In the evolving world of digital advertising analytics, Google’s Conversion Value Platform Comparable and by conv.time metrics represent an important upgrade in how marketers interpret conversion performance—especially when comparing Google Demand Gen campaigns with other advertising platforms. These metrics help bring nuanced clarity to conversion reporting and cross-platform comparability, solving issues that have long impacted advertisers’ ability to measure performance consistently.
This in-depth guide breaks down what these metrics are, why they matter, how they work, and practical ways to use them for more informed reporting and optimization. Let’s dive deep into the methodology, differences, and strategic value of these advanced Google Ads measurement metrics.
🧠 What Are “Platform Comparable” & “by conv.time” Metrics?
Google introduced the Platform Comparable conversion columns in Google Ads to provide advertisers with a new reporting lens—especially for Demand Gen campaigns. These columns adjust conversion reporting so that:
✔️ Platform Comparable Metrics
- Align Google Ads reporting more closely with how other advertising platforms measure conversions.
- Include view-through conversions (VTCs) instead of excluding them, like Google’s default metrics normally do.
- Isolate Demand Gen campaign conversions from other Google campaigns’ credit assignment, giving full credit to the last Demand Gen touchpoint, even when other Google interactions occurred earlier.
- This means that if a user interacted with a Demand Gen ad early and then later interacted with a Search ad before converting, the Demand Gen campaign still receives full reporting credit.
- Provide a separate reporting view that does not influence bidding or optimization—it’s purely for comparative performance analysis.
✔️ “by conv.time” Versions
Google provides enhanced versions of these metrics that are segmented based on the time the conversion occurred—not when the click happened. For details click here
Traditional columns (and Platform Comparable metrics without the “by conv.time” tag) attribute a conversion to:
📍 The time of the click that led to that conversion.
Meanwhile, “by conv.time” columns attribute the conversion to:
📍 The time the user actually completed the conversion action.
This subtle shift can significantly improve reporting accuracy when conversions happen days after the initial click—especially for longer sales cycles, subscription sign-ups, or delayed purchases. According to PPC practitioners, this is extremely useful for aligning with internal analytics and cross-platform attribution models.
🧩 Why These Metrics Matter
Google’s default conversion columns are ideal for bid optimization and machine learning models inside the platform—yet they aren’t always comparable to how other ad networks report conversions. Here’s why the newer columns matter:
🔁 1. Attribution Methodology Differences
- Standard Google conversion metrics often use data-driven attribution (DDA), which splits credit across multiple ads and touchpoints in the conversion path.
- Platform Comparable metrics bypass that shared credit model for Demand Gen campaigns by giving one campaign full credit, matching how many external platforms (like Meta, TikTok, LinkedIn) tend to attribute conversions.
This makes apples-to-apples comparisons possible, so marketers can understand if performance differences are real or simply due to different attribution rules.
🔁 2. Inclusion of View-Through Conversions (VTCs)
Standard reporting excludes VTCs—which are conversions that happen after someone sees an ad but doesn’t click it. Many platforms include those by default. Platform Comparable metrics include VTCs, giving a more holistic picture:
- More accurate when comparing display or video ads
- Better reflects brand-influenced conversions without clicks
- Reduces systemic undercounting in Google reporting
🔁 3. Timing Accuracy with “by conv.time”
Without “by conv.time,” conversions are attributed to the click’s timestamp, which can misalign actual conversion trends. Example:
➡️ A click on Monday leads to a conversion on Thursday—standard columns show it on Monday.
➡️ “By conv.time” shows it on Thursday.
This helps marketers:
- Align Google Ads data with internal analytics (CRM, GA4, backend sales data)
- Better understand actual user behavior timelines
- More accurately assess daily/weekly conversion patterns
🛠️ Practical Ways to Use These Metrics
Here’s how to integrate these columns into real reporting workflows:
📊 1. Cross-Platform Performance Comparison
If your brand runs ads on multiple platforms, these metrics help determine whether:
- Campaign performance differences are due to platform dynamics or attribution settings
- VTCs and other conversion types are fairly represented across platforms
For fair comparison:
- Match conversion events and lookback windows
- Align budgets and campaign structures
- Use Platform Comparable columns alongside cost per conversion, conversion value, and ROI metrics
📆 2. Trend & Timing Analysis
Using Conv. (Platform Comparable and by conv. time):
- Helps reveal real conversion patterns by date
- Reduces reporting distortions caused by Google’s default click-based attribution
This is especially useful for seasonal campaigns or ads with delayed actions (like lead forms that convert after a few days).
📈 3. Internal Reporting & Forecasting
Companies often sync Google data with:
- Internal CRM systems
- Revenue dashboards
- BI tools (Tableau, Power BI)
“By conv.time” metrics help align Google Ads reporting with:
- GA4 goals
- Back-end conversion records
- Financial reporting systems
This alignment is valuable for executive and cross-team insights.
⚠️ Best Practices & Cautions
While powerful, these metrics aren’t universally applicable:
❌ Not for Optimization
Platform Comparable columns don’t feed into Google’s bidding algorithms or optimization models. They’re reporting metrics only.
❌ Not Suitable For All Campaign Types
Currently, these metrics:
- Populate only for Demand Gen campaigns
- Don’t apply to Search, Display, or Performance Max in the same way as conventional columns
🟡 Interpretation Tip
A mismatch between standard and comparable columns can be positive or negative—what matters most is the directional insight: whether changes suggest better or worse performance relative to expectations or cross-platform benchmarks.