Slack to Google Chat Spaces
- Brian Bloom
- Jul 9
- 7 min read
Updated: Aug 6
Skip the $20k Enterprise Bills
Our complete Slack to Google Chat migration service starts at 85% less than what the big companies charge for the same result.
Migrating from Slack to Google Chat Spaces: A Strategic Guide
Making the leap from Slack to Google Chat Spaces? You're not alone. Whether you're consolidating your Google Workspace tools, reducing subscription costs, or seeking tighter integration with Gmail and Calendar, migrating chat platforms is a significant undertaking that requires careful planning.
The key to a successful migration isn't jumping straight into the technical transfer—it's understanding exactly what you're working with first.
Step 1: Know Your Slack Estate
Before you can migrate anything, you need a comprehensive audit of your current Slack workspace. This means understanding your channels, message volume, user activity patterns, file sharing habits, and conversation history. Flying blind into a migration is a recipe for lost data and frustrated users.
The good news? You can automate this discovery process with a simple Slack bot that will give you the analytics you need to make informed migration decisions.
Building Your Slack Analytics Bot
Here's a Python script that will help you gather comprehensive statistics about your Slack workspace:
python
import json
import time
from datetime import datetime, timedelta
from collections import defaultdict
import os
from slack_sdk import WebClient
from slack_sdk.errors import SlackApiError
class SlackAnalyzer:
def __init__(self, token):
self.client = WebClient(token=token)
self.analytics = {
'conversations_analyzed': 0,
'total_messages_analyzed': 0,
'message_volume_by_period': {
'last_24_hours': 0,
'last_7_days': 0,
'last_30_days': 0,
'last_90_days': 0,
'last_year': 0
},
'messages_by_hour': defaultdict(int),
'messages_by_day_of_week': defaultdict(int),
'top_active_conversations': [],
'reaction_analytics': defaultdict(int),
'file_sharing_stats': defaultdict(int),
'thread_analytics': {
'total_threads': 0,
'messages_in_threads': 0,
'avg_thread_length': 0
}
}
def get_all_channels(self):
"""Retrieve all channels in the workspace"""
try:
channels = []
cursor = None
while True:
response = self.client.conversations_list(
limit=100,
cursor=cursor,
types="public_channel,private_channel"
)
channels.extend(response['channels'])
cursor = response.get('response_metadata', {}).get('next_cursor')
if not cursor:
break
return channels
except SlackApiError as e:
print(f"Error fetching channels: {e}")
return []
def analyze_channel_history(self, channel_id, days_back=90):
"""Analyze message history for a specific channel"""
try:
# Calculate timestamp for X days back
oldest_timestamp = (datetime.now() - timedelta(days=days_back)).timestamp()
messages = []
cursor = None
while True:
response = self.client.conversations_history(
channel=channel_id,
oldest=str(oldest_timestamp),
limit=100,
cursor=cursor
)
batch_messages = response['messages']
messages.extend(batch_messages)
cursor = response.get('response_metadata', {}).get('next_cursor')
if not cursor:
break
# Rate limiting
time.sleep(1)
return messages
except SlackApiError as e:
print(f"Error fetching messages for channel {channel_id}: {e}")
return []
def process_messages(self, messages):
"""Process messages to extract analytics"""
now = datetime.now()
for message in messages:
if 'ts' not in message:
continue
timestamp = float(message['ts'])
msg_datetime = datetime.fromtimestamp(timestamp)
# Time period analysis
hours_ago = (now - msg_datetime).total_seconds() / 3600
if hours_ago <= 24:
self.analytics['message_volume_by_period']['last_24_hours'] += 1
if hours_ago <= 168: # 7 days
self.analytics['message_volume_by_period']['last_7_days'] += 1
if hours_ago <= 720: # 30 days
self.analytics['message_volume_by_period']['last_30_days'] += 1
if hours_ago <= 2160: # 90 days
self.analytics['message_volume_by_period']['last_90_days'] += 1
if hours_ago <= 8760: # 1 year
self.analytics['message_volume_by_period']['last_year'] += 1
# Hour and day analysis
self.analytics['messages_by_hour'][msg_datetime.hour] += 1
self.analytics['messages_by_day_of_week'][msg_datetime.strftime('%A')] += 1
# Reaction analysis
if 'reactions' in message:
for reaction in message['reactions']:
self.analytics['reaction_analytics'][reaction['name']] += reaction['count']
# File sharing analysis
if 'files' in message:
for file in message['files']:
file_type = file.get('filetype', 'unknown')
self.analytics['file_sharing_stats'][file_type] += 1
# Thread analysis
if 'thread_ts' in message:
self.analytics['thread_analytics']['total_threads'] += 1
if 'reply_count' in message:
self.analytics['thread_analytics']['messages_in_threads'] += message['reply_count']
self.analytics['total_messages_analyzed'] += 1
def analyze_workspace(self, max_channels=10):
"""Run complete workspace analysis"""
print("🔍 Starting Slack workspace analysis...")
# Get all channels
channels = self.get_all_channels()
print(f"📊 Found {len(channels)} channels")
# Analyze top channels by member count
channels.sort(key=lambda x: x.get('num_members', 0), reverse=True)
top_channels = channels[:max_channels]
for i, channel in enumerate(top_channels):
print(f"📈 Analyzing channel {i+1}/{len(top_channels)}: {channel['name']}")
messages = self.analyze_channel_history(channel['id'])
if messages:
self.process_messages(messages)
# Store channel-specific data
channel_data = {
'name': channel['name'],
'type': 'private_channels' if channel['is_private'] else 'public_channels',
'activity_score': len(messages),
'total_messages': len(messages),
'recent_messages': sum(1 for m in messages if (datetime.now() - datetime.fromtimestamp(float(m['ts']))).days <= 7),
'unique_participants': len(set(m.get('user', '') for m in messages if 'user' in m)),
'avg_message_length': sum(len(m.get('text', '')) for m in messages) / len(messages) if messages else 0,
'last_activity': max(float(m['ts']) for m in messages) if messages else 0
}
self.analytics['top_active_conversations'].append(channel_data)
self.analytics['conversations_analyzed'] += 1
# Calculate thread averages
if self.analytics['thread_analytics']['total_threads'] > 0:
self.analytics['thread_analytics']['avg_thread_length'] = (
self.analytics['thread_analytics']['messages_in_threads'] /
self.analytics['thread_analytics']['total_threads']
)
return self.analytics
def export_channel_list(self):
"""Export complete channel list for migration planning"""
channels = self.get_all_channels()
channel_export = []
for channel in channels:
channel_data = {
'id': channel['id'],
'name': channel['name'],
'is_archived': channel.get('is_archived', False),
'is_private': channel.get('is_private', False),
'num_members': channel.get('num_members', 0),
'created': channel.get('created', 0),
'creator': channel.get('creator', ''),
'purpose': channel.get('purpose', {}).get('value', ''),
'topic': channel.get('topic', {}).get('value', '')
}
channel_export.append(channel_data)
return channel_export
def main():
# Set up your Slack bot token
SLACK_BOT_TOKEN = os.getenv('SLACK_BOT_TOKEN')
if not SLACK_BOT_TOKEN:
print("❌ Please set SLACK_BOT_TOKEN environment variable")
return
analyzer = SlackAnalyzer(SLACK_BOT_TOKEN)
# Run analytics
print("🚀 Running comprehensive Slack analysis...")
analytics = analyzer.analyze_workspace(max_channels=20)
# Export channel list
channel_list = analyzer.export_channel_list()
# Save results
with open('slack_analytics.json', 'w') as f:
json.dump(analytics, f, indent=2)
with open('slack_channels.json', 'w') as f:
json.dump(channel_list, f, indent=2)
print("✅ Analysis complete!")
print(f"📊 Analyzed {analytics['conversations_analyzed']} channels")
print(f"💬 Found {analytics['total_messages_analyzed']} messages")
print(f"📁 Exported {len(channel_list)} channel definitions")
if __name__ == "__main__":
main()Setting Up Your Analysis Bot
Create a Slack App: Go to api.slack.com/apps and create a new app
Add Bot Permissions: Grant these OAuth scopes:
channels:history
channels:read
groups:history
groups:read
users:read
Install the App: Install it to your workspace and copy the Bot User OAuth Token
Run the Analysis: Set your token and execute the script
bash
export SLACK_BOT_TOKEN="xoxb-your-bot-token-here"
pip install slack-sdk
python slack_analyzer.pyWhat This Analysis Reveals
The script will generate two key files:
slack_analytics.json - Runtime statistics including:
Message volume trends over time
Peak usage hours and days
Most active channels and users
Reaction usage patterns
File sharing statistics
Thread conversation patterns
slack_channels.json - Complete channel inventory with:
Channel purposes and topics
Member counts and activity levels
Creation dates and ownership
Archive status
Using These Insights for Migration Planning
Armed with this data, you can make strategic decisions about your Google Chat Spaces migration:
🎯 Prioritize Active Channels: Migrate high-activity channels first to minimize user disruption
📅 Time Your Migration: Use peak usage data to schedule migrations during low-activity periods
👥 Plan User Training: Focus training on users in the most active channels
🗂️ Archive Before Migration: Clean up inactive channels before moving to reduce clutter
📊 Set Success Metrics: Use current activity levels as benchmarks for post-migration success
The Bottom Line
A successful Slack to Google Chat Spaces migration starts with understanding your current state. Don't guess at what needs to be moved—measure it. The few hours spent running this analysis will save you weeks of migration headaches and ensure nothing important gets left behind.
Your users will thank you for the thoughtful, data-driven approach, and your migration will be smoother, faster, and more successful.
📖 Coming Next: Look out for our next blog post where we'll dive deep into the actual migration process, including automated channel mapping, user communication templates, and data transfer scripts.
🚀 Need Help With Your Migration?
Don't want to build these scripts from scratch? We've already done the heavy lifting for you. Our team has developed comprehensive, battle-tested migration scripts that can help you move from Slack to Google Chat Spaces at a fraction of the cost of enterprise migration tools.
What we provide:
🔧 Pre-built analysis and migration scripts
📊 Automated channel mapping and user migration
💾 Data preservation and backup tools
📋 Migration project templates and checklists
🎯 Custom migration strategy consultation
Contact us today to learn how we can help accelerate your Slack to Google Chat migration while reducing costs and minimizing disruption to your team.
