的低转换成本引起的[31]。因此我将流失的标准分为合同流失和非合同流失。每种流失的描述如下。 第一个标准是合同流失。合同流失指的是客户在合同续约日期到达时不续约的情况[15][16]。这种流失意味着客户对相关服务领域失去兴趣并且改变了其状态使其不再可能重新进入。通常出现在客户关闭银行账户或从一个运营商转移到另一个服务时的流失问题中。此外合同流失在如音乐和电影流媒体服务等固定费率服务中经常出现。
第二个标准是非合同流失。一般来说在非合同情况下客户可以在没有时间限制的情况下离开服务/合同。从服务运营的角度来看首先构建一个流失标准然后将符合该标准的客户分类为流失客户。为此计算客户行为改变的日期[96]。当这种不活跃或行为改变的时间超过阈值时客户被视为流失客户。在此过程中设定为不活跃日期阈值的时间称为时间窗口[17]。定义非合同流失使得可以推断出在特定时间段内可能流失的客户的概率。时间窗口方法在分析非合同情况的活动日志时经常使用。当客户在一段时间内不使用服务时这种方法将客户视为流失。互联网服务通常不会删除账户。因此互联网服务将登录解释为长期的即服务的保留并将一段时间内未连接的访问解释为流失[17]。图1示意性地展示了使用时间窗口方法的非合同流失案例。图1的日志记录了10周。时间窗口设定为第4周到第7周的4周时间。从第4周到第7周没有任何活动日志的用户A、B和C被视为流失而其他有活动日志的用户D、E和F被视为保留。 流失分析通常是为了改善业务成果进行的。因此在大多数流失预测问题中流失期被定义为可以恢复客户信任的时间段。如果选择客户完全流失的时间段作为时间窗口流失定义的时间段会呈指数增加并且在业务方面不会带来任何收益因为改变想要流失的客户的意愿被认为是不可能的[17]。上述的合同流失接近于客户完全从服务中流失。因此如今大多数基于日志的流失预测问题使用概率方法来确定客户是否流失并给予客户重新使用其服务的激励。
时间窗口的设置标准因服务特性而异。Yang, Wanshan等人2019分析了日志数据以定义移动游戏的流失期分析结果显示超过95%的客户在连续缺席3天后没有返回。他们将3天设定为时间窗口流失期[43]。Lee, Eunjo等人2018考虑到PC游戏服务的特性将75%的客户持续未连接的时间定义为流失期[17]。他们收集了客户未连接的时间段并绘制了累积数据图。他们选择了超过75%客户流失的时间段作为时间窗口。图2示意性地展示了Lee, Eunjo等人2018收集的连续未连接天数的累积数据。根据图2客户未连接超过75%的时间段是14周。因此时间窗口流失期为14周。 如上所述有两种客户流失类型分别是合同流失和非合同流失。此外还有三种流失观察标准月度、日常和二元。月度和日常流失观察与客户状态在数据库中更新的周期有关。二元流失观察是通过操作这个数据库获得的。一般来说在合同设置中二元流失由合同的存在与否决定。在非合同设置中公司定义客户的不活跃特征当客户符合不活跃或不忠诚客户特征时客户被视为二元流失[96]。定义客户流失的多种方式是为了定期监控客户状态的变化。通过这种观察可以通过预测客户流失率并为可能流失的客户提供激励来增加预期的净业务价值[16][23]。
各个业务领域的流失分析
早期的大多数流失研究都是从管理角度进行的特别是客户关系管理CRM[30][31]。CRM流失涵盖了客户识别、客户吸引、客户保留和客户开发过程中可能发生的所有流失问题。现代流失预测问题主要使用日志数据进行分析。日志是使用互联网服务时留下的跟踪数据。因此使用日志数据实现的流失预测模型可以用于各个行业的互联网服务。有12个业务领域进行了流失预测。
电信行业占据了之前大多数流失研究。尽管客户获取成本高电信服务的客户粘性也很高。因此如果防止客户流失并提供适当的激励有助于维持销售[16][32][33][34]。
金融和保险行业也预测客户流失。张荣等人2017强调了建立流失预测模型和防止流失的必要性提到保险行业的高客户获取成本和高客户价值[11]。蒋丁安等人2003提到在线金融市场的客户价值高并使用Apriori算法根据金融产品选择和客户的金融产品选择顺序创建了流失情景[35]。拉里维埃尔·巴特和德克·范登波尔2004基于客户群体根据金融产品属性的不同通过测量每种产品的生存时间证明了选择金融产品的客户倾向不同流失的可能性也不同[36]。佐普尼迪斯·康斯坦丁、玛丽亚·马夫里和乔治·伊奥安努2008测量了金融产品的转换率和每种产品的客户生存期以发现有吸引力的产品[37]。在这里由于生存期短流失更频繁这被用作衡量需要补充金融产品的指标。格拉迪·尼古拉斯、巴特·巴森斯和克里斯托夫·克鲁克斯2009测量了客户生命周期价值和预期收益随时间的减少作为对应客户忠诚度的指标[38]。在此过程中使用机器学习计算流失率用于估计客户生命周期价值。
后来流失研究在游戏领域如同在电信领域一样积极进行。这些服务由于大量竞争客户流入和流失的周期很快。然而如果单一服务运行时间长服务竞争加剧客户获取成本CAC往往会增加[16][39][128]。随着CAC的增加预测和防止流失的技术变得更加重要。维尔亚宁·马库斯等人2016将生存分析应用于移动游戏并计算了流失率类似于金融服务的流失预测[40]。由于日志数据量大游戏领域在进行流失研究时积极使用机器学习技术[10][42][43]。米洛舍维奇·米洛斯、内纳德·兹维奇和伊戈尔·安杰尔科维奇2017在游戏流失研究中创建了流失预测模型通过找出并将可能流失的客户分为A/B组提供流失预防激励并统计证明了实际效果[44]。朗格·朱利安等人2014进行了一项类似的研究揭示了与一般营销目标相比现有高可能性流失客户的营销响应率更高[45]。
此外音乐流媒体服务领域甚至举办了构建预测模型的比赛流失研究也在互联网服务和报纸订阅领域进行。报纸订阅和音乐流媒体服务提供固定费率服务客户流失与合同续约期一致。另一方面由于互联网服务根据客户的意愿进入不活跃状态合同续约几乎是实时进行的。流失预测研究还在在线约会、在线商务、问答服务和社交网络服务领域进行[46]。
有些研究从心理学角度探讨客户流失。博博拉·佐赫布等人2011结合动机理论与客户使用MMORPG游戏分析了客户动机改变时的流失情况[47]。尼克·易2016调查了大约25万名玩家显示客户对游戏的态度按国家、种族和年龄分组[48]。
在营销领域格拉迪·尼古拉斯、巴特·巴森斯和克里斯托夫·克鲁克斯2009使用了RFM最近一次、频率和货币和CLV客户生命周期价值等营销视角的特征进行流失预测[38]。
尽管少数但在人力资源和能源领域也进行了流失预测研究。萨拉迪·V·维贾亚和吉里什·凯沙夫·帕尔希卡尔2011在员工流失时进行流失研究以降低再培训成本并证明员工价值[50]。莫耶尔松·朱莉和大卫·马滕斯2015基于客户提供的能源数据和社会人口数据估计客户是否会流失到另一能源供应商[51]。
结论
在本研究中我比较了使用日志数据的流失预测分析技术。流失分析用于互联网服务和游戏、保险和管理领域。流失预测研究通常是为了改善业务成果。因此时间窗口被用来选择潜在的流失客户而不是测量客户的完全流失。客户流失的损失成本通过CAC或CLV计算。在过去预测客户流失时研究人员使用生存分析或时间序列分析结合统计学、图论和传统的机器学习算法。最近使用深度学习算法的流失预测分析出现了。深度学习算法被发现优于其他算法。这可能是由于通过计算机收集了大量的客户日志数据并使用这些数据集的全部来进行流失预测。本论文的流失预测模型使用深度学习进行流失预测数据时间戳按秒级顺序或总量巨大的客户日志数据。在这种情况下处理日志的特征工程技术对模型性能的提升有显著影响。与其他建模技术不同深度学习模型能够通过嵌入时间序列特征将高维稀疏日志数据转换为低维密集特征。此外深度学习模型可以通过层叠神经元结构从大量数据中学习客户的行为模式。因此给定细微的时间戳和大量的观察数据将这些数据应用于深度学习算法以生成潜在特征预计会比传统的流失预测模型表现更好。
这是因为如今的日志数据被收集的时间更长深度学习算法相比旧算法更能捕捉客户的潜在状态。换句话说深度学习算法今天受到关注的原因在于现代流失预测使用的大量数据及其捕捉细微变化的能力。如前文所述传统的流失预测算法包括统计方法仍然在今天被积极使用。这是因为哪种流失预测模型在性能上表现最佳取决于数据格式的不同。使用深度学习的流失预测模型是一种新的解决方案具有良好的结构来预测现代流失数据集。因此为了解决当前的问题读者需要了解流失数据集的格式并应用合适的算法来解决流失预测问题。
此外我还概述了一种性能评估方法用于比较过去到现在使用的各种流失预测算法。大多数流失预测模型都与客户关系管理相关。例如流失预测模型是否对假阳性或假阴性具有鲁棒性可能会导致性能差异。根据本文的研究许多文章除了标准的精度外还使用AUC作为性能衡量方法。一般来说由于流失客户比非流失客户少需要一种专注于流失客户的性能特定方法。ROC曲线是模型正确预测流失客户的比率和预测剩余客户为流失客户的比率的图形。因此它是一种专注于流失客户预测的性能衡量方法。
在本研究中我全面比较了流失预测问题。本文有助于在各种流失预测算法中找到满足研究人员需求的方法。此外本文预计将用于改善服务和构建更好的流失分析模型。
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