What all you can test with A/B testing?
Created by: sudip samaddar
Modified on: Thu, 28 Jun, 2018 at 1:44 PM
Put 150 prospects through per step.
- Keep track of the volume of prospects you reach out to when A/B testing in your campaign. As prospects move through a campaign steps with an A/B test in it, Imaginesales Technologies randomly (but evenly) sends out a template to these prospects. For example: In an A/B test using 2 templates, your data will become valuable after at least 300 prospects have passed through the step. Any less than this will not allow you to see enough relevant data to make an informed decision on the quality of the message.
A/B test where you want to improve
- Because of how little effort goes into creating A/B tests for various message types, we recommend customers do it often and across all types of campaigns. Constant testing and refining will lead to a deep understanding of your customers and the best language to address them going forward.
Test either the open rate OR reply rate, but NOT both at the same time when testing email.
- You should only test open rate OR reply rate at one time to ensure you are getting the purest results.
- The open rate of your email will depend with the subject line. We recommend starting by A/B testing the open rate of your first email by changing the subject lines and leaving the body of the email the same. The reply rate will correspond with the body of the email.
Test Email Vs Call.
- Are you getting more engagement from emails or from calls? For this put 300 data in a campaign. Keep one of the steps as call and the other as email. Now run the steps for 150 data for each step. Now see the system analytics. It will tell you which outreach, call or email is working better.
Test Call Script A Vs Call Script B.
- Which call script has more positive response? Test that. Run the two steps for 150 leads. Check the call answered. Both the steps should have same number of answered. Now check for which call script the number of positive conversation is high.
Don't test too many templates at once
- If you are adding too many templates to your A/B test, it will take much longer to get valuable data.
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