Augmenting Decision Making via Interactive What-If Analysis

• URL: http://arxiv.org/abs/2109.06160v2
• Date: Wed, 15 Sep 2021 04:33:35 GMT
• Title: Augmenting Decision Making via Interactive What-If Analysis
• Authors: Sneha Gathani and Madelon Hulsebos and James Gale and Peter J. Haas and \c{C}a\u{g}atay Demiralp
• Abstract summary: Business users currently need to perform lengthy exploratory analyses. The increasing complexity of datasets combined with the cognitive limitations of humans makes it challenging to carry over multiple hypotheses. 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.
• Score: 4.920817773181235
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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