ForTune: Running Offline Scenarios to Estimate Impact on Business Metrics

Making ideal decisions as a product leader in a web-facing company is incredibly challenging. Beyond navigating the ambiguity of customer satisfaction and achieving business goals, leaders must also ensure their products and services remain relevant, desirable, and profitable. Data and experimentation are crucial for testing product hypotheses and informing decisions. Online controlled experiments, such as A/B testing, can provide highly reliable data to support decisions. However, these experiments can be time-consuming and costly, particularly when assessing impacts on key business metrics like retention or long-term value.

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Published
2025-04-04
DOI
https://doi.org/10.1145/3690624.3709431
Primary Topic
Advanced Causal Inference Techniques
Type
article
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article

ForTune: Running Offline Scenarios to Estimate Impact on Business Metrics

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Advanced Causal Inference Techniques
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ForTune: Running Offline Scenarios to Estimate Impact on Business Metrics

Mounia Lalmas, Konstantin Sozinov, Georges Dupret, Ben Carterette, Roberto Sanchis-Ojeda, Andrey Gatash, Ziggy Zacks, Manuel Mai, Carmen Barcena Gonzalez, Shubham Bansal, Amber Yuan, Gwo Liang Lien
article en

Abstract

Making ideal decisions as a product leader in a web-facing company is incredibly challenging. Beyond navigating the ambiguity of customer satisfaction and achieving business goals, leaders must also ensure their products and services remain relevant, desirable, and profitable. Data and experimentation are crucial for testing product hypotheses and informing decisions. Online controlled experiments, such as A/B testing, can provide highly reliable data to support decisions. However, these experiments can be time-consuming and costly, particularly when assessing impacts on key business metrics like retention or long-term value.

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Advanced Causal Inference Techniques
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