Geo-Segmentation: Segment your list by time zone
Posted: Tue May 20, 2025 9:09 am
Your ESP should allow you to send emails at a specific local time for each segment.
"Send Time Optimization" Features: Many advanced ESPs offer features that automatically send to each recipient at their individual optimal time based on their past engagement patterns. This leverages predictive analytics.
d) A/B Testing for Send Time (The Gold Standard)
Historical analysis provides a baseline, but true optimization comes from systematic A/B testing.
Hypothesis Formulation: Based on your initial analysis, form a hypothesis (e.g., "Sending on Tuesday at 10 AM will generate a higher open rate than sending on Wednesday at 2 PM for our 'New Arrivals' segment.")
Test Groups: Divide a segment of your audience into two or more equal groups.
Vary Send Time: Send identical emails to each switzerland email list group, but at different times.
Measure Key Metrics: Track open rates, CTRs, and conversions for each test group.
Statistical Significance: Ensure your results are statistically significant before drawing conclusions. A small difference might be due to chance.
Iterate: Continuously test different times, days, and segments. Email performance is dynamic.
Example A/B Test Scenario:
Segment: Engaged customers (opened 3+ emails in the last 3 months).
Hypothesis: Sending our weekly newsletter on Monday at 9:00 AM BDT will perform better than Tuesday at 1:00 PM BDT.
Test:
Group A (50%): Receives newsletter Monday at 9:00 AM BDT.
Group B (50%): Receives newsletter Tuesday at 1:00 PM BDT.
Measure: Open Rate, CTR, Conversions (e.g., recipe views).
Analysis: Compare metrics after a suitable period (e.g., 24-48 hours).
4. Leveraging Advanced Send Time Optimization Features
Many top-tier ESPs and MAPs now offer sophisticated "send time optimization" (STO) features that go beyond simple time zone adjustments.
AI/Machine Learning Driven STO: These features analyze the historical engagement patterns of each individual subscriber. They learn when a specific person is most likely to open and click an email and then automatically deliver the email at that custom optimal time. This is the pinnacle of send time personalization.
Predictive Analytics: Beyond historical trends, some platforms use predictive models to forecast future optimal times based on various factors.
Journey-Based Optimization: Within automated customer journeys, send time can be optimized for each step of the journey, ensuring the next email is sent at the most impactful moment after a previous interaction.
Implementing Send Time Optimization
Once you've gathered insights, implementation involves adjusting your sending strategy.
1. Scheduling Campaigns
Manual Scheduling: Based on your A/B test results and historical data, manually schedule your campaigns for the identified optimal times for each segment.
Segment-Specific Scheduling: If you have multiple segments with different optimal times, schedule separate sends for each.
Local Time Sending: Ensure your ESP allows you to schedule based on the recipient's local time zone, not just your company's time zone.
2. Automation Workflows
Triggered Emails: For emails like abandoned cart reminders or welcome series, the send time is often immediately after a specific action. However, even here, you might experiment with a slight delay if data suggests it's more effective (e.g., 30 minutes vs. 1 hour for abandoned carts).
Drip Campaigns/Nurture Sequences: Incorporate optimal send times for each step in a multi-email sequence. If a user opens an email on Tuesday at 10 AM, perhaps the next email in the sequence should be sent at a similar time on a subsequent day.
"Send Time Optimization" Features: Many advanced ESPs offer features that automatically send to each recipient at their individual optimal time based on their past engagement patterns. This leverages predictive analytics.
d) A/B Testing for Send Time (The Gold Standard)
Historical analysis provides a baseline, but true optimization comes from systematic A/B testing.
Hypothesis Formulation: Based on your initial analysis, form a hypothesis (e.g., "Sending on Tuesday at 10 AM will generate a higher open rate than sending on Wednesday at 2 PM for our 'New Arrivals' segment.")
Test Groups: Divide a segment of your audience into two or more equal groups.
Vary Send Time: Send identical emails to each switzerland email list group, but at different times.
Measure Key Metrics: Track open rates, CTRs, and conversions for each test group.
Statistical Significance: Ensure your results are statistically significant before drawing conclusions. A small difference might be due to chance.
Iterate: Continuously test different times, days, and segments. Email performance is dynamic.
Example A/B Test Scenario:
Segment: Engaged customers (opened 3+ emails in the last 3 months).
Hypothesis: Sending our weekly newsletter on Monday at 9:00 AM BDT will perform better than Tuesday at 1:00 PM BDT.
Test:
Group A (50%): Receives newsletter Monday at 9:00 AM BDT.
Group B (50%): Receives newsletter Tuesday at 1:00 PM BDT.
Measure: Open Rate, CTR, Conversions (e.g., recipe views).
Analysis: Compare metrics after a suitable period (e.g., 24-48 hours).
4. Leveraging Advanced Send Time Optimization Features
Many top-tier ESPs and MAPs now offer sophisticated "send time optimization" (STO) features that go beyond simple time zone adjustments.
AI/Machine Learning Driven STO: These features analyze the historical engagement patterns of each individual subscriber. They learn when a specific person is most likely to open and click an email and then automatically deliver the email at that custom optimal time. This is the pinnacle of send time personalization.
Predictive Analytics: Beyond historical trends, some platforms use predictive models to forecast future optimal times based on various factors.
Journey-Based Optimization: Within automated customer journeys, send time can be optimized for each step of the journey, ensuring the next email is sent at the most impactful moment after a previous interaction.
Implementing Send Time Optimization
Once you've gathered insights, implementation involves adjusting your sending strategy.
1. Scheduling Campaigns
Manual Scheduling: Based on your A/B test results and historical data, manually schedule your campaigns for the identified optimal times for each segment.
Segment-Specific Scheduling: If you have multiple segments with different optimal times, schedule separate sends for each.
Local Time Sending: Ensure your ESP allows you to schedule based on the recipient's local time zone, not just your company's time zone.
2. Automation Workflows
Triggered Emails: For emails like abandoned cart reminders or welcome series, the send time is often immediately after a specific action. However, even here, you might experiment with a slight delay if data suggests it's more effective (e.g., 30 minutes vs. 1 hour for abandoned carts).
Drip Campaigns/Nurture Sequences: Incorporate optimal send times for each step in a multi-email sequence. If a user opens an email on Tuesday at 10 AM, perhaps the next email in the sequence should be sent at a similar time on a subsequent day.