A/B testing in SMS is simpler than in email — fewer variables, faster results, cleaner attribution. But most SMS marketers either skip testing entirely (sending a single version to their whole list) or test variables that don't move meaningful metrics.
This guide covers what to actually test, how to structure a valid test, and how to interpret the results.
1. The Opening Hook (Highest Impact)
The first 10–15 words of your SMS determine whether the subscriber reads the rest. Testing different opening angles is the highest-leverage variable in SMS.
Test: Urgency-driven opening vs. value-driven opening
2. Offer Framing (High Impact)
The same offer framed differently performs differently.
Test: Percentage off vs. dollar amount
For orders under $50, percentage often wins. For orders over $100, dollar amount often wins. Test your audience's response — it varies.
3. CTA Wording (Medium Impact)
"Shop now" vs. "Grab yours" vs. "Claim your discount" — these small differences consistently show click rate variations of 2–8%.
4. Send Time (Medium Impact)
Tuesday 10 AM vs. Tuesday 7 PM, or Wednesday vs. Thursday. Timing tests require holding all other variables constant and running the test over multiple weeks to control for weekly variability.
5. Personalization (Medium Impact)
"Hey [Name]" vs. no name vs. "Hey [First Name], you left something behind"
Name personalization usually adds 3–7% lift in click rate. The exception: if your name data quality is poor (lots of "First Name" or all-caps names), skip it.
6. Message Length (Lower Impact)
Short (under 100 chars) vs. medium (100–160 chars). Results are highly context-dependent. Don't spend too much testing time here until you've tested the variables above.
Sample size: You need at least 500 contacts per variant for statistical significance on click rate. Tests with fewer than 1,000 contacts total will show noise as results. For smaller lists, accumulate data across multiple campaigns before drawing conclusions.
One variable at a time: Testing two different openings AND two different CTAs at the same time gives you no actionable data. You won't know which change drove the result.
Controlled timing: Send both variants within the same hour. Sending variant A on Tuesday and variant B on Wednesday introduces day-of-week as a confounding variable.
Random split: Use your platform's built-in A/B testing if available. If you're splitting manually, sort contacts randomly — not alphabetically (alphabetical sorts introduce geographic bias).
Statistical significance: A result is actionable when there's at least a 95% probability it wasn't due to chance. Most SMS platforms with A/B testing features calculate this for you. For manual calculation, use an online A/B test significance calculator with your variant click counts.
Not all results are created equal. A winning variant with a 14% click rate vs. 11% is meaningful. A winning variant with 12.4% vs. 12.1% on 300 contacts is noise.
Ask:
1. Is the sample large enough? (500+ per variant)
2. Is the difference large enough to be meaningful? (Generally 2%+ absolute difference)
3. Is the result statistically significant? (95% confidence or above)
If yes to all three, apply the winner to your remaining unsent contacts and update your messaging template going forward.
The real value of A/B testing is cumulative. Each test teaches you something. Over six months:
Document every test result, even the inconclusive ones. A null result (no significant difference) is still a finding — it tells you a variable isn't worth optimizing.
Maria Jensen
SMS Marketing Strategist at Textcanon
Helping businesses reach their audience through effective, compliant SMS marketing. Writing about strategy, deliverability, and growth.