Definition
Personalization techniques are the practical methods and approaches used to customise email content for individual subscribers. These range from simple token-based personalization (inserting a first name) to complex, AI-driven techniques that select content, products, and offers based on individual behaviour and predictive models.
Each technique offers different levels of relevance and requires different investments in data, technology, and content creation.
Personalization Levels
| Level | Technique | Data Required | Complexity |
|---|---|---|---|
| Basic | Merge tags (name, company, location) | Profile fields | Low |
| Segmentation | Group-based content selection | Segment membership | Low |
| Behavioural | Content based on past actions | Click, purchase, browse data | Medium |
| Dynamic | Real-time content blocks | Engagement data, preferences | Medium |
| Predictive | AI-selected content | Historical behaviour, ML models | High |
| 1:1 | Individual-level customisation | Comprehensive profile, real-time data | Very High |
Common Techniques
- Merge tag personalization: Insert subscriber name, company, location into copy
- Dynamic content blocks: Show different content sections based on subscriber attributes
- Product recommendations: Display personalised product selections based on history
- Send time optimisation: Deliver emails when each subscriber is most likely to engage
- Behavioural triggers: Send emails based on specific subscriber actions
- Content affinity targeting: Send content matching demonstrated topic interests
- Personalised subject lines: Include subscriber data in subject lines
- Dynamic images: Generate personalised images at send time
Implementation Guide
- Start with data: Collect and organise subscriber data (stated and behavioural)
- Choose techniques: Select personalization methods that match your data and goals
- Build content variations: Create content blocks for different segments or conditions
- Test and measure: A/B test personalised vs non-personalized versions
- Iterate: Expand personalization as data quality and coverage improve
- Monitor: Watch for data quality issues that cause incorrect personalization
Best Practices
- Personalize based on behaviour, not just demographics — what people do matters more than who they are
- Test each personalization technique against a control to measure actual lift
- Avoid over-personalization that feels invasive rather than helpful
- Ensure data quality — incorrect personalization damages trust
- Start simple and add sophistication as data and capabilities grow
- Document personalization rules so the team understands how content is selected
Related Glossary Terms
A/B Testing
A/B testing in email marketing is the practice of sending two variations of an email to a small sample of your list to determine which version performs better before sending the winner to the remaining subscribers.
Abandoned Cart Email
An abandoned cart email is an automated message sent to customers who added items to their online shopping cart but left without completing the purchase. It is one of the highest-converting email types in ecommerce.
AIDA Model for Email
The AIDA model (Attention, Interest, Desire, Action) is a classic copywriting framework used to structure email campaigns that guide subscribers from awareness to conversion.
AMP for Email
AMP for Email is a Google-developed framework that allows email messages to include interactive elements like forms, carousels, accordions, and live content. It turns static emails into dynamic, interactive experiences directly inside the inbox.
Anchoring Effect in Email Marketing
The anchoring effect is a cognitive bias where the first piece of information presented (the anchor) influences subsequent decisions, used in email to frame pricing and value perception.
Announcement Email
An announcement email is a dedicated campaign that communicates a specific update, milestone, or change to subscribers, from product launches and feature releases to company news and events.
Frequently Asked Questions
Behavioural personalization — using past actions to determine what content to show next — consistently produces the highest engagement lift. Product recommendations based on browse or purchase history typically outperform demographic or location-based personalization.
Start with one or two techniques and add more as you validate their impact. Using too many techniques at once makes it difficult to isolate what is working. A good starting point is merge tags plus one behavioural personalization element (such as content based on past clicks).
Personalization improves results when it is based on accurate data and correctly implemented. It can hurt results when data is wrong (suggesting products the subscriber already bought), when it feels invasive (using data the subscriber did not knowingly share), or when personalization rules are poorly designed.