CallRail + Google Analytics
Connect CallRail and Google Analytics to See the Full Attribution Picture
Put phone call data and web analytics in one place so you can measure every touchpoint and prove ROI across every channel.
Why integrate CallRail and Google Analytics?
CallRail and Google Analytics are two of the most useful tools in a marketer's stack, but they track fundamentally different customer behaviors — one capturing offline phone conversions, the other recording online interactions. When these platforms operate in silos, businesses end up with blind spots in their attribution data, unable to connect a paid search campaign to the phone call it generated. Integrating CallRail with Google Analytics closes that online-to-offline gap, giving marketing teams a complete view of what's actually driving conversions.
Automate & integrate CallRail & Google Analytics
Use case
Send Phone Call Conversions to Google Analytics as Goals
Every time CallRail logs a qualified inbound call, automatically fire a conversion event into Google Analytics so phone calls appear alongside form fills, purchases, and other digital goals. Your GA reports then reflect the full conversion picture, not just the on-site actions your tracking code can natively capture. Marketing teams get an apples-to-apples comparison of all conversion types in a single dashboard.
Use case
Attribute Phone Calls to Google Analytics Traffic Sources
Use CallRail's dynamic number insertion (DNI) session data alongside Google Analytics UTM parameters to map every inbound call back to its originating traffic source, medium, and campaign. Push enriched call records — including source, medium, and keyword — into Google Analytics as custom dimensions so attribution is visible at the session and user level. This closes the loop between ad spend and offline revenue.
Use case
Sync First-Call Data for New Lead Audience Segmentation
When CallRail identifies a first-time caller, automatically update Google Analytics with a custom event or dimension that marks the user as a new phone lead. That audience segment can then be used in Google Ads for remarketing, bid adjustments, or look-alike targeting — turning call data into audience signals that improve ad efficiency.
Use case
Track Call Duration as a Conversion Quality Signal
Not all phone calls are equal — a 30-second call rarely indicates the same intent as a 5-minute sales conversation. Use tray.ai to send CallRail call duration data to Google Analytics as a custom metric, so you can filter and segment conversions by call quality. Teams can then optimize campaigns toward high-quality calls rather than raw call volume.
Use case
Monitor Call Trends Alongside Web Traffic in Unified Reports
Automatically push daily or weekly call volume summaries from CallRail into Google Analytics as custom metrics or data layer events, so analysts can view call trends alongside web sessions, bounce rates, and page performance in one place. Correlations between content performance and call demand become visible without manual exports or spreadsheet merges.
Use case
Automate Offline Conversion Imports for Google Ads via Analytics
When CallRail marks a call as a converted lead or sale, trigger an automated workflow that pushes the offline conversion data through Google Analytics into the Google Ads offline conversion import pipeline. This feeds smart bidding with real sales signal data rather than proxy metrics like call duration or click-through rate. Ad spend then continuously optimizes toward actual revenue.
Use case
Alert Marketing Teams When Call Volume Drops Significantly
Set up a tray.ai workflow that monitors CallRail call volume and checks it against baseline traffic benchmarks from Google Analytics. If call volume drops sharply relative to web sessions — which can signal a tracking failure, landing page issue, or ad spend lapse — the marketing team gets notified automatically via Slack or email. Catching these problems early prevents data gaps that lead to bad optimization decisions.
Get started with CallRail & Google Analytics integration today
CallRail & Google Analytics Challenges
What challenges are there when working with CallRail & Google Analytics and how will using Tray.ai help?
Challenge
Bridging the Online-to-Offline Attribution Gap
Google Analytics tracks digital behavior but has no way to capture inbound phone calls, leaving a significant conversion type completely invisible in standard reporting. Manually exporting CallRail data and importing it into GA is time-consuming, error-prone, and always lagging behind real-time campaign performance.
How Tray.ai Can Help:
Tray.ai automates the real-time transfer of CallRail call events into Google Analytics via the Measurement Protocol, so phone conversions appear in GA reports within seconds of the call ending — no manual exports, no spreadsheet merges, no data lag.
Challenge
Matching CallRail Session Data to Google Analytics Client IDs
For call data to be attributed correctly in Google Analytics, each CallRail event needs to be tied to the right GA Client ID from the caller's original web session. This matching logic gets technically messy, especially across devices or when session data isn't cleanly passed through CallRail's DNI tracking.
How Tray.ai Can Help:
Tray.ai workflows can implement lookup logic that retrieves the GA Client ID stored in CallRail's session tracking, applies fallback matching rules when a direct ID isn't available, and handles edge cases without custom engineering work.
Challenge
Handling High Call Volume Without API Rate Limits
Businesses with high inbound call volumes risk hitting Google Analytics Measurement Protocol throughput limits, or creating data noise in GA if every call event is sent without filtering or batching. Unmanaged event streams can distort session metrics and inflate goal counts in ways that mislead reporting.
How Tray.ai Can Help:
Tray.ai has built-in rate limiting, event batching, and conditional logic so you can filter calls by duration, disposition, or first-time status before sending to GA. Only meaningful, qualified call events get recorded as conversions, keeping your analytics data clean and accurate.
Challenge
Keeping UTM Parameter Mapping Consistent Across Campaigns
Marketing teams frequently change campaign names, UTM structures, and tracking conventions in Google Analytics, which can cause CallRail attribution data to fall out of sync with the corresponding GA campaign dimensions. Without a centralized transformation layer, these inconsistencies compound over time and corrupt historical attribution reports.
How Tray.ai Can Help:
Tray.ai's workflow builder includes a data transformation layer where you can define standardized UTM mapping rules that normalize CallRail source and campaign data to match your Google Analytics naming conventions. Everything stays consistent as campaigns change, all managed in one place.
Challenge
Maintaining Integration Reliability Through API and Schema Changes
Both CallRail and Google Analytics release API updates, new tracking methods, and schema changes — like the move to GA4's event-based model — that can silently break integrations built on point-to-point connections or outdated scripts.
How Tray.ai Can Help:
Tray.ai's managed connectors for both CallRail and Google Analytics are maintained and updated by the platform team, with versioned API support and proactive alerting for breaking changes. When Google migrates from Universal Analytics to GA4 or CallRail updates its webhook payload, your workflows keep running without manual code fixes.
Start using our pre-built CallRail & Google Analytics templates today
Start from scratch or use one of our pre-built CallRail & Google Analytics templates to quickly solve your most common use cases.
CallRail & Google Analytics Templates
Find pre-built CallRail & Google Analytics solutions for common use cases
Template
CallRail Call Conversion to Google Analytics Goal Event
Automatically sends a custom event to Google Analytics every time CallRail logs an inbound call that meets your qualification criteria, creating a trackable goal conversion without any manual data entry or code changes.
Steps:
- Trigger: New inbound call is logged in CallRail with status 'answered' or duration above defined threshold
- Transform: Map CallRail call fields (source, medium, campaign, keyword, caller ID) to Google Analytics Measurement Protocol event parameters
- Action: Send a POST request to the Google Analytics Measurement Protocol endpoint to fire a 'phone_call_conversion' event tied to the user's Client ID
Connectors Used: CallRail, Google Analytics
Template
CallRail First-Time Caller to Google Analytics Custom Dimension Sync
When CallRail identifies a caller as a first-time contact, this template writes a custom dimension to Google Analytics marking the associated session as a new phone lead, enabling audience creation and funnel analysis for that segment.
Steps:
- Trigger: CallRail webhook fires when a call is tagged as 'first-time caller' by its tracking logic
- Lookup: Retrieve the Google Analytics Client ID from CallRail's session tracking data or match via phone number to a known session
- Action: Update the Google Analytics user profile with a 'new_phone_lead' custom dimension using the Measurement Protocol
Connectors Used: CallRail, Google Analytics
Template
Daily CallRail Call Summary to Google Analytics Custom Metrics Report
Runs on a daily schedule to pull aggregate call volume, average duration, and lead count from CallRail and push these metrics into Google Analytics as data layer events, enabling unified trend reporting without manual exports.
Steps:
- Schedule: Workflow triggers automatically at end of each business day
- Fetch: Query the CallRail API for daily call summary data including total calls, answered calls, average duration, and lead count
- Push: Send aggregated metrics to Google Analytics via the Measurement Protocol as a 'daily_call_summary' event with custom metric values
Connectors Used: CallRail, Google Analytics
Template
CallRail Qualified Lead Call to Google Analytics Offline Conversion
When a CallRail call is marked as a qualified lead by your sales team or by keyword scoring rules, this template fires an offline conversion event into Google Analytics and prepares the record for Google Ads offline conversion import to power smart bidding.
Steps:
- Trigger: CallRail call is tagged as 'qualified lead' or disposition is updated to a positive outcome in the CallRail dashboard
- Enrich: Extract UTM parameters, GCLID, and session data from the CallRail call record
- Convert: Send the GCLID and conversion details to Google Analytics as an offline conversion event, flagging it for Google Ads import
Connectors Used: CallRail, Google Analytics
Template
CallRail Call Volume Drop Alert with Google Analytics Traffic Correlation
Monitors hourly or daily call volume from CallRail against concurrent web session data from Google Analytics, and sends an automated Slack or email alert if the call-to-session ratio drops below a defined threshold, indicating a potential tracking or campaign issue.
Steps:
- Schedule: Workflow runs on a defined hourly or daily cadence
- Compare: Fetch call volume from CallRail API and web sessions from Google Analytics Reporting API for the same time window, then calculate the call-to-session conversion rate
- Alert: If the ratio drops more than 20% below the rolling 7-day baseline, send a formatted alert with current stats to the designated Slack channel or email distribution list
Connectors Used: CallRail, Google Analytics
Template
CallRail Keyword-Level Call Data to Google Analytics Campaign Dimension
Enriches Google Analytics session data with CallRail's keyword-level call attribution by pushing the triggering keyword and campaign name as custom dimensions whenever a tracked call occurs, enabling keyword-to-call reporting natively within GA.
Steps:
- Trigger: CallRail logs a new call with keyword attribution data from its dynamic number insertion tracking
- Transform: Parse CallRail's source, keyword, campaign, and ad group fields into Google Analytics Measurement Protocol custom dimension format
- Write: Send the enriched event to Google Analytics, associating the keyword and campaign data with the caller's session Client ID for attribution reporting
Connectors Used: CallRail, Google Analytics