A/B testing is a great way to optimize your outreach campaigns, ensuring you use the best-performing messaging to increase engagement and conversions.
This guide will show you how to run an A/B test manually by duplicating a campaign, introducing a variable, and moving prospects.
Steps to Run a Manual A/B Test in Outreachly
1. Duplicate the Campaign
In your Outreachly dashboard, you can find the campaign you want to test.
Click Duplicate to create an exact copy of the campaign.
Name the duplicated campaign clearly, like "Campaign A - Subject Line 1" and "Campaign B - Subject Line 2."
2. Add Your Variable
Decide what one element you want to test—this could be:
✅ Subject line
✅ Email body content
✅ Call-to-action
✅ Sending time
✅ Personalization style
Update only one variable in Campaign B to accurately measure its impact.
3. Move Prospects Between Campaigns
Add your data to one campaign and then split your prospect data evenly and randomly between Campaign A and Campaign B.
For a fair comparison, ensure each campaign has a similar mix of lead types, industries, or engagement history.
Follow this Guide on Moving Prospects.
4. Launch the Campaigns
Start both campaigns at the same time to maintain consistency in results.
Monitor key metrics such as open rates, reply rates, and conversion rates over a set period.
5. Analyze the Results
After a set period (e.g., one week), compare Campaign A and B based on:
Open rates (which subject line performed better?)
Reply rates (which email sparked more responses?)
Click-through rates (if links were included)
Conversion rates (did one version lead to more booked meetings or sales?)
You can use the winning version for future campaigns to improve outreach effectiveness.
🚀 Success! By running manual A/B tests, you can refine your outreach strategy, boost engagement, and maximize results in every campaign. Try it today and start optimizing like a pro!
Frequently Asked Questions (FAQ):
Q: Can I test multiple variables at once?
A: For accurate results, test one variable at a time. It’s hard to know which change made the difference if you test more than one.
Q: How long should I run the test?
A: A good rule of thumb until you have a statistically significant number of responses. This is likely more than you think!
Q: What if both versions perform the same?
A: If there’s no clear winner, test a new variable or try a different approach - minor tweaks can make a big difference over time!
Q: What does the A/B Test Condition do in the Smart Campaigns?
A: This condition only splits the data into 2 different routes. Limited reporting on the outcome is available, so we recommend duplicating the campaign as the best way to conduct an A/B test.