Generate a summary report with aggregate reporting
Summary reports are a powerful tool for examining your data from Attribution Reporting and Private Aggregation. This pathway will take you through the key concepts and tooling needed to enable you to build effective reports.
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6 गतिविधियां
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Introduction to summary reports
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Learn the key concepts to work with summary reports and design your data collection.
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Contribution budget for summary reports
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Learn about the role of the contribution budget for Attribution Reporting summary reports and how to allocate it to capture the data you need.
Learn how to use Noise Lab, a tool that helps grasp the effects of various noise parameters, and that enables quick exploration and assessment of various noise management strategies.
[null,null,[],[[["The Attribution Reporting API leverages aggregation keys to categorize and summarize campaign performance data based on dimensions like campaign, geography, and product."],["Advertisers can choose between two key structure strategies: a single granular key or multiple coarse keys, each with trade-offs between simplicity and noise reduction."],["Keys are generated using a hash-based approach, with source-side and trigger-side pieces set at different stages of the user journey (ad interaction and conversion)."],["Conversion data is scaled to minimize noise and protect user privacy, using a contribution budget to limit individual user impact."],["Summary reports provide aggregated insights into campaign performance, allowing advertisers to analyze data based on desired aggregation keys."]]],["Privacy Sandbox APIs, now available to most Chrome users, use summary reports with added noise to protect privacy. Managing this noise involves strategic decisions on dimension granularity, key structures, and batching frequency. The Noise Lab tool simulates these decisions, allowing users to assess noise effects. Key strategies include scaling, outlier management, and optimizing measurement goals and budget splits to improve signal-to-noise ratios. The metrics, Average Percentage Error (APE) and Root-Mean-Square Relative Error with Threshold (RMSRE_T), assess noise levels, guiding optimal key management strategies.\n"]]