Okazaki Kotaro, Inoue Katsumi
Department of Informatics, School of Multidisciplinary Sciences, The Graduate University for Advanced Studies, SOKENDAI, Tokyo, Japan.
SONAR Inc., Tokyo, Japan.
Front Artif Intell. 2022 May 11;5:824197. doi: 10.3389/frai.2022.824197. eCollection 2022.
Due to advances in computing power and internet technology, various industrial sectors are adopting IT infrastructure and artificial intelligence (AI) technologies. Recently, data-driven predictions have attracted interest in high-stakes decision-making. Despite this, advanced AI methods are less often used for such tasks. This is because AI technology is a black box for the social systems it is meant to support; trustworthiness and fairness have not yet been established. Meanwhile in the field of marketing, strategic decision-making is a high-stakes problem that has a significant impact on business trends. For global marketing, with its diverse cultures and market environments, future decision-making is likely to focus on building consensus on the formulation of the problem itself rather than on solutions for achieving the goal. There are two important and conflicting facts: the fact that the core of domestic strategic decision-making comes down to the formulation of the problem itself, and the fact that it is difficult to realize AI technology that can achieve problem formulation. How can we resolve this difficulty with current technology? This is the main challenge for the realization of high-level human-AI systems in the marketing field. Thus, we propose customer journey mapping (CJM) automation through model-level data fusion, a process for the practical problem formulation known as explainable alignment. Using domain-specific requirements and observations as inputs, the system automatically outputs a CJM. Explainable alignment corresponds with both human and AI perspectives and in formulating the problem, thereby improving strategic decision-making in marketing. Following preprocessing to make latent variables and their dynamics transparent with latent Dirichlet allocation and a variational autoencoder, a explanation is implemented in which a hidden Markov model and learning from an interpretation transition are combined with a long short-term memory architecture that learns sequential data between touchpoints for extracting attitude rules for CJM. Finally, we realize the application of human-AI systems to strategic decision-making in marketing with actual logs in over-the-top media services, in which the dynamic behavior of customers for CJM can be automatically extracted.
由于计算能力和互联网技术的进步，各个工业部门都在采用信息技术基础设施和人工智能（AI）技术。最近，数据驱动的预测在高风险决策中引起了关注。尽管如此，先进的人工智能方法在这类任务中的应用却较少。这是因为人工智能技术对于它旨在支持的社会系统来说是一个黑箱；其可信度和公平性尚未确立。与此同时，在营销领域，战略决策是一个高风险问题，对商业趋势有重大影响。对于具有多元文化和市场环境的全球营销而言，未来的决策可能会侧重于就问题本身的表述达成共识，而不是实现目标的解决方案。有两个重要且相互矛盾的事实：国内战略决策的核心归结为问题本身的表述这一事实，以及难以实现能够进行问题表述的人工智能技术这一事实。我们如何利用当前技术解决这一难题？这是营销领域实现高级人机系统的主要挑战。因此，我们提出通过模型级数据融合实现客户旅程映射（CJM）自动化，这是一个用于实际问题表述的过程，即所谓的可解释对齐。该系统以特定领域的要求和观察结果为输入，自动输出一个客户旅程映射。可解释对齐与人类和人工智能的视角相对应，并在问题表述中发挥作用，从而改善营销中的战略决策。在使用潜在狄利克雷分配和变分自编码器对潜在变量及其动态进行预处理以使其透明之后，实施一种解释，其中将隐藏马尔可夫模型和从解释转换中学习与长短期记忆架构相结合，该架构学习接触点之间的序列数据以提取客户旅程映射的态度规则。最后，我们通过顶级媒体服务中的实际日志实现了人机系统在营销战略决策中的应用，其中可以自动提取客户旅程映射的动态行为。
