Manager effectiveness drives team performance, retention, and organizational success—but traditional measurement methods rely on annual surveys that deliver stale insights months after problems emerge. The average executive spends 23 hours a week in meetings, nearly half of which could be cut without impacting productivity (Worklytics). In hybrid and remote environments, calendars have become battlegrounds where collaboration clashes with focus time, leading to overbooked teams, burnt-out employees, and missed opportunities to do meaningful work (Worklytics).
What if you could measure manager effectiveness daily using data you already collect? Google Workspace generates rich metadata through Calendar, Gmail, and Drive that reveals management patterns in real-time. Worklytics has introduced four new ways to model work: Workday Intensity, Work-Life Balance, Manager Effectiveness, and Team Cohesion (Worklytics). This comprehensive guide shows analytics leaders how to transform raw Google Workspace data into a composite Manager Effectiveness Score that updates automatically—no surveys required.
Traditional manager effectiveness measurement suffers from three critical flaws:
Worklytics provides real-time team metrics, customizable dashboards, and actionable insights from your Google Calendar data (Worklytics). The platform processes and cleans data, generates over 400 metrics, and pushes them to users for immediate analysis (Worklytics).
Microsoft's Viva Insights research identifies five core themes that define effective management. These themes provide the foundation for our data-driven scoring model:
Worklytics helps streamline and optimize meetings, track productivity and performance metrics, analyze diversity, equity, and inclusion, assess management and leadership metrics, and get insight into employee satisfaction, retention, and turnover (Worklytics).
Google Workspace generates three primary data streams that reveal management effectiveness patterns:
Google Calendar Time Insights can boost productivity by providing detailed breakdowns of meeting time, focus time, and out-of-office/personal time (Worklytics). Time Insights is available in the web version of Google Calendar for users across various Google Workspace tiers, including Business Standard, Business Plus, Enterprise, and Education accounts (Worklytics).
Worklytics integrates with Google Calendar data along with over 25 other tools in your tech stack (Worklytics). The platform can analyze data from Gmail, Google Calendar, Google Chat, Google Drive, Google Gemini, Google Meet, and Google Workspace among many other applications (Worklytics).
First, establish your data pipeline using Worklytics' calendar v3.0 schema. This schema captures essential meeting metadata while maintaining privacy compliance.
-- Calendar Events Base Table
CREATE TABLE calendar_events (
event_id STRING,
manager_id STRING,
attendee_ids ARRAY<STRING>,
start_time TIMESTAMP,
end_time TIMESTAMP,
duration_minutes INT64,
meeting_type STRING, -- '1on1', 'team', 'cross_functional', 'external'
is_recurring BOOLEAN,
is_focus_time BOOLEAN,
created_timestamp TIMESTAMP,
last_modified TIMESTAMP
);
Extract the fundamental metrics that feed into each effectiveness pillar:
-- 1:1 Meeting Frequency (Coaching)
WITH one_on_ones AS (
SELECT
manager_id,
ARRAY_LENGTH(attendee_ids) as attendee_count,
COUNT(*) as total_1on1s,
COUNT(DISTINCT EXTRACT(WEEK FROM start_time)) as weeks_with_1on1s,
AVG(duration_minutes) as avg_1on1_duration
FROM calendar_events
WHERE meeting_type = '1on1'
AND start_time >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
GROUP BY manager_id, ARRAY_LENGTH(attendee_ids)
HAVING attendee_count = 2 -- Manager + 1 direct report
),
-- Focus Time Protection (Capacity)
focus_time_metrics AS (
SELECT
manager_id,
SUM(CASE WHEN is_focus_time THEN duration_minutes ELSE 0 END) as focus_time_minutes,
COUNT(CASE WHEN is_focus_time THEN 1 END) as focus_blocks_created,
SUM(duration_minutes) as total_calendar_minutes
FROM calendar_events
WHERE start_time >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
GROUP BY manager_id
),
-- Cross-Team Collaboration (Connection)
cross_team_collaboration AS (
SELECT
manager_id,
COUNT(*) as cross_team_meetings,
COUNT(DISTINCT EXTRACT(WEEK FROM start_time)) as weeks_with_cross_team
FROM calendar_events
WHERE meeting_type = 'cross_functional'
AND start_time >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
GROUP BY manager_id
)
Apply the five Viva Insights themes as weighting factors:
-- Manager Effectiveness Score Calculation
WITH manager_scores AS (
SELECT
m.manager_id,
-- Coaching Score (25% weight)
CASE
WHEN o.weeks_with_1on1s >= 3 THEN 100
WHEN o.weeks_with_1on1s >= 2 THEN 75
WHEN o.weeks_with_1on1s >= 1 THEN 50
ELSE 0
END as coaching_score,
-- Capacity Score (20% weight)
CASE
WHEN f.focus_time_minutes / f.total_calendar_minutes >= 0.3 THEN 100
WHEN f.focus_time_minutes / f.total_calendar_minutes >= 0.2 THEN 75
WHEN f.focus_time_minutes / f.total_calendar_minutes >= 0.1 THEN 50
ELSE 25
END as capacity_score,
-- Connection Score (20% weight)
CASE
WHEN c.weeks_with_cross_team >= 3 THEN 100
WHEN c.weeks_with_cross_team >= 2 THEN 75
WHEN c.weeks_with_cross_team >= 1 THEN 50
ELSE 25
END as connection_score,
-- Empowerment Score (20% weight) - Based on delegation patterns
-- Implementation depends on additional meeting metadata
75 as empowerment_score, -- Placeholder
-- Modeling Score (15% weight) - Based on after-hours patterns
CASE
WHEN after_hours_meetings <= 2 THEN 100
WHEN after_hours_meetings <= 5 THEN 75
WHEN after_hours_meetings <= 10 THEN 50
ELSE 25
END as modeling_score
FROM managers m
LEFT JOIN one_on_ones o ON m.manager_id = o.manager_id
LEFT JOIN focus_time_metrics f ON m.manager_id = f.manager_id
LEFT JOIN cross_team_collaboration c ON m.manager_id = c.manager_id
)
SELECT
manager_id,
ROUND(
(coaching_score * 0.25) +
(capacity_score * 0.20) +
(connection_score * 0.20) +
(empowerment_score * 0.20) +
(modeling_score * 0.15)
) as manager_effectiveness_score,
coaching_score,
capacity_score,
connection_score,
empowerment_score,
modeling_score
FROM manager_scores;
Enhance your scoring model with additional Google Workspace signals:
-- Email Response Time Analysis
WITH email_responsiveness AS (
SELECT
sender_manager_id,
recipient_id,
AVG(TIMESTAMP_DIFF(response_timestamp, sent_timestamp, HOUR)) as avg_response_hours,
COUNT(*) as total_exchanges
FROM email_threads
WHERE recipient_type = 'direct_report'
AND sent_timestamp >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
GROUP BY sender_manager_id, recipient_id
),
-- Document Collaboration Patterns
document_collaboration AS (
SELECT
owner_manager_id,
COUNT(DISTINCT collaborator_id) as unique_collaborators,
SUM(comment_count) as total_feedback_given,
AVG(days_to_first_feedback) as avg_feedback_speed
FROM document_activity
WHERE activity_date >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
GROUP BY owner_manager_id
)
By default, Google Calendar Time Insights are private to the individual user and are not visible to managers or administrators (Worklytics). However, when paired with Worklytics, these insights include organizational-level metrics and deeper context that provide the foundation for smarter decision-making, reduce burnout, and foster a healthier, more intentional workplace culture (Worklytics).
Set up automated data ingestion and processing:
-- Daily Manager Effectiveness Refresh
CREATE OR REPLACE PROCEDURE RefreshManagerEffectiveness()
BEGIN
-- Truncate and reload daily metrics
TRUNCATE TABLE manager_effectiveness_daily;
INSERT INTO manager_effectiveness_daily
SELECT
CURRENT_DATE() as score_date,
manager_id,
manager_effectiveness_score,
coaching_score,
capacity_score,
connection_score,
empowerment_score,
modeling_score,
-- Trend indicators
LAG(manager_effectiveness_score, 7) OVER (
PARTITION BY manager_id
ORDER BY score_date
) as score_7_days_ago,
-- Risk flags
CASE
WHEN manager_effectiveness_score < 50 THEN 'High Risk'
WHEN manager_effectiveness_score < 70 THEN 'Medium Risk'
ELSE 'Low Risk'
END as risk_level
FROM (
-- Insert your manager effectiveness calculation here
SELECT * FROM manager_scores
);
END;
Create executive dashboards that surface actionable insights:
# Manager Effectiveness Dashboard
view: manager_effectiveness {
sql_table_name: `project.dataset.manager_effectiveness_daily` ;;
dimension: manager_id {
type: string
sql: ${TABLE}.manager_id ;;
}
measure: avg_effectiveness_score {
type: average
sql: ${TABLE}.manager_effectiveness_score ;;
value_format: "0.0"
}
dimension: risk_level {
type: string
sql: ${TABLE}.risk_level ;;
html:
{% if value == 'High Risk' %}
<div style="color: red; font-weight: bold;">{{ value }}</div>
{% elsif value == 'Medium Risk' %}
<div style="color: orange; font-weight: bold;">{{ value }}</div>
{% else %}
<div style="color: green; font-weight: bold;">{{ value }}</div>
{% endif %} ;;
}
measure: coaching_trend {
type: number
sql: ${TABLE}.coaching_score - LAG(${TABLE}.coaching_score, 7) OVER (
PARTITION BY ${manager_id}
ORDER BY ${score_date}
) ;;
}
}
Worklytics provides real-time team metrics, customizable dashboards, and actionable insights from your Google Calendar data (Worklytics). The platform offers a unified set of metrics that shows team interaction across various tools such as email, Zoom, Slack, JIRA, Salesforce, and more (Worklytics).
Validate your Manager Effectiveness Score against known business outcomes:
-- Correlation with Employee Turnover
WITH turnover_analysis AS (
SELECT
m.manager_id,
AVG(m.manager_effectiveness_score) as avg_score,
COUNT(t.employee_id) as turnover_count,
COUNT(e.employee_id) as team_size,
SAFE_DIVIDE(COUNT(t.employee_id), COUNT(e.employee_id)) as turnover_rate
FROM manager_effectiveness_daily m
JOIN employees e ON m.manager_id = e.manager_id
LEFT JOIN turnover_events t ON e.employee_id = t.employee_id
AND t.turnover_date >= DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY)
WHERE m.score_date >= DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY)
GROUP BY m.manager_id
)
SELECT
CORR(avg_score, turnover_rate) as score_turnover_correlation,
COUNT(*) as manager_count
FROM turnover_analysis
WHERE team_size >= 3; -- Minimum team size for statistical relevance
-- Correlation with Engagement Metrics
WITH engagement_correlation AS (
SELECT
m.manager_id,
AVG(m.manager_effectiveness_score) as avg_effectiveness,
AVG(eng.engagement_score) as avg_engagement,
COUNT(*) as measurement_days
FROM manager_effectiveness_daily m
JOIN team_engagement_daily eng ON m.manager_id = eng.manager_id
AND m.score_date = eng.score_date
WHERE m.score_date >= DATE_SUB(CURRENT_DATE(), INTERVAL 60 DAY)
GROUP BY m.manager_id
HAVING measurement_days >= 30 -- Ensure sufficient data
)
SELECT
CORR(avg_effectiveness, avg_engagement) as effectiveness_engagement_correlation,
AVG(avg_effectiveness) as mean_effectiveness,
AVG(avg_engagement) as mean_engagement
FROM engagement_correlation;
Time is the most finite resource in your organization and the least understood (Worklytics). Worklytics helps protect against over-collaboration by benchmarking workflow against peers to identify if a team is spending too much time in meetings or if responsiveness to internal clients is high or low (Worklytics).
Different management roles require different effectiveness profiles:
-- Role-Based Scoring Weights
WITH role_weights AS (
SELECT
'Senior Manager' as role_type,
0.30 as coaching_weight,
0.15 as capacity_weight,
0.25 as connection_weight,
0.20 as empowerment_weight,
0.10 as modeling_weight
UNION ALL
SELECT
'Team Lead' as role_type,
0.35 as coaching_weight,
0.25 as capacity_weight,
0.15 as connection_weight,
0.15 as empowerment_weight,
0.10 as modeling_weight
UNION ALL
SELECT
'Director' as role_type,
0.20 as coaching_weight,
0.10 as capacity_weight,
0.35 as connection_weight,
0.25 as empowerment_weight,
0.10 as modeling_weight
)
SELECT
m.manager_id,
m.role_type,
ROUND(
(m.coaching_score * w.coaching_weight) +
(m.capacity_score * w.capacity_weight) +
(m.connection_score * w.connection_weight) +
(m.empowerment_score * w.empowerment_weight) +
(m.modeling_score * w.modeling_weight)
) as weighted_effectiveness_score
FROM manager_scores m
JOIN role_weights w ON m.role_type = w.role_type;
Account for business cycles and organizational changes:
-- Contextual Score Adjustments
WITH contextual_adjustments AS (
SELECT
manager_id,
score_date,
base_effectiveness_score,
-- Adjust for quarter-end periods
CASE
WHEN EXTRACT(MONTH FROM score_date) IN (3, 6, 9, 12)
AND EXTRACT(DAY FROM score_date) >= 25
THEN base_effectiveness_score * 0.9 -- 10% adjustment for quarter-end stress
ELSE base_effectiveness_score
END as quarter_adjusted_score,
-- Adjust for team size changes
CASE
WHEN team_size_change_pct > 0.25 -- 25% team growth
THEN base_effectiveness_score * 0.85 -- Adjustment for onboarding overhead
WHEN team_size_change_pct < -0.25 -- 25% team reduction
THEN base_effectiveness_score * 0.90 -- Adjustment for transition stress
ELSE base_effectiveness_score
END as team_change_adjusted_score
FROM manager_effectiveness_daily
)
SELECT
manager_id,
score_date,
LEAST(quarter_adjusted_score, team_change_adjusted_score) as final_effectiveness_score
FROM contextual_adjustments;
Worklytics can be used to ensure efficient and effective meetings for remote and hybrid teams (Worklytics). Hybrid work has changed the shape of the workday, elongating the span of the day but decreasing the intensity of work (Worklytics).
Worklytics automatically anonymizes or pseudonymizes data to protect employee privacy, secure data and ensure compliance (Worklytics). Built with privacy at its core, Worklytics uses data anonymization and aggregation to ensure compliance with GDPR, CCPA, and other data protection standards.
A Manager-Effectiveness Score is a data-driven metric that measures leadership performance using objective workplace data rather than subjective surveys. It's important because traditional annual surveys deliver stale insights months after problems emerge, while this approach provides real-time visibility into management behaviors that drive team performance, retention, and organizational success.
Google Workspace data provides rich insights through calendar analytics, email patterns, meeting behaviors, and collaboration metrics. Since the average executive spends 23 hours a week in meetings, analyzing meeting frequency, duration, attendee patterns, and response times can reveal management styles and effectiveness. Google Calendar data analytics can show how managers allocate time, support their teams, and facilitate productive collaboration.
Key metrics include one-on-one meeting frequency with direct reports, meeting efficiency ratios, email response times to team members, and collaboration network analysis. Effective managers typically have regular touchpoints with their teams, maintain reasonable meeting loads, respond promptly to employee communications, and facilitate cross-team collaboration without creating bottlenecks.
Yes, when implemented properly with data anonymization and pseudonymization techniques. Platforms like Worklytics can automatically protect employee privacy while analyzing collaboration patterns. The focus should be on aggregate behavioral patterns and trends rather than individual surveillance, ensuring compliance with privacy regulations while generating actionable insights.
Unlike annual surveys that provide delayed feedback, Google Workspace analytics offer real-time, objective insights into management behaviors. This method eliminates survey bias, provides continuous monitoring capabilities, and identifies issues as they emerge rather than months later. It also captures actual work patterns rather than perceived effectiveness, leading to more accurate and actionable assessments.
You need access to Google Workspace data through APIs or analytics platforms that integrate with Google Calendar, Gmail, Google Meet, and other collaboration tools. Worklytics offers pre-built connectors for over 25 work platforms including comprehensive Google Workspace integration, making it easier to aggregate and analyze the necessary data for building manager effectiveness metrics.
