How Do I A/B Test RCS Campaigns?

A/B Testing RCS Campaigns: Complete Guide

A/B testing is how you turn good campaigns into great ones.

The Golden Rule: One Variable at a Time

Don't test multiple changes simultaneously. If you change subject line AND image AND CTA, you don't know what drove the lift.

Example of what NOT to do:

• Variant A: "Flash Sale!" with red image and "Shop Now" button
• Variant B: "Limited Time" with blue image and "Buy Today" button
• You changed 3 things — what drove the difference?

Example of what to do:

• Variant A: "Flash Sale!" with red image and "Shop Now" button
• Variant B: "Limited Time" with red image and "Shop Now" button
• Only subject line changed — now you know the impact of wording

What to Test (Priority Order)

1. Subject Line / First Message

• Wording: urgent vs friendly vs benefit-focused
• Personalization: name vs no name
• Expected lift: 10-30%

2. Rich Media

• Image vs no image
• Single image vs carousel
• Static image vs video
• Expected lift: 20-50%

3. Call-to-Action

• Button text: "Shop Now" vs "Browse" vs "See Deals"
• Button color and placement
• Number of buttons: 1 vs 2 vs 3
• Expected lift: 15-40%

4. Send Time

• Morning vs afternoon vs evening
• Weekday vs weekend
• Expected lift: 10-25%

5. Personalization Depth

• Generic vs name personalization
• Name vs name + location
• Basic vs behavioral personalization
• Expected lift: 20-60%

Setting Up the Test

Audience size:

• Minimum 1,000 recipients per variant
• Better: 5,000+ per variant
• Best: 10,000+ per variant

Randomization:

• Split audience randomly (not by segment)
• Ensure equal distribution

Test duration:

• Minimum: 1 week
• Optimal: 2 weeks
• Don't end early — statistical significance takes time

Holdout group (optional but valuable):

• Reserve 10% that gets NO message
• Compare variants against holdout for true lift

Primary vs Secondary Metrics

Primary metric (decides the winner):

• Usually conversion rate, revenue per message, or specific KPI
• Set BEFORE the test starts

Secondary metrics (help understand why):

• Open rate, click-through rate, engagement quality
• Use to learn and iterate

Statistical Significance

You need 95% confidence that results aren't due to random chance. Most A/B testing tools calculate this.

Quick rule of thumb:

• Less than 100 conversions per variant: not enough data
• 100-400 conversions: maybe significant
• 400+ conversions: usually significant

Sequential Testing Strategy

Month 1: Test subject lines and CTAs (high-impact, easy wins) Month 2: Test rich media variationsMonth 3: Test personalization depthMonth 4: Test timing and frequencyMonth 5: Test segment-specific creative

This gives 5-10 proven optimizations over 5 months.

Common A/B Testing Mistakes

• Testing too many things at once
• Ending tests too early
• Not reaching statistical significance
• Testing irrelevant variables
• Ignoring holdout groups
• Not documenting results

The Bottom Line

A/B testing systematically improves RCS performance. Test one variable at a time, use adequate sample sizes, run for sufficient duration, and focus on business outcomes.

Build a testing roadmap and run 1-2 tests per month. Over time, you'll have 12-24 proven optimizations that compound.