Office occupancy rates have become a critical metric for facilities managers and workplace analysts, especially as organizations navigate hybrid work models and optimize their real estate investments. With the average executive spending 23 hours a week in meetings, understanding actual space utilization versus booked capacity is essential for making informed decisions about office layouts, meeting room availability, and overall workspace efficiency. (Worklytics)
Google Workspace Calendar data provides a goldmine of insights for calculating true occupancy rates, but extracting actionable intelligence requires careful data processing and analysis. (Worklytics) This comprehensive guide will walk you through the entire process of leveraging Google Workspace Calendar data to calculate accurate office occupancy rates, from initial data extraction through final visualization in Looker Studio.
By the end of this tutorial, you'll have a repeatable weekly script that answers the exact question: "How do I calculate office occupancy rate using Google Workspace calendar data?" We'll cover privacy considerations, data cleaning techniques, and provide downloadable resources to streamline your implementation. (Worklytics)
Office occupancy rate measures the percentage of available workspace that is actually being used during a given time period. This metric goes beyond simple headcount to include meeting room utilization, desk booking efficiency, and the gap between scheduled versus actual attendance.
Traditional occupancy calculations often rely on badge swipes or manual counts, but these methods miss the nuanced reality of modern hybrid work. Research suggests that individual productivity may slow during the months immediately following a return-to-office due to a spike in collaboration leading to more meetings and messages. (Worklytics) This makes accurate occupancy measurement even more critical for optimizing workspace design and policies.
| Metric | Description | Calculation Method |
|---|---|---|
| Booked Occupancy | Percentage of spaces reserved via calendar | (Booked Hours / Available Hours) × 100 |
| Actual Occupancy | Percentage of booked spaces actually used | (Attended Hours / Booked Hours) × 100 |
| True Utilization | Overall space efficiency | (Attended Hours / Available Hours) × 100 |
| No-Show Rate | Percentage of bookings with no attendance | (No-Show Hours / Booked Hours) × 100 |
Google Workspace Calendar data offers several advantages for occupancy analysis:
Worklytics integrates with Google Calendar data along with over 25 other tools in your tech stack, providing a unified view of workspace utilization patterns. (Worklytics)
Before diving into data extraction, ensure you have:
Worklytics offers three approaches for connecting a Google Workspace organization to the platform for analysis. (Worklytics) Each option provides different levels of control and customization:
Option 1: Standard Integration
Appropriate for most organizations, this approach provides streamlined setup with built-in privacy controls and automated data processing.
Option 2: Custom Configuration
Offers more granular control for organizations with specific compliance requirements or unique data processing needs.
Option 3: Enterprise Setup
Provides maximum flexibility for large enterprises with complex organizational structures.
Options 2 and 3 are not shown in the web interface by default; contact Worklytics to enable them for your organization. (Worklytics)
Before implementing any occupancy tracking solution, it's crucial to address privacy concerns. Over 58% of the workforce now engages in remote work, increasing reliance on employee monitoring tools, and 86% of employees believe it should be a legal requirement for employers to disclose if they use monitoring tools. (Worklytics)
Worklytics addresses these concerns through:
The Worklytics platform collects and analyzes workplace data at the instruction of Customer Organizations on their behalf, in accordance with their Privacy Policy, Terms of Service, and any customer agreement, laws, or regulations which may supersede those terms. (Worklytics)
First, set up API access to extract calendar data from your Google Workspace environment. This involves:
The core data extraction focuses on two primary event types:
Meeting Room Bookings
Desk Reservations
Extracted calendar data typically includes:
| Field | Description | Example |
|---|---|---|
event_id |
Unique event identifier | abc123def456 |
resource_email |
Room or desk identifier | conference-room-a@company.com |
start_time |
Event start timestamp | 2025-08-04T09:00:00Z |
end_time |
Event end timestamp | 2025-08-04T10:00:00Z |
organizer |
Event creator | user@company.com |
attendees |
Invited participants | [user1@company.com, user2@company.com] |
status |
Event status | confirmed, cancelled, tentative |
response_status |
Attendance confirmation | accepted, declined, needsAction |
Once extracted, import the raw calendar data into BigQuery for processing. Create a staging table structure that accommodates the various data types and nested fields from the Calendar API.
Here are sample SQL queries for cleaning and processing the calendar data:
Remove Duplicate Events
-- Remove duplicate calendar events
CREATE OR REPLACE TABLE `project.dataset.cleaned_events` AS
SELECT DISTINCT
event_id,
resource_email,
start_time,
end_time,
organizer,
attendees,
status
FROM `project.dataset.raw_calendar_events`
WHERE status != 'cancelled'
AND start_time >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 30 DAY)
Calculate Event Duration
-- Add duration calculations
CREATE OR REPLACE TABLE `project.dataset.events_with_duration` AS
SELECT *,
TIMESTAMP_DIFF(end_time, start_time, MINUTE) as duration_minutes,
EXTRACT(HOUR FROM start_time) as start_hour,
EXTRACT(DAYOFWEEK FROM start_time) as day_of_week,
FORMAT_TIMESTAMP('%Y-%m-%d', start_time) as event_date
FROM `project.dataset.cleaned_events`
Identify Meeting Rooms vs Desk Bookings
-- Categorize resource types
CREATE OR REPLACE TABLE `project.dataset.categorized_events` AS
SELECT *,
CASE
WHEN REGEXP_CONTAINS(resource_email, r'room|conference|meeting') THEN 'meeting_room'
WHEN REGEXP_CONTAINS(resource_email, r'desk|workspace|hotdesk') THEN 'desk'
ELSE 'other'
END as resource_type
FROM `project.dataset.events_with_duration`
One of the most critical aspects of accurate occupancy calculation is accounting for no-shows. This requires analyzing attendee response patterns and actual participation data.
Attendance Analysis
-- Calculate attendance rates
CREATE OR REPLACE TABLE `project.dataset.attendance_analysis` AS
SELECT
event_id,
resource_email,
resource_type,
start_time,
duration_minutes,
ARRAY_LENGTH(attendees) as invited_count,
ARRAY_LENGTH(
ARRAY(
SELECT attendee
FROM UNNEST(attendees) as attendee
WHERE attendee.response_status = 'accepted'
)
) as accepted_count,
ARRAY_LENGTH(
ARRAY(
SELECT attendee
FROM UNNEST(attendees) as attendee
WHERE attendee.response_status IN ('accepted', 'needsAction')
)
) as likely_attendees
FROM `project.dataset.categorized_events`
Before calculating occupancy rates, establish baseline capacity for each resource:
-- Define resource capacity
CREATE OR REPLACE TABLE `project.dataset.resource_capacity` AS
SELECT
resource_email,
resource_type,
CASE resource_type
WHEN 'meeting_room' THEN 8 -- 8 hours per day
WHEN 'desk' THEN 8 -- 8 hours per day
ELSE 8
END as daily_capacity_hours,
CASE resource_type
WHEN 'meeting_room' THEN 12 -- max occupancy
WHEN 'desk' THEN 1 -- single occupancy
ELSE 1
END as max_occupancy
FROM (
SELECT DISTINCT resource_email, resource_type
FROM `project.dataset.categorized_events`
)
-- Calculate daily occupancy rates
CREATE OR REPLACE TABLE `project.dataset.daily_occupancy` AS
SELECT
event_date,
resource_email,
resource_type,
COUNT(*) as total_bookings,
SUM(duration_minutes) / 60.0 as booked_hours,
SUM(duration_minutes * likely_attendees) / 60.0 as estimated_occupied_hours,
AVG(SAFE_DIVIDE(likely_attendees, invited_count)) as avg_attendance_rate,
rc.daily_capacity_hours,
SAFE_DIVIDE(SUM(duration_minutes) / 60.0, rc.daily_capacity_hours) * 100 as booked_occupancy_pct,
SAFE_DIVIDE(SUM(duration_minutes * likely_attendees) / 60.0, rc.daily_capacity_hours) * 100 as actual_occupancy_pct
FROM `project.dataset.attendance_analysis` aa
JOIN `project.dataset.resource_capacity` rc ON aa.resource_email = rc.resource_email
GROUP BY event_date, resource_email, resource_type, rc.daily_capacity_hours
For more sophisticated analysis, consider these additional metrics:
Peak Hour Analysis
-- Identify peak usage hours
CREATE OR REPLACE TABLE `project.dataset.hourly_occupancy` AS
SELECT
start_hour,
resource_type,
COUNT(*) as booking_count,
AVG(duration_minutes) as avg_duration,
SUM(likely_attendees) as total_attendees
FROM `project.dataset.attendance_analysis`
WHERE start_hour BETWEEN 8 AND 18 -- Business hours
GROUP BY start_hour, resource_type
ORDER BY start_hour
Weekly Patterns
-- Analyze weekly utilization patterns
CREATE OR REPLACE TABLE `project.dataset.weekly_patterns` AS
SELECT
day_of_week,
CASE day_of_week
WHEN 1 THEN 'Sunday'
WHEN 2 THEN 'Monday'
WHEN 3 THEN 'Tuesday'
WHEN 4 THEN 'Wednesday'
WHEN 5 THEN 'Thursday'
WHEN 6 THEN 'Friday'
WHEN 7 THEN 'Saturday'
END as day_name,
resource_type,
AVG(actual_occupancy_pct) as avg_occupancy,
COUNT(DISTINCT resource_email) as active_resources
FROM `project.dataset.daily_occupancy`
GROUP BY day_of_week, resource_type
ORDER BY day_of_week
Worklytics provides real-time team metrics, customizable dashboards, and actionable insights from your Google Calendar data. (Worklytics) The platform processes and cleans data, generating over 400 metrics that can enhance your occupancy analysis. (Worklytics)
To integrate your processed occupancy data with Worklytics:
Worklytics' anonymization proxy ensures that individual privacy is protected while still providing valuable occupancy insights. (Worklytics) This is particularly important given that 30% of companies are embracing a Structured Hybrid work environment, often defining Anchor Days when all employees are onsite. (Worklytics)
The platform aggregates individual booking patterns into organizational insights without exposing personal calendar details, ensuring compliance with privacy regulations while delivering actionable occupancy intelligence.
Create a comprehensive Looker Studio dashboard to visualize your occupancy metrics:
Key Dashboard Components:
| Component | Purpose | Data Source |
|---|---|---|
| Occupancy Overview | High-level KPIs | daily_occupancy table |
| Resource Utilization | Individual space performance | daily_occupancy table |
| Peak Hours Heatmap | Usage patterns by time | hourly_occupancy table |
| Weekly Trends | Day-of-week analysis | weekly_patterns table |
| No-Show Analysis | Booking vs attendance gaps | attendance_analysis table |
Executive Summary Cards
Trend Analysis Charts
Detailed Breakdowns
To accelerate your implementation, we've created a pre-configured Looker Studio dashboard template that connects directly to the BigQuery tables created in this guide. The template includes:
Accurate occupancy calculation requires sophisticated no-show detection. Consider these approaches:
Response-Based Detection
Historical Pattern Analysis
Integration with Access Systems
Implement these quality checks to ensure accurate occupancy calculations:
-- Data quality validation queries
SELECT
'Duplicate Events' as check_type,
COUNT(*) as issue_count
FROM (
SELECT event_id, COUNT(*) as cnt
FROM `project.dataset.cleaned_events`
GROUP BY event_id
HAVING cnt > 1
)
UNION ALL
SELECT
'Invalid Durations' as check_type,
COUNT(*) as issue_count
FROM `project.dataset.events_with_duration`
WHERE duration_minutes <= 0 OR duration_minutes > 480 -- 8 hours max
UNION ALL
SELECT
'Future Events' as check_type,
COUNT(*) as issue_count
FROM `project.dataset.cleaned_events`
WHERE start_time > CURRENT_TIMESTAMP()
Create a repeatable process for weekly occupancy reporting:
Automated Workflow Components:
Sample Automation Schedule:
Set up monitoring to ensure data pipeline reliability:
Leverage historical data to predict future occupancy patterns:
Seasonal Adjustments
Capacity Planning
Hybrid Work Optimization
With hybrid work changing the shape of the workday, elongating the span of the day and changing the intensity of work, occupancy analytics become even more critical. (Worklytics) Use your occupancy data to:
Worklytics can help streamline and optimize meetings, track productivity and performance metrics, analyze diversity, equity, and inclusion, assess management and leadership metrics, and provide insight into employee satisfaction, retention, and turnover. (Worklytics)
Combine occupancy data with other workplace metrics:
Meeting Effectiveness Analysis
Employee Experience Metrics
Space Utilization Optimization
Worklytics supports strategic decisions in areas like space utilization and occupancy planning by providing visibility into how physical and digital workspaces are used. (Worklytics) This comprehensive approach helps organizations:
Google Workspace Calendar data provides valuable insights into meeting room bookings, in-person vs. virtual meetings, and actual space utilization patterns. By analyzing calendar events, location data, and meeting types, organizations can determine real occupancy rates versus booked capacity, helping optimize real estate investments and space planning decisions.
Key metrics include meeting room utilization rates, percentage of in-person vs. virtual meetings, peak occupancy hours, average meeting duration, and space capacity versus actual attendance. These metrics help identify underutilized spaces and optimize office layouts for hybrid work environments.
Worklytics offers direct Google Calendar integration that analyzes meeting patterns, room bookings, and collaboration data while maintaining privacy compliance. The platform can process calendar data to identify occupancy trends, meeting effectiveness, and space utilization patterns across your organization.
When analyzing calendar data, it's crucial to maintain employee privacy by aggregating data at appropriate levels and avoiding individual tracking. Worklytics ensures GDPR compliance and focuses on organizational patterns rather than individual behaviors, helping organizations balance insights with privacy requirements.
Anchor days, where all employees are required onsite, can significantly impact occupancy calculations by creating predictable spikes in space utilization. Research shows 30% of companies use structured hybrid models, which can lead to increased collaboration and meeting frequency, affecting both productivity and space requirements during designated in-office days.
You'll need access to Google Workspace Calendar APIs, data processing tools for cleaning and standardizing datasets, and visualization platforms for reporting. Worklytics integrates with over 25 collaboration tools and can connect to existing data warehouses, providing machine learning capabilities to clean and analyze occupancy data effectively.
