How to Send SMS to B2B Customers and Keep It Professional
Learn how to send SMS to B2B customers, why it works, and the different campaign types, and see…
A/B testing is a simple method for comparing two versions of an SMS message to see which one performs better.
It usually involves sending Version A to one group of contacts and Version B to another group at the same time.
Each version might change a single element, such as wording, length, link placement, or send time.
The system tracks metrics like open rate, click-through rate, replies, and opt-outs to measure how each version performs.
It relies on accurate contact data, delivery reports, and engagement data collected by your SMS platform.
Using this information, the platform identifies the variant that produces stronger results and applies it to future sends.
This process helps refine tone, timing, and content so recipients experience clearer, more relevant, and less intrusive messages.
of texts are read immediately
of consumers want to text businesses
of consumers said they have tried to text a business
A/B testing in SMS works best when each experiment is tied to a real business scenario, such as refining a reminder template or improving a recurring promotion.
Start by deciding exactly what you want to learn, for example whether a friendlier greeting improves replies to appointment reminders or whether a shorter link description reduces confusion.
Keep each test simple by changing only one element at a time so that any difference in performance clearly relates to that change, not to a mix of unrelated edits.
Maintain a consistent brand tone in both versions so the comparison focuses on clarity and structure rather than two completely different personalities.
Make sure your contact data, opt in status, and timestamps are accurate before running tests, since poor data can distort open and response patterns.
Avoid stopping a test too early or declaring a winner based on a very small send, as this often produces misleading conclusions that weaken future decisions.
Document each test, result, and follow up change so your team can apply lessons consistently instead of repeating the same experiments.



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