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**Every time a search engine displays a product, it makes a decision**. Not just a sort order, but a business decision that impacts customer experience, revenue, and fairness between competing products and channels. But what actually defines a good decision? **A ranking system is effectively a judge: every query is a trial, every product is a candidate, and user behavior becomes the evidence**. The challenge is that this evidence is incomplete, biased, and distributed across channels. Clicks measure attention but not intent, while conversions capture purchases but miss delayed or offline behavior. Search ranking must also balance competing objectives. The products people click are not always the ones they buy. Visually appealing items attract curiosity and traffic, while the products that generate revenue may appear less attractive at first glance. Optimizing purely for purchases can also favor high volume, low cost items and erode overall business value. Time adds another layer of complexity. Some products perform consistently year after year, while others spike during seasonal moments or emerging trends. Ranking systems must decide whether to trust long term reliability or react to short term signals. At the same time, behavioral signals are distorted by position bias, sparse data, and delayed attribution across channels. **In an omnichannel environment where customers research online and purchase later in store, the ROPO (Research Online Purchase Offline) effect makes naive metrics misleading**. To address this challenge, the Search and Publication team developed a decision pipeline that industrializes the production of implicit judgments and acts as a **Truth Provider for AI systems**. The system transforms noisy behavioral signals into stable implicit judgments used to evaluate ranking systems, guide experimentation, train machine learning models, and provide reliable ground truth signals used by multiple internal products. Built on top of BigQuery and dbt, the pipeline processes hundreds of millions of search sessions and several millions of products every day. It integrates multi channel evidence, bias correction, Bayesian priors, uncertainty estimation, temporal weighting, and composite indicators balancing discovery and purchase signals. Attendees will gain a practical understanding of how to transform noisy behavioral data into reliable implicit judgments, and how to choose the right signals when discovery, conversion, and long term reliability pull ranking decisions in different directions. **Ranking is not just an algorithm problem. It is a decision problem**. Every decision needs an honest definition of what good actually means.
