Augmenting Decision Making via Interactive What-If Analysis
Sneha Gathani, Madelon Hulsebos, James Gale, Peter J. Haas, Çağatay Demiralp
The fundamental goal of business data analysis is to improve business decisions by understanding the relationship between data and objectives. 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 investments, increasing sales, etc. To discover the relationship between data and their KPI of interest, business users perform data exploration by analyzing multiple slices of the dataset mentally. 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 performing such analyses is hard mentally. Existing commercial tools provide partial solutions whose effectiveness remains unclear. They are also often developed for data scientists, not business users. Here we argue for four functionalities that we believe are necessary to enable business users to reason with insights, learn the relationships between data and KPIs, and facilitate data-driven decisions. We implement these functionalities in SigmaDecision, 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 analysis, customer retention analysis, and deal closing analysis, and report on feedback from multiple business users. Overall, business users find SigmaDecision 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.
Paper | Under Review: CIDR 2022 - Conference on Innovative Data Systems Research