Summary
Determining the optimal duration and statistical significance for email A/B tests, especially for subject lines, is crucial for accurate insights. While specific recommendations vary, a common best practice is to run tests for at least 24 hours, and often 2-3 days, to capture diverse recipient behaviors across the week. For robust results, it's generally advised to aim for 90% to 95% statistical significance, ensuring the observed differences are reliable and not due to chance. The duration of the test also depends heavily on audience size and the volume of engagement, with larger lists achieving significance faster. Ultimately, prioritizing sufficient data collection and reaching a desired confidence level should dictate when to conclude an A/B test rather than strictly adhering to a fixed time frame.
Key findings
- Typical Test Duration: Email A/B tests for subject lines commonly run for a minimum of 24 hours, often extending to 2-3 days, and sometimes up to 7 days to eliminate day-of-week biases and allow ample time for all subscribers to open.
- Statistical Significance: The recommended statistical significance level for subject line A/B tests is generally 90% to 95%, with 95% being the most robust. Some sources suggest 80% as a default for subject line tests or for more frequent, internal emails.
- Required Opens: To achieve reliable results, each subject line variation should aim for at least 100-200 opens, with some experts recommending 500-1000 opens per variant, particularly for larger audiences.
- Meaningful Lift: For test results to be considered meaningful, aim for a lift, or improvement, of better than 3%. A lift lower than 3% is often considered negligible in email marketing.
- Audience Size Impact: The necessary test duration is heavily influenced by audience size. Larger email lists may reach statistical significance in just a few hours, while smaller lists might require 1-3 days or even a full week to gather sufficient data.
Key considerations
- Full Engagement Cycle: Run tests for at least 24 hours, and often 2-3 days, to capture varied recipient behaviors across different times of day and days of the week. This avoids skewing results to immediate openers and accounts for users who engage with email later.
- Data Volume Over Time: Prioritize achieving sufficient data volume, or opens, to reach statistical significance rather than adhering to a fixed time frame. Larger lists might conclude tests in hours, while smaller lists may need several days or up to a week.
- Using Calculators: Employ a statistical significance calculator to determine if test results are reliable. This helps ensure the observed differences are not due to random chance.
- Contextual Significance: Adjust the target statistical significance level based on the email's importance. While 95% is ideal for critical campaigns, 90% is often acceptable for subject lines in email marketing, and 80-85% might suffice for frequent or internal communications, allowing for quicker iteration.
- Traffic Distribution: For automated campaigns, spread traffic over every hour of every day to track efficacy and gather comprehensive data over a longer period, such as a month, with in-depth analysis around the third week.
- Avoiding Stale Tests: Be cautious of running tests for too long, as the relevance of the test might decrease over extended periods. Balance the need for sufficient data with the timeliness of the content.
What email marketers say
11 marketer opinions
When running A/B tests for email subject lines, a primary goal is to gather sufficient data to confidently identify a winner. While some initial insights might emerge within a few hours, experts widely recommend a minimum test duration of 24 to 48 hours to adequately capture diverse recipient behaviors, as users open emails at varying times throughout the day and week. For larger lists or more comprehensive results, extending the test to 2-4 days, or even a full week, helps to mitigate day-of-week biases and ensure a broader representation of engagement patterns. Crucially, tests should continue until statistical significance is achieved, which commonly ranges from 90% to 95%. While 95% is considered ideal for high-stakes campaigns, a 90% confidence level is often deemed acceptable for frequent subject line testing in email marketing, and even 80-85% might be suitable for internal or less critical communications, allowing for faster iteration.
Key opinions
- Optimal Duration: Most recommendations suggest running subject line A/B tests for at least 24-48 hours to account for varied recipient behaviors, with some advocating for 2-7 days for more comprehensive data collection and to mitigate day-of-week biases.
- Statistical Significance Targets: Aim for 90-95% statistical significance, with 95% being the gold standard for critical campaigns. For more frequent or internal tests, 80-85% might be acceptable, balancing confidence with iteration speed.
- Data Volume Thresholds: Rather than just time, ensure each subject line variant receives a minimum of 100-200 opens, with some suggesting 500-1000 opens, to ensure sufficient data for reliable results.
- Adapting to List Size: Test duration can vary significantly based on list size, with larger lists potentially reaching significance within hours, while smaller lists may require several days to accumulate enough engagement.
Key considerations
- Capture Full Engagement Cycle: Extend test duration beyond immediate engagement, typically 24-72 hours, to include recipients who open emails at different times throughout the day and week, thus avoiding a skewed view of performance.
- Prioritize Data Volume: The primary driver for concluding a test should be the achievement of statistical significance, which relies on sufficient data volume, rather than a fixed time limit. Use sample size calculators to determine required interactions.
- Contextual Significance Levels: Adjust the required statistical significance based on the impact of the email. Higher stakes necessitate higher confidence (95%), while lower stakes may allow for quicker tests at slightly lower confidence (90% or even 80-85%).
- Avoid Over-Testing: While sufficient data is crucial, be mindful of running tests for too long, as the relevance of the test content might diminish over an extended period.
Marketer view
Email marketer from Email Geeks explains they wait for statistical significance when running A/B tests, which might take a couple of hours. She adds that if statistical significance isn't reached, it might be necessary to wait a full 24 hours or send the A/B test to a larger group.
20 Dec 2021 - Email Geeks
Marketer view
Email marketer from Email Geeks explains that a major challenge in determining A/B test duration is avoiding skewing results to a subset of email engagement behaviors. He notes that users open emails at different times, from immediately to in the evening, and it is important not to optimize solely for immediate openers but also not to ignore them.
11 Jul 2024 - Email Geeks
What the experts say
3 expert opinions
For email A/B tests, particularly for subject lines, experts emphasize reaching a high level of statistical significance, typically 90% or 95%, with 95% being a strong recommendation to ensure observed results are not due to chance. The duration of these tests should be flexible and dictated by the need to gather sufficient data to meet this confidence level, rather than a predetermined timeframe. While small email senders might require up to a week to accumulate enough data, automated campaigns may benefit from running for a month for comprehensive analysis. Additionally, a meaningful lift of over 3% is often cited as a crucial threshold for a winning subject line to be considered significant enough to act upon.
Key opinions
- Statistical Significance Target: A 95% statistical significance or confidence level is widely recommended for email A/B tests, meaning there's only a 5% chance results are due to random error. Some marketers also find 90% acceptable.
- Minimum Lift for Action: For test results to be considered trustworthy and actionable, aim for a lift, or improvement, greater than 3%, as smaller differences are often negligible.
- Duration Driven by Data: Test duration should be determined by the need to achieve a large enough sample size and statistical significance, not by a fixed time frame. This means tests could run for a few days to a week for smaller lists, or a month for automated campaigns.
Key considerations
- Leverage Statistical Calculators: Utilize a statistical significance calculator to confirm the reliability of A/B test outcomes, aiming for a 95% confidence level.
- Prioritize Sufficient Data: Focus on achieving an adequate sample size and statistical significance rather than adhering to a strict time limit for test duration. Tests should run until enough data is collected to confidently reach the desired confidence level.
- Evaluate Meaningful Lift: Beyond statistical significance, ensure the observed improvement, or lift, is substantial, with a lift better than 3% often considered a meaningful difference for action.
- Adapt Duration to Sender Size: Small email senders may need to run subject line A/B tests for up to a week to gather sufficient data to achieve statistical significance.
- Long-Term Automated Campaigns: For automated campaigns, consider running tests over a month, with in-depth analysis around the third week, spreading traffic over every hour of every day to track efficacy comprehensively.
- Option for Early Conclusion: If a clear winner emerges and statistical significance is met, consider calling the test early to serve the winning subject line to the remaining audience.
Expert view
Expert from Email Geeks recommends using a statistical significance calculator, aiming for 95% statistical significance and a lift better than 3% to trust test results, noting that anything less than a 3% lift is often negligible. For automated campaigns, he runs them over a month, checking numbers in the third week for an in-depth analysis of opens, specific link clicks, and opt-outs. He may call the test early to serve the winner to the remaining audience and spreads traffic over every hour of every day to track efficacy.
5 Jul 2023 - Email Geeks
Expert view
Expert from Spam Resource explains that for email A/B tests, including subject line tests, a 95% confidence level is generally recommended for statistical significance. This means there is only a 5% chance the observed results are due to random error. The test should run long enough to ensure a large enough sample size is achieved to hit this confidence level, rather than adhering to a fixed time frame.
26 Jun 2024 - Spam Resource
What the documentation says
4 technical articles
A successful email A/B test for subject lines balances sufficient run time with achieving a robust statistical confidence level. While a minimum of 24 hours is advised, many experts suggest 3-5 days, or even up to 7 days, to ensure diverse recipient behaviors across the week are captured. The ultimate duration, however, should be fluid, depending on the audience size - larger lists may conclude tests within hours, whereas smaller lists require more time to gather enough data. For declaring a subject line winner, a statistical significance of 90% is often recommended, though 95% is considered ideal for highest confidence, while some platforms may default to 80% for quicker iterations.
Key findings
- Flexible Duration: Email A/B tests for subject lines should run flexibly, from a few hours for large lists to 3-7 days for smaller ones, with 3-5 days often recommended to capture varied weekly engagement patterns.
- Significance Spectrum: For subject line winners, statistical significance typically falls between 80% and 95% confidence, with 90% and 95% being common recommendations for reliability, while 80% is a possible default for faster iterations.
- Automated Winner Detection: Some Email Service Providers automatically determine a winning subject line once enough data has been collected and statistical significance is achieved.
Key considerations
- Extend Test Period: To ensure comprehensive data, extend A/B tests for subject lines beyond immediate engagement, ideally running for 3-5 days to account for diverse recipient behaviors across an entire week.
- Audience Size Impacts Run Time: The size of your email list directly influences test duration; larger audiences may yield significant results in hours, while smaller lists require several days to gather sufficient data for reliable analysis.
- Select Appropriate Confidence: Align the required statistical significance level with the campaign's importance; opt for 95% for critical tests, 90% for general email marketing, and consider 80% for less critical, quicker iterations.
Technical article
Documentation from HubSpot Knowledge Base explains that email A/B tests should ideally run for 3-5 days to capture varying open behaviors throughout the week. For statistical significance, a 90% confidence level is generally recommended to declare a winner for email subject line A/B tests.
18 Jan 2023 - HubSpot Knowledge Base
Technical article
Documentation from Mailchimp Guides states that the duration of an email A/B test depends on the audience size, with larger lists potentially concluding in a few hours, while smaller lists may need 1-3 days. They recommend an 80% confidence level as a default for subject line tests, with options to increase it up to 95%.
16 Apr 2024 - Mailchimp Guides
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