tomevault-io plugin: instantly updated: 2026-01-20
A/B Testing Patterns
Testing Fundamentals
One Variable at a Time
CRITICAL: Only change one element per test for clear attribution.
| Test Type | Variable | Keep Same |
|---|---|---|
| Subject Line | Subject only | Body, CTA, timing |
| Opening Line | First sentence | Subject, rest of body |
| CTA | Call to action | Subject, body intro |
| Send Time | Delivery time | All copy elements |
Sample Size Requirements
| Confidence Level | Minimum Sample per Variant |
|---|---|
| 90% | 100 |
| 95% (standard) | 150 |
| 99% | 200 |
Formula:
sample_size = (Z^2 * p * (1-p)) / E^2
Where:
Z = 1.96 for 95% confidence
p = expected conversion rate (use 0.5 if unknown)
E = margin of error (typically 0.05)
Subject Line Testing
Test Categories
| Category | Control Example | Variant Example |
|---|---|---|
| Curiosity vs Specific | "Quick question" | "2 min about {{company}}'s pipeline" |
| Personal vs Generic | "{{first_name}}, saw this" | "Your team might like this" |
| Question vs Statement | "Struggling with X?" | "How we fixed X for [Company]" |
| Short vs Medium | "Quick win?" | "{{first_name}}, 2 ideas for {{company}}" |
Best Practices
- Test 2-3 variants maximum - More variants require more sample
- Run for minimum 3 days - Account for daily patterns
- Test during stable periods - Avoid holidays, major events
- Document everything - Record hypothesis, results, learnings
Body Copy Testing
Elements to Test
| Element | Low-Lift | High-Lift |
|---|---|---|
| Opening hook | Different pain point | Different approach entirely |
| Social proof | Different company name | No social proof |
| Value proposition | Reframe benefit | Different benefit |
| CTA | Soft vs hard ask | Different action |
Copy Frameworks to Test
PAS vs AIDA:
- PAS: Problem-Agitate-Solution (emotional)
- AIDA: Attention-Interest-Desire-Action (logical)
Test Hypothesis: PAS performs better for pain-point-heavy ICPs, AIDA for solution-seekers.
Timing Tests
Variables to Test
| Variable | Options to Test |
|---|---|
| Day of week | Tue vs Thu (typically best) |
| Time of day | 8-10am vs 2-4pm |
| Timezone | Send in prospect's local time vs batch send |
| Sequence gaps | 2-day vs 3-day follow-up gaps |
Default Schedule (Starting Point)
Optimal Sending Windows:
Primary: Tuesday-Thursday, 9-11am local time
Secondary: Tuesday-Thursday, 2-4pm local time
Avoid: Monday morning, Friday afternoon
Statistical Significance
Quick Significance Check
| Total Sample | Lift Needed for 95% Confidence |
|---|---|
| 200 (100 per variant) | 15%+ lift |
| 500 (250 per variant) | 10%+ lift |
| 1000 (500 per variant) | 7%+ lift |
Decision Framework
IF lift >= 15% AND sample >= 100/variant:
Declare winner with medium confidence
IF lift >= 10% AND sample >= 250/variant:
Declare winner with high confidence
IF lift < 10% OR sample < 100/variant:
Continue test or call it inconclusive
Implementing A/B Tests in Instantly
Method 1: Split Leads
- Export lead list
- Randomly split into Variant A and Variant B groups
- Create two identical campaigns with one variable different
- Use
move_leads_to_campaignto assign leads
Method 2: Sequential Testing
- Run Control for X days, collect metrics
- Update campaign with Variant (
update_campaign_sequence) - Run Variant for X days, collect metrics
- Compare (less rigorous, use only if lead volume is limited)
Tracking Results
## A/B Test Log
**Test ID**: {uuid}
**Campaign**: {campaign_name}
**Variable**: {what_was_tested}
**Hypothesis**: {expected_outcome}
**Control**:
- Version: {control_description}
- Sample: {n}
- Open Rate: {x}%
- Reply Rate: {y}%
**Variant**:
- Version: {variant_description}
- Sample: {n}
- Open Rate: {x}%
- Reply Rate: {y}%
**Result**: {Winner|Inconclusive}
**Lift**: {z}%
**Confidence**: {confidence}%
**Learning**: {what_we_learned}
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