通过交互式假设分析增强决策制定
Augmenting Decision Making via Interactive What-If Analysis
摘要：
业务数据分析的基本目标是使用数据改进业务决策。业务用户通常会做出决策以实现关键绩效指标（KPI），例如增加客户保留率或销售额，或降低成本。为了发现假设为驱动因素的数据属性与对应于感兴趣的KPI的数据属性之间的关系，业务用户目前需要执行冗长的探索性分析。这涉及考虑多种组合和场景，并相应地对数据执行切片、切割和变换，例如，分析一年中各季度的客户保留率，或建议跨客户阶层的最佳媒体渠道。然而，数据集日益复杂，加上人类的认知局限性，使得即使是简单的数据集，也很难进行多个假设。因此，在心理上进行这样的分析是很困难的。现有的商业工具要么提供部分解决方案，要么无法完全满足商业用户的需求。在这里，我们认为四个功能，使业务用户交互式学习和推理的数据属性集之间的关系，从而促进数据驱动的决策。我们在SystemD中实现了这些功能，SystemD是一个交互式可视化数据分析系统，使业务用户能够通过提出假设问题来实验数据。我们通过三个业务用例来评估该系统：营销组合建模、客户保留分析和交易完成分析，并报告来自多个业务用户的反馈。用户发现SystemD的功能非常有用，可以围绕他们感兴趣的KPI快速测试和验证他们的假设，解决他们未满足的分析需求。反馈还表明，可以增强用户体验设计，以进一步提高这些功能的可理解性。
英文摘要：
The fundamental goal of business data analysis is to improve business decisions using data. Business users often make decisions to achieve key performance indicators (KPIs) such as increasing customer retention or sales, or decreasing costs. To discover the relationship between data attributes hypothesized to be drivers and those corresponding to KPIs of interest, business users currently need to perform lengthy exploratory analyses. This involves considering multitudes of combinations and scenarios and performing slicing, dicing, and transformations on the data accordingly, e.g., analyzing customer retention across quarters of the year or suggesting optimal media channels across strata of customers. However, the increasing complexity of datasets combined with the cognitive limitations of humans makes it challenging to carry over multiple hypotheses, even for simple datasets. Therefore mentally performing such analyses is hard. Existing commercial tools either provide partial solutions or fail to cater to business users altogether. Here we argue for four functionalities to enable business users to interactively learn and reason about the relationships between sets of data attributes thereby facilitating data-driven decision making. We implement these functionalities in SystemD, an interactive visual data analysis system enabling business users to experiment with the data by asking what-if questions. We evaluate the system through three business use cases: marketing mix modeling, customer retention analysis, and deal closing analysis, and report on feedback from multiple business users. Users find the SystemD functionalities highly useful for quick testing and validation of their hypotheses around their KPIs of interest, addressing their unmet analysis needs. The feedback also suggests that the UX design can be enhanced to further improve the understandability of these functionalities.
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