Search Ads 360 Dimensions & Metrics: Complete Reference 2026

Search Ads 360 (SA360) is Google's enterprise search management platform, designed for advertisers who manage paid search campaigns across multiple engines. Its core value is normalizing data from Google Ads, Microsoft Ads, Yahoo Japan, Baidu, and other engines into a single reporting and optimization layer — with Floodlight conversion tracking providing a unified attribution framework across all channels.

This guide provides a complete reference of every dimension and metric available in Search Ads 360 as of 2026. We've organized them by category, explained what each one measures, and included practical context on when and how to use them for cross-engine reporting, bid strategy optimization, and conversion analysis.

What Are SA360 Dimensions vs Metrics?

Before diving into the full reference, it's important to understand the difference between dimensions and metrics in Search Ads 360 — and how they differ from native engine data.

Dimensions are descriptive attributes that identify and categorize your data. They include structural identifiers (engine account, campaign, ad group, keyword), configuration attributes (bid strategy, match type, device), and Floodlight activity definitions. Dimensions answer the question: "How do I want to slice this data?"

Metrics are quantitative measurements showing how your campaigns performed. SA360 pulls metrics from each connected engine (impressions, clicks, cost) and adds its own: Floodlight conversions, bid strategy performance, and cross-engine computed fields. Metrics answer the question: "What happened?"

The key difference from native engine reporting is that SA360 normalizes dimensions and metrics across engines. A "campaign" in Google Ads and a "campaign" in Microsoft Ads both map to the SA360 campaign dimension, enabling apples-to-apples comparison. Floodlight conversions provide a single measurement standard across all engines, replacing engine-specific conversion tracking.

How Is SA360 Data Structured?

SA360 data follows a hierarchy of Advertiser > Engine Account > Campaign > Ad Group > Ad / Keyword. The advertiser level represents your organization. Engine accounts are the connections to individual search engine accounts (e.g., a Google Ads account or a Microsoft Ads account). From there, the hierarchy mirrors native engine structures: campaigns contain ad groups, which contain ads and keywords.

SA360 overlays two additional data layers: Floodlight activities (conversion events defined in CM360) and bid strategies (automated bidding rules that span across campaigns and engines). Both of these layers have their own dimensions and metrics that don't exist in native engine reporting.

Engine Account Dimensions

Engine account dimensions identify the search engine connections within your SA360 advertiser account. These are the top-level organizational dimensions that enable cross-engine reporting and management.

Campaign and Ad Group Dimensions

Campaign and ad group dimensions mirror native engine structures but are normalized across all connected engines. This normalization enables unified reporting — you can compare campaigns across Google Ads and Microsoft Ads using the same dimension names and hierarchical structure.

Ad Dimensions

Ad dimensions describe individual ads within ad groups. SA360 normalizes ad data across engines, allowing you to compare ad creative performance across Google Ads and Microsoft Ads in a unified view.

Keyword Dimensions

Keyword dimensions describe the search terms you're bidding on and their configuration. Keywords are a fundamental dimension in search advertising — they define when your ads appear and are the primary lever for targeting intent-based audiences.

Floodlight Dimensions

Floodlight dimensions describe the conversion tracking framework that SA360 shares with Campaign Manager 360. Floodlight provides unified conversion measurement across all search engines and channels — a critical advantage for cross-engine optimization and attribution.

Core Performance Metrics

Core metrics measure the fundamental delivery and cost of your search campaigns. SA360 imports these from each connected engine and normalizes them for cross-engine comparison. These are the baseline metrics for understanding campaign reach, traffic, and efficiency.

Floodlight Conversion Metrics

Floodlight metrics measure the conversions and revenue attributed to your search campaigns through the CM360 Floodlight tracking system. These are SA360's primary conversion metrics — providing unified measurement across all connected search engines.

Engine-Specific Metrics

SA360 exposes certain metrics that are specific to individual engines. These provide additional insight into ad quality and competitiveness but may not be directly comparable across engines since each engine calculates them differently.

Bid Strategy Metrics

SA360 bid strategies use machine learning to optimize keyword bids across all connected engines toward defined performance targets. Bid strategy metrics help you understand how the algorithm is performing, whether it's meeting targets, and where opportunities exist to scale.

Audience and Targeting Breakdowns

Audience breakdowns let you segment search performance by user characteristics and targeting criteria. These dimensions help you understand which audience segments drive the best search performance and optimize bid adjustments accordingly.

Cross-Engine Comparison Metrics

Cross-engine comparison is SA360's primary differentiator. These computed metrics and reporting capabilities let you evaluate search engine performance side by side using normalized data.

How to Use These Metrics for Cross-Engine Optimization

SA360's value is maximized when you use its unified data model for cross-engine decision making. Here's a framework for using the right metrics at each stage of search advertising optimization.

For budget allocation across engines

Compare Floodlight ROAS and Floodlight CPA across engine accounts. These use the same conversion tracking, making them directly comparable. Engine-native conversion metrics are not comparable because each engine counts conversions differently. Use headroom analysis from bid strategies to identify which engines have room to scale — then reallocate budget from engines hitting diminishing returns to engines with headroom.

For keyword strategy

Track keyword-level Floodlight conversion rate across engines. The same keyword may convert at very different rates on Google vs Microsoft due to audience composition differences. Use match type breakdowns to understand how broad vs exact match performs on each engine. Monitor search query reports from both engines to find keywords that perform well on one engine and could be expanded to the other.

For bid strategy management

Monitor bid strategy status — strategies in "learning" phase should not be evaluated against targets. Check target achievement rate weekly to assess strategy health. Use estimated conversions at higher spend to determine whether increasing budget would be profitable. If a strategy is consistently missing its target CPA, either loosen the target or review the underlying conversion volume — strategies with fewer than 15-20 conversions per week struggle to optimize.

For Floodlight conversion analysis

Break down Floodlight conversions by activity to understand which conversion types each engine drives best. Some engines may excel at purchase conversions while others drive more lead form submissions. Compare click-through vs view-through conversions — search campaigns should primarily drive click-through conversions. Monitor cross-environment conversions to understand the full conversion path across devices.

For creative optimization across engines

Compare ad-level CTR and conversion rate across engines for equivalent ad messaging. The same headline may perform differently on Google vs Microsoft due to audience demographics and SERP layout differences. Use SA360's unified ad reporting to identify winning messages that transfer across engines and engine-specific opportunities where tailored creative outperforms shared copy.

Common Mistakes When Analyzing SA360 Data

Avoid these common errors that lead to flawed cross-engine optimization decisions.

1. Comparing engine-native conversions across engines

Google Ads conversion tracking and Microsoft Ads UET count conversions using different methodologies, attribution windows, and deduplication logic. Comparing Google Ads purchases to Microsoft Ads purchases is misleading. Always use Floodlight conversions for cross-engine comparison — they provide a single measurement standard applied uniformly across all engines.

2. Evaluating bid strategies too early

SA360 bid strategies require a learning phase (typically 1-2 weeks and 15-20+ conversions per week) to calibrate. Evaluating performance during learning phase produces unreliable results. Check bid strategy status before drawing conclusions. Only compare bid strategy performance after the algorithm shows "Active" status and has been running for at least 2-3 weeks.

3. Ignoring impression share metrics

A campaign with strong ROAS but 20% impression share is leaving 80% of potential impressions on the table. Track lost impression share (budget) and lost impression share (rank) to understand why. Budget-limited campaigns need more spend. Rank-limited campaigns need better Quality Scores or higher bids. Ignoring impression share means missing major scaling opportunities.

4. Applying the same bid modifiers across engines

Mobile vs desktop performance varies significantly between Google and Microsoft. Google tends to have more mobile search volume; Microsoft skews desktop. Applying the same device bid modifier across both engines wastes budget. Analyze device breakdown metrics per engine and set engine-specific bid adjustments based on each engine's actual device performance data.

5. Not leveraging cross-engine keyword insights

SA360 lets you compare keyword performance across engines, but many advertisers manage keywords independently per engine. Use cross-engine keyword reports to identify keywords that convert well on one engine but aren't active on the other. Also compare search query reports across engines — Microsoft users may search using different terms than Google users for the same intent.

6. Conflating Floodlight attribution models

Different Floodlight activities can use different attribution models (last click, first click, data-driven, etc.). If your purchase activity uses last-click attribution and your lead activity uses data-driven attribution, the CPA numbers are not calculated on the same basis. Always check the attribution model dimension when comparing conversion metrics across different Floodlight activities. Standardize attribution models when doing cross-activity analysis.