Mario D’Arco, Ph.D. in Management and Information Technology, Department of Business Science - Management and Innovation Systems/DISA-MIS, University of Salerno, Italy. Mario D'Arco 博士，意大利萨莱诺大学商业科学系 - 管理与创新系统/DISA-MIS 管理和信息技术博士。
Letizia Lo Presti, Research Associate in Management, Department of Law and Economics, University of Rome Unitelma Sapienza, Italy. Letizia Lo Presti，意大利罗马大学法律与经济系管理研究助理。
Vittoria Marino, Associate Professor in Marketing, Department of Business Science - Management and Innovation Systems/DISA-MIS, University of Salerno, Italy. Vittoria Marino，意大利萨莱诺大学商业科学系 - 管理与创新系统/DISA-MIS 市场营销副教授。
Riccardo Resciniti, Full Professor in Marketing, Department of Law, Economics, Management and Quantitative Methods, University of Sannio, Italy. Riccardo Resciniti，意大利桑尼奥大学法律、经济学、管理和定量方法系市场营销正教授。
(C) （三）
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited. 这是一篇开放获取的文章，根据 Creative Commons Attribution 4.0 International 许可证的条款分发，该许可证允许在任何媒体上不受限制地重复使用、分发和复制，前提是正确引用原始作品。
Mario D’Arco (Italy), Letizia Lo Presti (Italy), Vittoria Marino (Italy), Riccardo Resciniti (Italy) Mario D'Arco （意大利）， Letizia Lo Presti （意大利）， Vittoria Marino （意大利）， Riccardo Resciniti （意大利）
EMBRACING AI AND BIG DATA IN CUSTOMER JOURNEY MAPPING: FROM LITERATURE REVIEW TO A THEORETICAL FRAMEWORK 在客户旅程地图中采用 AI 和大数据：从文献综述到理论框架
Abstract 抽象
Nowadays, Big Data and Artificial Intelligence (AI) play an important role in different functional areas of marketing. Starting from this assumption, the main objective of this theoretical paper is to better understand the relationship between Big Data, AI, and customer journey mapping. For this purpose, the authors revised the extant literature on the impact of Big Data and AI on marketing practices to illustrate how such data analytics tools can increase the marketing performance and reduce the complexity of the pattern of consumer activity. The results of this research offer some interesting ideas for marketing managers. The proposed Big Data and AI framework to explore and manage the customer journey illustrates how the combined use of Big Data and AI analytics tools can offer effective support to decision-making systems and reduce the risk of bad marketing decision. Specifically, the authors suggest ten main areas of application of Big Data and AI technologies concerning the customer journey mapping. Each one supports a specific task, such as (1) customer profiling; (2) promotion strategy; (3) client acquisition; (4) ad targeting; (5) demand forecasting; (6) pricing strategy; (7) purchase history; (8) predictive analytics; (9) monitor consumer sentiments; and (10) customer relationship management (CRM) activities. 如今，大数据和人工智能 （AI） 在营销的不同功能领域发挥着重要作用。从这个假设开始，本理论论文的主要目标是更好地了解大数据、AI 和客户旅程映射之间的关系。为此，作者修订了关于大数据和人工智能对营销实践影响的现有文献，以说明此类数据分析工具如何提高营销绩效并降低消费者活动模式的复杂性。这项研究的结果为营销经理提供了一些有趣的想法。拟议的大数据和 AI 框架用于探索和管理客户旅程，说明了大数据和 AI 分析工具的结合使用如何为决策系统提供有效支持并降低不良营销决策的风险。具体来说，作者提出了大数据和 AI 技术在客户旅程映射方面的十个主要应用领域。每个 API 都支持特定任务，例如 （1） 客户分析;（2） 推广策略;（3） 客户获取;（4） 广告定位;（5） 需求预测;（6） 定价策略;（7） 购买历史;（8） 预测分析;（9） 监控消费者情绪;以及 （10） 客户关系管理 （CRM） 活动。
In recent years, the word “Big Data” has become increasingly popular. Both academics and non-academics use this term to designate large volumes of extensively varied data that are generated, captured, and processed at high velocity (Laney, 2001). In concomitance with the rise of Big Data technologies, Artificial Intelligence (AI) is being revitalized and has again become an appealing topic for research. According to Duan, Edwards, and Dwivedi (2019), the term AI is used to designate “the ability of a machine to learn from experience, adjust to new inputs and perform human-like tasks” (p. 63). AI tools can support the processing of large amounts of data and turn them into useful information. 近年来，“大数据”一词越来越受欢迎。学术界和非学术界都使用这个术语来指代高速生成、捕获和处理的大量变化广泛的数据（Laney，2001）。随着大数据技术的兴起，人工智能 （AI） 正在焕发活力，并再次成为一个有吸引力的研究课题。根据 Duan、Edwards 和 Dwivedi （2019） 的说法，人工智能一词用于指代“机器从经验中学习、适应新输入和执行类似人类的任务的能力”（第 63 页）。AI 工具可以支持处理大量数据并将其转化为有用的信息。
As highlighted by Huang (2019), Big Data and AI are widely used in many different fields, “such as robotics, speech recognition, image recognition, machine translation, automatic response, natural language processing and automatic driving” (p. 165). Furthermore, Big Data and AI are transforming the business environment and many areas of marketing. The correlation between these methods and marketing 正如 Huang （2019） 所强调的那样，大数据和人工智能广泛应用于许多不同的领域，“例如机器人、语音识别、图像识别、机器翻译、自动响应、自然语言处理和自动驾驶”（第 165 页）。此外，大数据和 AI 正在改变商业环境和营销的许多领域。这些方法与营销之间的相关性
discipline is related to the technological progress, which enables broad implementation of Big Data and AI applications in practice, such as marketing analytics toolbox based on machine learning (Sun, Huang, Wu, Song, & Wunsch, 2017; Choi, Wallace, & Wang, 2018). 学科与技术进步有关，这使得大数据和人工智能应用在实践中得到广泛实施，例如基于机器学习的营销分析工具箱（Sun， Huang， Wu， Song， & Wunsch， 2017;Choi， Wallace， & Wang， 2018）。
Since consumer behavior is being influenced more and more by digital applications, the generation and availability of data is growing at a faster rate than ever before. The convergence of Big Data, AI, and marketing can create greater customer value and several advantages for companies. For example, marketers and organizations can use Big Data-related analytics techniques to gain important information about transactions, purchase quantities, and customer credentials (Thackeray, Neiger, Hanson, & McKenzie, 2008). Additionally, data are useful to better understand the consumer behavior, purchase preferences, and marketing trends (Yin & Kaynak, 2015). Furthermore, company management, supported by Big Data analytics tools, can make better decision about production quantity, stock control and inventory, sales forecasting, logistics optimization, supplier coordination, and purchase channels selection (Schneider & Gupta, 2016; Bradlow, Gangwar, Kopalle, & Voleti, 2017). 由于消费者行为越来越多地受到数字应用程序的影响，数据的生成和可用性以前所未有的速度增长。大数据、AI 和营销的融合可以为公司创造更大的客户价值和多项优势。例如，营销人员和组织可以使用大数据相关的分析技术来获取有关交易、购买数量和客户凭证的重要信息（Thackeray， Neiger， Hanson， & McKenzie， 2008）。此外，数据对于更好地理解消费者行为、购买偏好和营销趋势非常有用（Yin & Kaynak，2015）。此外，在大数据分析工具的支持下，公司管理层可以更好地做出关于生产数量、库存控制和库存、销售预测、物流优化、供应商协调和采购渠道选择的决策（Schneider & Gupta，2016;Bradlow， Gangwar， Kopalle， & Voleti， 2017）。
Based on these premises, it is important to investigate how Big Data and AI should be leveraged strategically to plan the customer journey. Customer journey is a metaphor to conceptualize the customer experience during the purchase cycle. Specifically, both researchers and practitioners with this metaphor designate the sequence of customer’s direct and indirect encounters with a specific product, service, or brand (Meyer & Schwager, 2007). Such encounters are mediated by different types of touchpoints, namely, online and offline channels that affect the customer’s experiences and purchase intentions. 基于这些前提，重要的是要研究如何战略性地利用大数据和 AI 来规划客户旅程。Customer journey 是一个隐喻，用于概念化购买周期中的客户体验。具体来说，研究人员和实践者都使用这个比喻来指定客户与特定产品、服务或品牌的直接和间接接触的顺序（Meyer & Schwager， 2007）。此类接触由不同类型的接触点（即影响客户体验和购买意愿的线上和线下渠道）进行调解。
As highlighted by Lemon and Verhoef (2016), customer journey consists of three phases: the prepurchase phase, the purchase phase, and the postpurchase phase. The first phase encompasses the behaviors such as need recognition, search, and the formation of consideration set assembled from exposure to information found on the web, ads, user-generated contents, words of mouth, or other stimuli. In the second phase, consumers, based on the information provided, select what they want and proceed with the payment. The third phase is characterized by such behaviors as usage and consumption, and positive or negative postpurchase engagement phenomena. 正如 Lemon 和 Verhoef （2016） 所强调的那样，客户旅程包括三个阶段：购买前阶段、购买阶段和购买后阶段。第一阶段包括需求识别、搜索等行为，以及通过接触网络、广告、用户生成的内容、口耳相传或其他刺激来形成考虑集。在第二阶段，消费者根据提供的信息选择他们想要的东西并继续付款。第三阶段的特点是使用和消费等行为，以及积极或消极的售后参与现象。
The main objective of this theoretical paper is to systematize the relationship between Big Data, AI, and customer journey map. Starting from an exploration of the extant literature concerning the impact of Big Data and AI on marketing practices, the authors aim at developing a theoretical framework focused on strategic use of Big Data and AI across the customer journey mapping. Specifically, the findings reveal how such data analytics tools can increase the marketing performance (i.e., media spend and touch point selection (see Edelman, 2010), and reduce the complexity of the purchase patterns and consumer activities. 本理论论文的主要目标是将大数据、人工智能和客户旅程图之间的关系系统化。从探索有关大数据和 AI 对营销实践影响的现有文献开始，作者旨在开发一个理论框架，专注于大数据和 AI 在客户旅程映射中的战略使用。具体来说，研究结果揭示了此类数据分析工具如何提高营销绩效（即媒体支出和接触点选择（参见 Edelman，2010 年），并降低购买模式和消费者活动的复杂性。
1. THEORETICAL BASIS 1. 理论基础
As highlighted by Fink (2005), “A literature review is a systematic, explicit, and reproducible design for identifying, evaluating, and interpreting the existing body of recorded documents” (p. 3). Literature reviews are conducted for a variety of purposes. First, they present in a rigorous way the knowledge already available on a specific topic. Second, the collection of previous works helps the researchers to identify new patterns and themes that can contribute to theory development. 正如 Fink （2005） 所强调的那样，“文献综述是一种系统、明确和可重复的设计，用于识别、评估和解释现有的记录文件”（第 3 页）。文献综述有多种目的。首先，他们以严格的方式呈现特定主题已有的知识。其次，以前作品的集合有助于研究人员确定有助于理论发展的新模式和主题。
In this research, the purpose of the literature review is (1) to define those existing research concerning the usefulness of Big Data and AI adoption in the marketing, (2) to provide a framework for implementing and managing these technologies to understand the customer journey. 在这项研究中，文献综述的目的是 （1） 定义那些关于大数据和 AI 在营销中采用的有用性的现有研究，（2） 提供一个框架来实施和管理这些技术以了解客户旅程。
Due to the existence in the academic literature of a large number of articles about Big Data and AI, we followed a specific inclusion/exclusion protocol. Firstly, publications were selected from 2014 onwards, since as highlighted by Grover and Kar 由于学术文献中存在大量关于大数据和 AI 的文章，我们遵循了特定的包含/排除协议。首先，从 2014 年开始选择出版物，因为正如 Grover 和 Kar 所强调的那样
