SMS Marketing A/B Testing：How to Optimize Your Campaigns and Boost Revenue

Introduction to SMS Marketing A/B Testing

A/B testing, also known as split testing, is an experiment where two versions of an SMS campaign are compared to determine which one performs better. The goal is to optimize SMS marketing results by testing different campaign variables.

With A/B testing, an SMS audience is split into two groups. Each group receives a different version of the message, known as a variant. The variants are identical except for one variable that is being tested. For example, the test could be performed on copy, imagery, emoji usage, timing, or call-to-action.

After sending the two campaign variants to the test groups, you would measure and compare the performance of each one based on your selected metrics. These could include open rate, click-through rate, conversion rate, revenue per message, etc.

The variant that outperforms the other is considered the winner. You would then send that optimized version of the campaign to your entire subscriber list to boost results.

Some key benefits of A/B testing SMS campaigns include:

• Gain data-driven insights into subscriber behavior
• Identify the best-performing combinations of variables
• Improve open rates, clicks, and conversions
• Increase revenue and return on SMS marketing spend
• Refine messaging to better resonate with your audience

Successful SMS A/B testing requires setting a test duration and minimum sample size to achieve statistical significance. It also involves careful analysis of the data and results. When executed properly, A/B testing can significantly improve SMS campaign performance.

How to Choose a Hypothesis

When setting up an A/B test, the first step is to come up with hypotheses for what changes could improve your SMS campaign performance. This involves brainstorming different variables in your messaging that you could test.

Some examples of elements to hypothesize about changing:

• Message content and tone
• Use of visuals like images or emoji
• Call-to-action
• Offer or incentive
• Send date and time
• Frequency of messages
• Segmentation and targeting

For each variable, form an hypothesis predicting how the change will affect your KPIs. For example:

Sending the SMS campaign on Tuesday evenings instead of Monday mornings will increase open rates by 15%.

Be sure to focus on hypotheses that have the potential for high impact on revenue or subscriber engagement. Testing minor copy changes is unlikely to produce significant lifts.

Prioritize the hypotheses based on potential upside, ease of testing, and alignment with strategic goals. Limit your A/B test to 1-2 primary hypothesis to avoid too many variables.

By starting with a strong hypothesis, you give your test the best chance of revealing impactful insights.

Selecting Metrics to Measure

When running an A/B test for SMS marketing, you need to determine the right metrics to track in order to measure performance. There are a few key metrics every SMS marketer should focus on:

Open Rates

The open rate shows the percentage of subscribers who opened your SMS message. This helps you gauge initial engagement and interest in your message. If Version A receives a significantly higher open rate than Version B, it likely indicates Version A's content is more intriguing to your subscribers.

Click-Through Rates

The click-through rate (CTR) reveals how many people who opened the message went on to click on a link or CTA included in the message. A higher CTR for a variation indicates the offer or link resonated better with subscribers.

Bounce Rates

Bounce rates in SMS refer to the percentage of messages that couldn't be delivered due to invalid numbers. Monitor this to ensure your SMS list is clean and up-to-date. If one variation has a significantly higher bounce rate, it may indicate an issue with your segmentation or delivery for that test.

Conversions

For ecommerce brands, conversions mean how many people made a purchase after receiving the SMS message. For other businesses, it could mean signups, downloads, event registrations, consultations booked, etc. Conversions demonstrate how well your SMS achieved your desired action.

Revenue

For ecommerce, track how much revenue was driven by each version. The goal is to determine which SMS copy and creative not only converts more people, but converts them into higher paying customers. This allows you to calculate which variation ultimately leads to higher ROI.

Analyzing this data will reveal which version of your SMS campaign resonates best with your subscribers and aligns with your goals. Make data-driven decisions to optimize for key metrics like open rate, CTR, conversions, and revenue.

Determining Test Duration and Sample Size

One of the most important considerations when running A/B tests is determining the minimum duration and ideal sample size needed to achieve statistical significance.

Test Duration

Experts recommend running SMS A/B tests for a minimum of 1 week. This ensures there is enough time for the test audience to receive the messages, interact, and convert. Shorter test periods of just 1-2 days are seldom long enough to gather sufficient data.

Aim for at least a 7 day testing period, and longer durations of 2-4 weeks for more robust results. Campaigns sent to cold audiences may require longer tests than those sent to highly engaged subscribers.

Sample Size

The sample size, or number of subscribers in each test variant, directly impacts the statistical significance of your A/B test results. Larger sample sizes reduce variability and make the test more reliable.

As a general rule of thumb, each variant group should contain at least 2,500 subscribers to produce actionable data. For smaller lists, test on the maximum size possible. Statistical significance calculators can also help determine ideal sample size.

Split your list evenly between test variants to remove bias. Avoid testing on fewer than 1,000 subscribers, as results may be inconclusive. Always check that sample size and test duration is sufficient to achieve a 95% or higher confidence level.

Avoid Short Tests

Brief 1-2 day A/B test periods almost never provide statistically significant results, and winners can be determined by normal day-to-day fluctuations. Stick to longer test durations for reliable, impactful insights.

Setting Up the Test

Once you have your hypothesis and test campaign versions ready, it's time to set up your A/B test. Here are the steps to properly configure your split test:

Split Your Audience

Divide your audience into two segments - the control group (Group A) and variant group (Group B). Make sure to split your list randomly so there is no sampling bias. You want the two groups to be statistically similar in terms of demographics, past purchase history, and other attributes.

A 50/50 split is common, but you may adjust the proportion based on your sample size needs. Just ensure neither group is too small to draw conclusions from.

Send Control Version to Group A

Send your original non-modified SMS campaign version to Group A. This is your control that serves as the benchmark to measure against. Make sure Group A mirrors your typical target audience.

Send Variant Version to Group B

Send the modified SMS campaign version to Group B. This is your variant you are testing to see if it outperforms the control. For example, if you are testing subject lines, Group B would receive the version with a new subject line.

Keep everything else in the SMS campaigns identical between Group A and B - the only change should be what you are specifically testing. Also, send the campaigns at the same time to control other variables.

You're now ready to monitor the results and determine if your variant shows a statistically significant uplift over the control. Crunch the numbers once your test is complete.

Analyzing and Selecting a Winner

Once your A/B test has run for your predetermined duration, it's time to analyze the results and select a winner. Here are the key steps in this process:

• Compare metrics between the control and variant. Calculate the lift for each variant compared to the control for your selected metrics. For example, if the open rate for the control was 20% and the open rate for Variant A was 25%, then Variant A achieved a 25% lift in open rate.

• Conduct statistical significance testing. Given natural variances in data, it's important to test whether the differences between variants are statistically significant and not just random chance. There are a few ways to test significance:

  • Percentage change - If a variant's lift is over 10-15% compared to the control, it's likely statistically significant.

  • Confidence interval - Calculate the confidence interval for each variant's metric. If the intervals don't overlap between control and variant, the difference is likely significant.

  • A/B testing calculators - Use a tool like Google Optimize or VWO to automatically calculate significance.

• Declare a winner. Based on the size of lift and statistical significance, declare which variation performed best. If no variant is clearly superior, run the test again with a larger sample.

• Implement the winning variation. Going forward, use the winning version of your campaign for future sends. Continue monitoring its performance to ensure the lift remains consistent.

By methodically analyzing A/B testing data, you can confidently determine the optimal version of your SMS campaign to boost results. Just be sure to achieve statistical significance before declaring a winner.

Optimizing Campaigns with A/B Test Results

Once you've run your A/B test and determined a winner, it's time to act on those results. Here are some tips for optimizing your SMS campaigns using your A/B test findings:

Implement the Winning Variation More Broadly

Take the better performing variation from your A/B test and roll it out more widely. Use the winning messaging, imagery, timing, or other elements that proved themselves in your test in future campaigns. This optimization can lead to significant lifts in open rates, click-throughs, and conversions across your program.

Brainstorm Additional Optimization Opportunities

Your A/B test results likely revealed insights beyond just a singular winner. Look at the metrics across variations - even the underperformers - and analyze why certain messages resonated better. What other ideas does this spark for ways you could improve your campaigns? Keep testing and optimizing across different variables.

Continuously Improve Campaigns Over Time

A/B testing is an ongoing process. Continue to challenge your assumptions and test new ideas. Variations that win this month may lose their edge as subscriber preferences change. Keep innovating with your messaging and strategy. Optimization is never "one and done."

A/B Testing Tips and Best Practices

When running A/B tests, it's important to follow some best practices to get accurate, actionable results. Here are some top tips for effective SMS A/B testing:

• Limit the number of variables changed between variations - Only test one or two variables at a time. If you change too many things, you won't know which one impacted the results.

• Test one thing at a time - Split testing multiple variables simultaneously makes results hard to analyze. Focus on one hypothesis per test.

• Prioritize high-impact tests first - Start by testing content and messaging that is likely to have the biggest effect on metrics like open rate and conversions.

• Be patient and run tests long enough - Don't stop too soon before statistical significance is achieved. Give tests time to collect enough data to determine a winner.

• Test subject lines and calls-to-action - These high visibility elements often have significant impact on open rates and response.

• Vary message length - Test short vs. long SMS, number of messages, etc. Length can impact open rates.

• Personalize content - A/B test personalized messages with subscriber names vs. generic messages.

• Test incentives - Offering discounts, sales, contests etc. in one variation can lift response rates.

• Send at different times - Test morning vs. afternoon or different days of the week. Timing affects open rates.

• Measure relevant metrics - Ensure you select and analyze metrics aligned to campaign goals like clicks, conversions, etc.

Following best practices helps ensure your A/B tests yield actionable data to optimize future SMS campaigns.

Common A/B Tests for SMS Campaigns

When running A/B tests on your SMS marketing campaigns, there are several key elements you can test to optimize performance. Here are some of the most common variables to experiment with:

Message Copy

Test different versions of your core message copy. For example, try different lengths, tones of voice, levels of personalization, ways to highlight value propositions, etc. Pay attention to open rates to see which message copy resonates most.

Call-to-Action

Your SMS CTA directly impacts click-through rates and conversions. Try testing commands vs questions, urgent vs casual wording, specificity of the CTA, adding emoji, and any power words that convey value.

Timing

Send your SMS campaigns at different times of day or days of the week to see when your subscribers are most engaged. Weekday afternoons tend to have higher open rates. You can also experiment with timing between messages in an automated series.

Frequency

Test sending campaigns with different frequencies, such as every day vs every other day vs once a week. Look at open and click fatigue to find the right balance.

Emoji Usage

Emoji can make your SMS campaigns more fun and attention-grabbing. Test having no emoji, moderate emoji usage, or high emoji usage. Track open and click rates to gauge effectiveness.

Personalization

Personalized SMS campaigns have higher open rates. A/B test first name only, full name, custom attributes like hometown or purchase history, and dynamic content to determine the ideal personalization.

A/B Testing Mistakes to Avoid

When setting up and running SMS A/B tests, it's important to avoid some common pitfalls that can lead to inaccurate or misleading results:

Changing too many variables - Only test one element at a time in order to isolate the impact of each change. Testing multiple variables simultaneously makes it difficult to determine which one impacted the results.

Small sample sizes - Splitting your audience into too many small test groups reduces statistical significance. Have at least a few hundred subscribers in each test segment for meaningful data.

Short durations - Give your A/B test enough time for results to emerge from the noise. Let it run for at least 1 week, ideally 2 weeks or more. Ending too early may not surface the true winner.

Not testing statistically significant results - Just because one variation performed slightly better doesn't mean the difference is statistically significant. Use a significance calculator to validate a real difference.

Not optimizing winning variation - Don't leave money on the table. Roll out the top performing variation to your entire subscriber base to maximize results moving forward.