Cogny vs Supermetrics: AI Agent vs Data Connector

Question

What's the difference between Cogny and Supermetrics for marketing data analysis?

Answer

Supermetrics is a data connector that moves data from ad platforms to spreadsheets, dashboards, or data warehouses.

Cogny is an AI agent that analyzes your data and tells you exactly what to do.

Supermetrics gives you data. Cogny gives you answers.

At a Glance Comparison

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Core Difference: ETL vs AI

Supermetrics: Extract, Transform, Load

Supermetrics is a data connector.

It extracts data from:

• Google Ads
• Meta Ads
• LinkedIn Ads
• TikTok Ads
• 100+ marketing platforms

It loads that data into:

• Google Sheets
• Google Data Studio (Looker Studio)
• Excel
• BigQuery
• Snowflake
• Other BI tools

What it does: Moves data from Point A to Point B.

What it doesn't do: Analyze the data. Tell you what it means. Recommend what to do.

You still need to:

• Build reports
• Analyze trends
• Spot opportunities
• Decide on actions

It's automation for data collection. Not automation for insights.

Cogny: AI Analysis Agent

Cogny is an AI agent.

It connects to your data sources. Analyzes everything. Generates specific, actionable tickets:

"Pause these 15 keywords (€3,200 spent, zero conversions) → Save €3,200/month"

"Reallocate 20% budget from Campaign A (2.1 ROAS) to Campaign B (6.8 ROAS) → Expected gain: €12K/month"

"Your Meta audiences overlap 67% → Consolidate → Save €1,800/month"

What it does: Analyzes your data. Finds optimization opportunities. Tells you exactly what to do.

What you do: Review recommendations. Execute in minutes. See results.

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When to Choose Supermetrics

You should choose Supermetrics if:

1. You're building custom dashboards

   • Need data in Google Sheets for formulas
   • Building Data Studio reports from scratch
   • Creating custom analysis workflows

2. You're a data analyst or BI team

   • Comfortable with data transformation
   • Want full control over data structure
   • Building reports for stakeholders

3. You need many data sources in one place

   • Running ads on 10+ platforms
   • Need to consolidate everything
   • Already have analysis capability

4. You're feeding a data warehouse

   • Loading data to BigQuery/Snowflake
   • Building centralized data infrastructure
   • Part of larger data pipeline

5. You don't need analysis, just data

   • Have in-house analysts
   • Using other tools for insights
   • Just need the data extraction

Best for:

• Data analysts
• BI teams
• Agencies building custom client dashboards
• Companies with existing analytics infrastructure

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When to Choose Cogny

You should choose Cogny if:

1. You want AI to do the analysis

   • Don't have time for manual analysis
   • Want specific recommendations, not raw data
   • Prefer "what to do" over "here's data"

2. You're optimizing performance

   • Want to reduce CAC
   • Improve ROAS
   • Find wasted spend
   • Scale efficiently

3. You're a small marketing team

   • Don't have dedicated data analysts
   • Wearing multiple hats
   • Need insights fast

4. You use BigQuery or want to

   • Already have data in BigQuery
   • Want deep analysis on complete data
   • Need to query billions of rows

5. You're tired of manual optimization

   • Spending hours in spreadsheets
   • Missing opportunities
   • Want automation

Best for:

• Performance marketers
• Growth teams
• SaaS companies
• E-commerce brands
• Marketing agencies wanting AI insights

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Can You Use Both?

Yes. And many teams do.

Common pattern:

Supermetrics: Feeds raw data into BigQuery ↓ Cogny: Analyzes BigQuery data with AI ↓ You: Execute AI recommendations

Supermetrics handles data movement. Cogny handles intelligence.

Example workflow:

1. Supermetrics loads Google Ads, Meta Ads, LinkedIn data to BigQuery
2. Cogny connects to BigQuery
3. AI analyzes all platforms together
4. You get cross-platform optimization tickets

Total cost: ~$200-400/month (Supermetrics) + $500-1000/month (Cogny)

Value: Complete data infrastructure + AI insights

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Feature Deep Dive

Data Connectivity

Supermetrics:

• 100+ connectors (Google Ads, Meta, LinkedIn, TikTok, Snapchat, Pinterest, etc.)
• Pre-built for every major ad platform
• New connectors added regularly
• Reliable, stable connections

Cogny:

• Google Ads, Meta Ads, GA4, BigQuery, custom sources
• Fewer native connectors, but covers major platforms
• Can connect via BigQuery for unlimited sources

Winner: Supermetrics for breadth. Cogny sufficient for most teams.

Analysis Capability

Supermetrics:

• Zero analysis capability
• Just moves data
• You build analysis on top

Cogny:

• AI-powered deep analysis
• Finds patterns humans miss
• Generates specific recommendations
• Continuous monitoring

Winner: Cogny. Not even close.

Reporting

Supermetrics:

• Templates for Data Studio, Sheets
• Customizable
• You design the reports
• Static (until you refresh data)

Cogny:

• Natural language queries
• "Show me CAC by channel"
• AI generates custom reports on demand
• Dynamic, interactive

Winner: Depends on use case. Supermetrics for pre-built dashboards. Cogny for ad-hoc analysis.

Time to Value

Supermetrics:

• Setup: 15-30 minutes per connector
• Build reports: 2-8 hours (for first dashboards)
• Ongoing: Manual analysis required
• Time to insights: Days to weeks

Cogny:

• Setup: 5 minutes
• Analysis: Automatic
• First tickets: Within 24 hours
• Time to insights: 1 day

Winner: Cogny for speed.

Destinations

Supermetrics:

• Google Sheets
• Excel
• Google Data Studio (Looker Studio)
• BigQuery
• Snowflake
• Redshift
• Azure Synapse
• 20+ destinations

Cogny:

• Built-in AI platform
• No need to send data elsewhere
• Can connect TO BigQuery (as a source)

Winner: Supermetrics for flexibility. Cogny for simplicity.

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Pricing Comparison

Supermetrics Pricing

Essential: $99/month

• 1 data source
• Google Sheets/Excel only
• Good for simple needs

Super: $399/month

• Multiple sources
• All destinations including BigQuery
• Most common tier

Enterprise: $999+/month

• High data volumes
• Priority support
• Custom requirements

Additional costs:

• Each data source costs extra
• High-volume surcharges
• BigQuery/Snowflake connectors more expensive

Typical spend: $200-600/month for mid-size business

Cogny Pricing

Custom pricing based on:

• Data volume
• Number of sources
• Team size

Starting: ~$500/month Enterprise: Custom

What's included:

• Unlimited AI analysis
• All features
• Growth ticket generation
• Natural language queries

Cost-Benefit Analysis

Scenario: Marketing team with $50K/month ad spend

Supermetrics option:

• Supermetrics: $399/month
• Data Studio: Free
• Analyst time: 20 hours/month analyzing data
• At $100/hour: $2,000/month labor cost
• Total cost: $2,399/month
• Output: Dashboards and manual insights

Cogny option:

• Cogny: $600/month
• Analyst time: 4 hours/month reviewing tickets
• At $100/hour: $400/month labor cost
• Total cost: $1,000/month
• Output: AI tickets + time saved + optimization wins

Savings: $1,399/month

Plus: Cogny typically finds 10-20% ad efficiency gains. On $50K spend: $5K-10K/month additional value.

ROI:

• Supermetrics ROI: Efficiency from centralized data
• Cogny ROI: Time savings + ad spend optimization

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Real User Scenarios

Scenario 1: Agency with 20 Clients

Profile:

• Managing diverse clients across industries
• Need client-facing dashboards
• Each client has different ad platforms

Best approach: Supermetrics + Cogny

Why:

Use Supermetrics to:

• Feed all client data to BigQuery
• Build beautiful client dashboards
• Standardize reporting

Use Cogny to:

• Generate optimization tickets per client
• Cross-client benchmarking
• Scale analysis across 20 accounts

Benefit: Supermetrics handles client reporting. Cogny handles optimization work.

Agency can scale without hiring more analysts.

Scenario 2: Small E-commerce Team

Profile:

• 3-person marketing team
• Google Ads + Meta Ads only
• No data analyst on staff

Best choice: Cogny

Why:

Don't need Supermetrics because:

• Only 2 platforms (not 10+)
• Cogny connects directly
• No custom dashboards needed

Need Cogny because:

• No analyst to interpret data
• AI does the analysis
• Specific recommendations to execute

Saves:

• Time (no dashboard building)
• Money (one tool instead of two)
• Complexity (simpler stack)

Scenario 3: Data-Driven SaaS Company

Profile:

• Sophisticated data team
• Data warehouse in BigQuery
• Custom analytics infrastructure
• 15+ marketing channels

Best choice: Supermetrics + Cogny

Why:

Use Supermetrics to:

• Feed all 15 channels to BigQuery
• Keep data warehouse fresh
• Integrate with existing data stack

Use Cogny to:

• Add AI layer on top of data warehouse
• Generate insights analysts might miss
• Scale analysis across channels

Benefit: Keeps existing infrastructure. Adds AI intelligence layer.

Scenario 4: Marketing Manager Without Tech Resources

Profile:

• Managing $80K/month ad spend
• No data team
• No BigQuery
• Just wants to optimize ads

Best choice: Cogny

Why:

Don't need Supermetrics because:

• Would just be data in spreadsheet
• Still need to analyze it manually
• Adds complexity without solving core problem

Need Cogny because:

• Gets actionable recommendations immediately
• No setup of dashboards required
• AI handles all analysis

Simple workflow:

1. Connect ad accounts to Cogny
2. Review AI tickets
3. Execute optimizations
4. See results

No intermediate steps. No dashboard building. No manual analysis.

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Migration Between Tools

From Supermetrics to Cogny

Common reason: Tired of building reports and doing manual analysis.

Process:

1. Keep Supermetrics feeding your data warehouse
2. Add Cogny on top of BigQuery
3. Let AI analyze the data Supermetrics collects
4. Optionally cancel Supermetrics if Cogny's native connectors suffice

What you gain:

• AI recommendations
• Time savings
• Optimization insights

What you keep:

• Existing data pipeline (if using BigQuery)

From Cogny to Supermetrics

Common reason: Rare. Would be if you need data in specific format Cogny doesn't support.

Process:

1. Set up Supermetrics connectors
2. Build your analysis layer
3. Disconnect Cogny

What you gain:

• More destination options
• Full control over data structure

What you lose:

• AI analysis
• Actionable recommendations
• Time savings

Note: This migration almost never happens. Cogny solves a different problem than Supermetrics.

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The Complementary Use Case

Many sophisticated teams use both:

Architecture:

Ad Platforms (Google, Meta, LinkedIn, TikTok, etc.)
    ↓
Supermetrics (data extraction)
    ↓
BigQuery (data warehouse)
    ↓
Cogny (AI analysis)
    ↓
Growth Tickets (actionable insights)

Why this works:

Supermetrics does what it's best at:

• Reliable data extraction
• 100+ platform connectors
• Feeds the warehouse

Cogny does what it's best at:

• AI analysis across all data
• Pattern detection
• Actionable recommendations

Combined cost: $600-1,000/month Combined value: Complete data infrastructure + AI intelligence

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FAQ

Q: Can Cogny replace Supermetrics?

For most teams: Yes.

If you only need:

• Google Ads
• Meta Ads
• GA4 (via BigQuery)

Cogny connects directly. No need for Supermetrics.

If you need:

• 10+ ad platforms
• Data in Google Sheets
• Custom Data Studio dashboards

You might want Supermetrics too.

Q: Can Supermetrics replace Cogny?

No.

Supermetrics moves data. Cogny analyzes data.

Completely different functions.

Q: Which is easier to use?

For non-technical marketers: Cogny

Connect accounts, get recommendations. Done.

For data analysts: Supermetrics

Full control over data structure and reporting.

Q: Which scales better?

Depends on dimension:

Data sources: Supermetrics (100+ connectors) Analysis depth: Cogny (AI scales infinitely) Team size: Cogny (AI replaces analyst headcount) Data volume: Both scale well

Q: Do I need a data warehouse to use either?

Supermetrics: No, but recommended for serious use

• Can use Google Sheets/Data Studio without warehouse
• BigQuery/Snowflake better for large data volumes

Cogny: Optional

• Works standalone with direct platform connections
• Even better with BigQuery for deeper analysis

Q: Which has better ROI?

Different ROI types:

Supermetrics ROI:

• Time saved on data collection
• Centralized reporting efficiency
• Typical: 5-10 hours/week saved

Cogny ROI:

• Time saved on analysis (15-20 hours/week)
• Ad spend efficiency gains (10-30%)
• Typical: $5K-20K/month value

For pure financial ROI: Cogny wins.

Q: Can I export data from Cogny?

Yes.

But that's not Cogny's strength.

If you need data export as primary function: Use Supermetrics. If you need AI insights: Use Cogny.

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The Bottom Line

Choose Supermetrics if:

• You're a data analyst
• Building custom dashboards
• Need 10+ platform connectors
• Feeding a data warehouse
• Want full control over data structure

Choose Cogny if:

• You're a marketer who wants insights, not data
• Want AI to do the analysis
• Need actionable recommendations
• Tired of manual optimization
• Small team without data analysts

Use both if:

• You're a sophisticated data organization
• Want best-in-class data pipeline + AI intelligence
• Budget allows ($800-1,200/month combined)
• Need both reporting and optimization

Most teams should choose:

Cogny alone

Why:

• Solves the actual problem (optimization)
• Doesn't require building dashboards
• Time savings + ad efficiency gains
• Simpler stack

Supermetrics is great at what it does. But what it does is move data.

Most marketers don't need more data. They need to know what to DO with data.

That's where Cogny comes in.

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See What AI Finds in Your Data

You probably don't need another dashboard.

You need to know:

• Where you're wasting money
• What to pause
• What to scale
• How to reallocate budget

AI finds this in hours. Manual analysis takes weeks.

See it yourself:

Schedule a demo and we'll analyze your campaigns live.

We'll show you what tickets Cogny would generate. Often finding $5K-20K/month in quick wins.

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About This Comparison

Written by the Cogny team—built by the founders who created AI optimization systems for Netflix, Zalando, and Momondo at Campanja, and scaled growth for Kry, Epidemic Sound, and Yubico through GrowthHackers.se.

We've used Supermetrics for years. It's a solid tool. But it solves a different problem than AI analysis.

Last Updated: December 5, 2024