Email Personalization Playbook

PERSONALIZATION STRATEGY

Personalization Maturity Model

Level	Description	Examples	Requirements
Level 0: None	Same email to everyone	Batch and blast	None
Level 1: Basic	Name/company insertion	"Hi [Name]"	CRM data
Level 2: Segmented	Content varies by segment	Industry-specific content	Segment logic
Level 3: Behavioral	Triggered by actions	Abandoned cart	Event tracking
Level 4: Predictive	AI-driven content	Next best action	ML models

Personalization ROI Calculator

PERSONALIZATION IMPACT ASSESSMENT

BASELINE (No personalization):
Current open rate: ____%
Current click rate: ____%
Current conversion rate: ____%
Monthly conversions: _____
Revenue per conversion: $_____

EXPECTED LIFT BY LEVEL:
Level 1 (Basic): +5-10% engagement
Level 2 (Segmented): +15-25% engagement
Level 3 (Behavioral): +25-40% engagement
Level 4 (Predictive): +40-60% engagement

PROJECTED IMPACT:
Target level: _____
Expected engagement lift: ___%
Projected additional conversions: _____
Projected additional revenue: $_____/month

INVESTMENT REQUIRED:
Implementation effort: _____
Technology needed: _____
Ongoing maintenance: _____

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PERSONALIZATION TYPES

Data-Driven Personalization Matrix

Data Type	Personalization Use	Example
Name	Greeting, subject line	"Hi Sarah"
Company	Relevance, credibility	"For teams like [Company]"
Industry	Content, examples	Industry-specific case study
Role/Title	Messaging angle	Technical vs. business content
Company size	Offer, pricing	Enterprise vs. SMB offer
Location	Events, timing	Local event invitation
Previous content	Recommendations	"Since you read X, you'll love Y"
Product usage	Tips, upsell	Feature-specific guidance

Behavioral Personalization Triggers

BEHAVIORAL TRIGGER MATRIX

WEBSITE BEHAVIOR:
Trigger: Visited pricing page (2x)
Personalization: Bottom-funnel content, demo offer
Email: "Ready to see pricing for [Company]?"

Trigger: Downloaded case study
Personalization: Related success stories
Email: "More results like [Downloaded Case Study]"

Trigger: Viewed product page
Personalization: Feature-focused content
Email: "More about [Feature they viewed]"

PRODUCT BEHAVIOR:
Trigger: Used feature X for first time
Personalization: Tips to get more value
Email: "Getting started with [Feature]"

Trigger: Hasn't logged in 7 days
Personalization: Re-engagement with value
Email: "What you missed this week"

Trigger: High usage pattern
Personalization: Upgrade opportunity
Email: "You're getting great results—here's how to do more"

Dynamic Content Blocks

Block Type	Logic	Content Variations
Hero image	Industry/persona	3-5 industry-specific images
Case study	Industry match	Relevant customer story
CTA	Lifecycle stage	Demo/Trial/Learn More
Feature highlight	Usage/interest	Feature not yet used
Social proof	Company size	SMB vs. Enterprise logos

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IMPLEMENTATION GUIDE

Data Requirements Checklist

PERSONALIZATION DATA AUDIT

BASIC PERSONALIZATION DATA:
☐ First name (___% coverage)
☐ Last name (___% coverage)
☐ Company name (___% coverage)
☐ Email address (100% coverage)

ENRICHMENT DATA:
☐ Job title (___% coverage)
☐ Industry (___% coverage)
☐ Company size (___% coverage)
☐ Location (___% coverage)

BEHAVIORAL DATA:
☐ Website visits tracked
☐ Content downloads tracked
☐ Email engagement tracked
☐ Product usage tracked

DATA GAPS TO FILL:
1. _______________
2. _______________
3. _______________

Fallback Strategy

Personalization	Fallback When Missing	Example
First name	"there" or "friend"	"Hi there"
Company name	Generic reference	"your company"
Industry	Remove block	Hide industry-specific content
Recent content	Show popular content	"Our most-read guide"

Dynamic Content Setup Template

DYNAMIC CONTENT BLOCK

BLOCK NAME: _______________
BLOCK LOCATION: _______________

RULE 1:
If [condition]: _______________
Show: _______________

RULE 2:
If [condition]: _______________
Show: _______________

RULE 3:
If [condition]: _______________
Show: _______________

DEFAULT:
If no conditions match, show: _______________

TESTING:
☐ Rule 1 tested with sample record
☐ Rule 2 tested with sample record
☐ Rule 3 tested with sample record
☐ Default tested with empty data

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PRIVACY & BEST PRACTICES

Personalization Guardrails

PERSONALIZATION ETHICS CHECKLIST

DO:
✓ Use data they knowingly provided
✓ Use data from their direct interactions
✓ Provide value with personalization
✓ Be transparent about data use
✓ Allow preference management

DON'T:
✗ Use data that feels "creepy"
✗ Reference third-party data sources
✗ Over-personalize (feels surveillance-y)
✗ Reference sensitive personal information
✗ Use personalization that highlights mistakes

CREEPY VS. HELPFUL:
HELPFUL: "Based on your interest in [topic they engaged with]"
CREEPY: "Since you've visited our pricing page 7 times"

HELPFUL: "For [Industry] companies like yours"
CREEPY: "Based on your LinkedIn profile"

HELPFUL: "You might like this based on [past download]"
CREEPY: "We noticed you were looking at competitors"

Personalization Testing Protocol

Test	Method	Success Metric
Accuracy	Manual spot-check 100 records	>95% correct
Fallbacks	Test with empty data	Graceful degradation
Edge cases	Test unusual values	No errors/breaks
Performance	A/B test personalized vs. generic	Lift in engagement

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MEASUREMENT

Personalization Impact Dashboard

PERSONALIZATION PERFORMANCE REPORT

PERIOD: _______________

IMPLEMENTATION STATUS:
☐ Basic (name/company): Active
☐ Segmented (industry/size): Active/Inactive
☐ Behavioral (triggers): Active/Inactive
☐ Dynamic content: Active/Inactive

PERFORMANCE BY TYPE:
                    Personalized    Generic    Lift
Open rate:          ____%          ____%      +/-____%
Click rate:         ____%          ____%      +/-____%
Conversion rate:    ____%          ____%      +/-____%

TOP PERFORMING PERSONALIZATION:
1. _______________ (+__% lift)
2. _______________ (+__% lift)
3. _______________ (+__% lift)

NEXT OPTIMIZATIONS:
1. _______________
2. _______________

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