intelligems-segment-analysis

Analyze which audience segments each active Intelligems experiment is winning in. Shows device type, visitor type, and traffic source breakdowns with confidence levels. Use when you want to see segment-level performance of A/B tests.

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Install skill "intelligems-segment-analysis" with this command: npx skills add victorpay1/intelligems-plugins/victorpay1-intelligems-plugins-intelligems-segment-analysis

/intelligems-segment-analysis

Analyze segment-level performance of active Intelligems experiments.

Shows which segments (device, visitor type, traffic source) each experiment is winning in, with:

  • Visitors and orders per segment
  • Which variation is performing best
  • RPV lift and confidence levels
  • GPV lift (if COGS data exists)

Prerequisites

  • Python 3.8+
  • Intelligems API key (get from Intelligems support)

Step 1: Get API Key

Ask the user for their Intelligems API key.

If they don't provide it upfront, ask:

"What's your Intelligems API key? You can get one by contacting support@intelligems.io"

IMPORTANT: Never use a hardcoded or default API key. The user must provide their own.


Step 2: Set Up Project

Create a project directory with the analysis script:

mkdir -p intelligems-segment-analysis
cd intelligems-segment-analysis

Create config.py

Copy from references/config.py:

# Intelligems Segment Analysis Configuration

# API Configuration
API_BASE = "https://api.intelligems.io/v25-10-beta"

# Thresholds (Intelligems Philosophy: 80% is enough)
MIN_CONFIDENCE = 0.80  # 80% probability to beat baseline
MIN_RUNTIME_DAYS = 14  # Don't make status judgments until test runs 2+ weeks

# Segment types to analyze
SEGMENT_TYPES = [
    ("device_type", "BY DEVICE"),
    ("visitor_type", "BY VISITOR TYPE"),
    ("source_channel", "BY TRAFFIC SOURCE"),
]

Create segment_analysis.py

Copy the full script from references/segment_analysis.py.

Create .env

Create .env file with the user's API key:

INTELLIGEMS_API_KEY=<user's key here>

Install Dependencies

pip install requests python-dotenv tabulate

Step 3: Run Analysis

python segment_analysis.py

Step 4: Interpret Results

Status meanings:

  • Doing well - Variant beating control with 80%+ confidence
  • Not doing well - Control beating variant with 80%+ confidence
  • Inconclusive - Not enough confidence either way
  • Too early - Test running less than 2 weeks (don't trust yet)
  • Low data - Not enough orders to calculate confidence

Example output:

======================================================================
  Homepage Price Test
   Runtime: 21 days | Visitors: 45,000 | Orders: 320
======================================================================

📱 BY DEVICE
╭─────────┬──────────┬────────┬───────────┬─────────────┬──────────┬────────────╮
│ Segment │ Visitors │ Orders │ Variation │ Status      │ RPV Lift │ Confidence │
├─────────┼──────────┼────────┼───────────┼─────────────┼──────────┼────────────┤
│ Mobile  │   32,000 │    180 │ +5%       │ Doing well  │ +12.3%   │ 87%        │
│ Desktop │   13,000 │    140 │ +5%       │ Inconclusive│ +4.2%    │ 62%        │
╰─────────┴──────────┴────────┴───────────┴─────────────┴──────────┴────────────╯

This shows the +5% price variant is winning on mobile (87% confidence) but inconclusive on desktop.


Troubleshooting

"INTELLIGEMS_API_KEY not found"

  • Ensure .env file exists with the key
  • Or export: export INTELLIGEMS_API_KEY=your_key

"No active experiments found"

  • Check that experiments have status "started" in Intelligems dashboard

"Error fetching experiments"

  • Verify API key is correct
  • Check network connection

References

  • references/segment_analysis.py - Full analysis script
  • references/config.py - Configuration file
  • references/setup-guide.md - Detailed setup instructions

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