Purpose

The paper aims to help enterprises gain valuable knowledge about big data implementation in practice and improve their information management ability, as they accumulate experience, to reuse or adapt the proposed method to achieve a sustainable competitive advantage.

Design/methodology/approach

Guided by the theory of technological frames of reference (TFR) and transaction cost theory (TCT), this paper describes a real-world case study in the banking industry to explain how to help enterprises leverage big data analytics for changes. Through close integration with bank's daily operations and strategic planning, the case study shows how the analytics team frame the challenge and analyze the data with two analytic models – customer segmentation (unsupervised) and product affinity prediction (supervised), to initiate the adoption of big data analytics in precise marketing.

Findings

The study reported relevant findings from a longitudinal data analysis and identified some key success factors. First, non-technical factors, for example intuitive analytics results, appropriate evaluation baseline, multiple-wave implementation and selection of marketing channels critically influence big data implementation progress in organizations. Second, a successful campaign also relies on technical factors. For example, the clustering analytics could promote customers' response rates, and the product affinity prediction model could boost efficient transaction and lower time costs.

Originality/value

For theoretical contribution, this paper verified that the outstanding characteristics of online mutual fund platforms brought up by Nagle, Seamans and Tadelis (2010) could not guarantee organizations' competitive advantages from the aspect of TCT.

中文翻译：

---

大数据分析对银行业的影响：案例研究

目的

本文旨在帮助企业在实践中获得有关大数据实施的宝贵知识，并提高他们的信息管理能力，随着他们积累经验，重用或调整所提出的方法以获得可持续的竞争优势。

设计/方法/途径

本文在技术参考框架 (TFR) 和交易成本理论 (TCT) 理论的指导下，描述了银行业的真实案例研究，以解释如何帮助企业利用大数据分析来应对变化。通过与银行的日常运营和战略规划紧密结合，案例研究展示了分析团队如何利用两种分析模型——客户细分（无监督）和产品亲和力预测（有监督）来应对挑战和分析数据，以启动大数据的采用精准营销中的数据分析。

发现

该研究报告了纵向数据分析的相关发现，并确定了一些关键的成功因素。首先，非技术因素，例如直观的分析结果、适当的评估基线、多波实施和营销渠道的选择，对组织的大数据实施进度产生了关键影响。其次，成功的营销活动还依赖于技术因素。例如，聚类分析可以提高客户的响应率，产品亲和力预测模型可以提高交易效率并降低时间成本。

原创性/价值

在理论贡献方面，本文验证了 Nagle、Seamans 和 Tadelis（2010）提出的在线共同基金平台的突出特征并不能从 TCT 方面保证组织的竞争优势。