Foxtrot Communications

The 5-Day Experiment Tax

Every time your growth team ships a new A/B test with custom properties, your data pipeline breaks. Data engineers spend days updating schemas manually while experiments sit idle.

❌ Without Forge (5 Days)

Monday: Product team proposes checkout experiment
Tuesday: Engineering adds event tracking with new fields
Wednesday: Pipeline breaks (unexpected schema)
Thursday: Data engineer updates ETL manually
Friday: Pipeline fixed, results finally available

✅ With Forge (4 Hours)

9 AM: Product team ships experiment with new variant
10 AM: Event data flows to BigQuery
11 AM: Forge detects new fields, updates schema automatically
12 PM: Slack alert: "New variant detected"
1 PM: Growth team analyzes results, iterates

How Forge Handles Experiments

Example: Checkout Experiment

Your growth team wants to test a new "one-click checkout" feature:


// Control variant (existing schema)
{
"event": "checkout_started",
"user_id": "12345",
"experiment_id": "checkout_v2",
"variant": "control"
}
// Treatment variant (NEW fields)
{
"event": "checkout_started",
"user_id": "67890",
"experiment_id": "checkout_v2",
"variant": "one_click",
"one_click_enabled": true,      // ← NEW
"payment_provider": "apple_pay" // ← NEW
}

What Forge Does Automatically:

✅ Detects one_click_enabled and payment_provider fields
✅ Adds columns to your events table in BigQuery
✅ Backfills NULL for control variant (preserves historical data)
✅ Posts to Slack: "New fields in experiment checkout_v2"
✅ Versions the schema (trackable history)

Result: Your growth team analyzes results same day, no data engineering tickets.

Ship A/B Tests 20× Faster