ForTune: Running Offline Scenarios to Estimate Impact on Business Metrics

2024-02-29

arXiv.org

AI Summary

Existing A/B testing suffers from long cycles and high operational costs, hindering rapid evaluation of product decisions on user retention and long-term profitability; offline methods, in contrast, exhibit low reliability and weak causal identification. This paper proposes a lightweight offline scenario analysis framework that integrates user behavioral log modeling, counterfactual inference, causal chain estimation of business metrics, and scalable simulation—enabling hypothesis validation within minutes. The framework introduces three novel capabilities: (1) automated generation of hypotheses grounded in large-scale business metric correlations; (2) multi-objective trade-off assessment under shifts in consumption structure; and (3) trend forecasting of long-term metrics such as retention rate and user lifetime value. Empirical evaluation on real-world production data demonstrates significantly higher prediction accuracy than baseline approaches, substantially improving both the efficiency and scientific rigor of product decision-making.

Technology Category

• Search and Optimization: Evaluation and Analysis
• Reasoning under Uncertainty: Causality
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Making ideal decisions as a product leader in a web-facing company is extremely difficult. In addition to navigating the ambiguity of customer satisfaction and achieving business goals, one must also pave a path forward for ones' products and services to remain relevant, desirable, and profitable. Data and experimentation to test product hypotheses are key to informing product decisions. Online controlled experiments by A/B testing may provide the best data to support such decisions with high confidence, but can be time-consuming and expensive, especially when one wants to understand impact to key business metrics such as retention or long-term value. Offline experimentation allows one to rapidly iterate and test, but often cannot provide the same level of confidence, and cannot easily shine a light on impact on business metrics. We introduce a novel, lightweight, and flexible approach to investigating hypotheses, called scenario analysis, that aims to support product leaders' decisions using data about users and estimates of business metrics. Its strengths are that it can provide guidance on trade-offs that are incurred by growing or shifting consumption, estimate trends in long-term outcomes like retention and other important business metrics, and can generate hypotheses about relationships between metrics at scale.

Problem

• Product Decision Impact
• Online Experimentation
• Profit Prediction

Innovation

• Scenarios Analysis
• Cost-Efficient Decision Making
• Predictive Modeling