The fundamental goal of business data analysis is to improve business decisions using data. Business users such as sales, marketing, product, or operations managers often make decisions to achieve key performance indicator (KPI) goals such as increasing customer retention, decreasing cost, and increasing sales. 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, considering multitudes of combinations and scenarios, slicing, dicing, and transforming the data accordingly. For example, 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 whose effectiveness remains unclear or fail to cater to business users. Here we argue for four functionalities that we believe are necessary to enable business users to interactively learn and reason about the relationships (functions) between sets of data attributes, facilitating data-driven decision making. We implement these functionalities in SystemD, an interactive visual 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 analysis, customer retention analysis, and deal closing analysis, and report on feedback from multiple business users. Overall, business users find SystemD intuitive and useful for quick testing and validation of their hypotheses around interested KPI as well as in making effective and fast data-driven decisions.

中文翻译：

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通过交互式假设分析增强决策制定

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（例如销售、营销、产品或运营经理）经常做出决策以实现关键绩效指标 (KPI) 目标，例如提高客户保留率、降低成本和增加销售额。为了发现假设为驱动因素的数据属性与与感兴趣的 KPI 对应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，考虑多种组合和场景，相应地对数据进行切片、切块和转换。例如，分析一年中各个季度的客户保留情况或建议跨客户层的最佳媒体渠道。然而，数据集日益复杂，再加上人类的认知局限性，使得实现多个假设变得具有挑战性，即使是简单的数据集也是如此。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，其有效性尚不清楚，要么无法满足业务用户的需求。在这里，我们论证了我们认为必要的四个功能，以使业务用户能够以交互方式学习和推理数据属性集之间的关系（功能），从而促进数据驱动的决策制定。我们在 SystemD 中实现了这些功能，这是一个交互式可视化分析系统，使业务用户能够通过询问假设问题来试验数据。我们通过三个业务用例评估系统：营销组合建模分析，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。总体而言，业务用户发现 SystemD 直观且有用，可用于围绕感兴趣的 KPI 快速测试和验证他们的假设，以及制定有效且快速的数据驱动决策。