xiaohongshu
xiaohongshu
email-marketing
Эксперт по email-маркетингу. Используй для email кампаний, автоматизации, сегментации, deliverability и A/B тестов.
Full skill instructions
Comprehensive expertise in email marketing strategy and execution.
# SPF Record
v=spf1 include:_spf.google.com include:sendgrid.net ~all
# DKIM Record
selector._domainkey.example.com IN TXT "v=DKIM1; k=rsa; p=MIGfMA0GCSqGSIb3..."
# DMARC Record
_dmarc.example.com IN TXT "v=DMARC1; p=quarantine; rua=mailto:[email protected]"
| Metric | Benchmark | Description |
|---|---|---|
| Open Rate | 20-25% | Unique opens / Delivered |
| Click Rate | 2-5% | Unique clicks / Delivered |
| Click-to-Open | 10-15% | Clicks / Opens |
| Unsubscribe Rate | <0.5% | Unsubscribes / Delivered |
| Bounce Rate | <2% | Bounces / Sent |
| Spam Complaints | <0.1% | Complaints / Delivered |
| Conversion Rate | Varies | Conversions / Clicks |
Behavioral Segmentation:
- Purchase history
- Email engagement
- Website activity
- Product preferences
- Cart abandonment
Demographic Segmentation:
- Location/timezone
- Job title/industry
- Company size
- Age/gender
Lifecycle Stages:
- New subscribers
- Active customers
- At-risk (declining engagement)
- Churned (re-activation target)
- VIP/high-value
Day 0 - Welcome Email:
trigger: subscription_confirmed
content: Brand introduction, expectations
cta: Complete profile
Day 2 - Value Email:
trigger: previous_opened OR time_delay
content: Top content, quick wins
cta: Explore resources
Day 5 - Social Proof:
trigger: time_delay
content: Customer stories, testimonials
cta: See case studies
Day 7 - Soft CTA:
trigger: time_delay
content: Product introduction
cta: Start free trial
Hour 1 - Reminder:
trigger: cart_abandoned
content: Items in cart reminder
cta: Complete purchase
Hour 24 - Urgency:
trigger: no_purchase
content: Items may sell out
cta: Secure your items
Hour 72 - Incentive:
trigger: no_purchase
content: Special discount offer
cta: Get 10% off
Subject Lines:
- Length (short vs long)
- Personalization
- Emojis
- Questions vs statements
- Urgency words
Content:
- Layout (single vs multi-column)
- Image count and placement
- CTA button color/text
- Copy length
- Personalization depth
Timing:
- Send day
- Send time
- Timezone optimization
import scipy.stats as stats
def calculate_significance(control_opens, control_sent,
variant_opens, variant_sent,
confidence=0.95):
"""Calculate if A/B test result is significant."""
control_rate = control_opens / control_sent
variant_rate = variant_opens / variant_sent
# Pooled proportion
pooled = (control_opens + variant_opens) / (control_sent + variant_sent)
# Standard error
se = (pooled * (1 - pooled) * (1/control_sent + 1/variant_sent)) ** 0.5
# Z-score
z = (variant_rate - control_rate) / se
# P-value
p_value = 2 * (1 - stats.norm.cdf(abs(z)))
return {
'control_rate': control_rate,
'variant_rate': variant_rate,
'lift': (variant_rate - control_rate) / control_rate * 100,
'p_value': p_value,
'significant': p_value < (1 - confidence)
}
def optimize_send_time(subscriber_data):
"""Analyze historical engagement to find optimal send times."""
engagement_by_hour = {}
for subscriber in subscriber_data:
local_time = convert_to_local(subscriber['open_time'],
subscriber['timezone'])
hour = local_time.hour
if hour not in engagement_by_hour:
engagement_by_hour[hour] = {'opens': 0, 'total': 0}
engagement_by_hour[hour]['opens'] += 1
engagement_by_hour[hour]['total'] += 1
# Calculate open rates by hour
for hour, data in engagement_by_hour.items():
data['rate'] = data['opens'] / data['total']
# Find best hours
sorted_hours = sorted(engagement_by_hour.items(),
key=lambda x: x[1]['rate'],
reverse=True)
return sorted_hours[:3] # Top 3 hours
-- Calculate subscriber engagement score
SELECT
subscriber_id,
email,
COUNT(CASE WHEN event_type = 'open' THEN 1 END) as opens_30d,
COUNT(CASE WHEN event_type = 'click' THEN 1 END) as clicks_30d,
MAX(event_date) as last_activity,
CASE
WHEN COUNT(CASE WHEN event_type = 'open' THEN 1 END) >= 5 THEN 'highly_engaged'
WHEN COUNT(CASE WHEN event_type = 'open' THEN 1 END) >= 2 THEN 'engaged'
WHEN COUNT(CASE WHEN event_type = 'open' THEN 1 END) >= 1 THEN 'somewhat_engaged'
ELSE 'unengaged'
END as engagement_tier
FROM email_events
WHERE event_date >= CURRENT_DATE - INTERVAL '30 days'
GROUP BY subscriber_id, email;
Re-engagement Campaign:
trigger: no_opens_60_days
sequence:
- Day 0: "We miss you" email
- Day 7: "Last chance" with offer
- Day 14: Final warning
action_after_sequence:
if: no_engagement
then: move_to_suppression_list
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