Email Personalisation · Dynamic Content, Merge Tags & AI

Merge tags (also called personalisation tokens or custom fields) insert subscriber-specific data into email content. Syntax varies by platform — Mailchimp uses *|FNAME|*; Klaviyo uses {{ first_name }}; HubSpot uses {{ contact.firstname }} — but the concept is universal.

Common merge tag applications

Subject: {{ first_name }}, your cart is waiting
Body: Hi {{ first_name }},
We noticed you left {{ product_name }} in your cart.
Your order from {{ last_order_date }} was {{ last_order_value }}.
Based in {{ city }}? Free next-day delivery available.

Merge tag best practices

• Always set a fallback value. If the first name field is empty, the email sends "Hi ," — embarrassing and trust-damaging. Set fallback: {{ first_name | default: "there" }} sends "Hi there" for missing names.
• First name only — not full name. "Hi Jane" feels personal; "Hi Jane Smith" feels clinical and data-aware.
• Do not over-personalise. Referencing too many personal details in a single email can feel surveillant rather than helpful. Use personalisation where it adds genuine value, not as a demonstration of data capability.

Dynamic content blocks display different content to different subscribers within the same email — based on segment membership, subscriber attributes, or behavioural data. One campaign send; multiple versions.

Dynamic content use cases

• Gender-specific product images. An apparel retailer sends one campaign showing men's products to male subscribers and women's products to female subscribers — same send, different hero image and product blocks.
• Location-specific content. A national retailer shows store-specific offers and hours based on the subscriber's city in their profile.
• Customer tier content. VIP customers see an exclusive early-access offer block; standard customers see a regular promotional offer block — same email template, different offer.
• Purchase history-based recommendations. Show different cross-sell products based on what each subscriber has previously purchased.
• Lifecycle stage messaging. New subscribers see a "getting started" CTA; returning customers see a "what's new" CTA — same newsletter template, different CTA block.

Behavioural personalisation sends emails triggered by or tailored to specific actions subscribers have taken. Unlike merge tags (static profile data), behavioural personalisation responds to real-time actions:

• Browse abandonment. Subscriber visited a product category but did not add to cart — email them content about that category within 24 hours
• Content engagement follow-up. Subscriber clicked on a link about a specific topic in a newsletter — follow up with more depth on that topic in the next email
• Feature usage in SaaS. User activated a feature — trigger an educational sequence about advanced uses of that feature
• Inactivity triggers. Subscriber has not logged in for 14 days — trigger a re-engagement email with usage tips or a new feature highlight

Product recommendations in email use purchase history and browsing data to suggest products each subscriber is most likely to be interested in. Implementation approaches range from simple to sophisticated:

• Rule-based recommendations. "If subscriber purchased product in category X, show other products in category X." Simple, effective for clear category-based catalogues.
• Collaborative filtering. "Customers who bought what you bought also bought Y." Uses purchase patterns across your customer base to surface relevant cross-sell products.
• Predictive engines. Machine learning models that consider purchase history, browse history, email engagement, and similar customer profiles to predict the most likely next purchase for each individual subscriber. Available as ESP integrations (Klaviyo's product recommendations feature, for example) or standalone recommendation engines.

AI personalisation in email marketing in 2026 operates at several levels that were not possible with rule-based systems:

• Send time optimisation. AI predicts the optimal send time per subscriber based on their historical open and click patterns — sending each subscriber's email at the moment they are most likely to engage, rather than a single bulk send time for the whole list.
• Subject line personalisation. AI selects from multiple subject line variants the one each subscriber is most likely to respond to, based on their historical preferences for question vs statement, emoji vs no emoji, length, etc.
• Content selection. AI selects which content blocks to show each subscriber based on predicted relevance — from a library of approved content options, it assembles each subscriber's email uniquely.
• Generative copy variations. AI generates multiple versions of body copy — same message, different tone, length, or framing — and serves each subscriber the version predicted to resonate best.

Before scaling any personalisation tactic, test it against a control (the non-personalised version) to confirm it actually improves performance. Common personalisation testing approaches:

• A/B test: personalised subject line vs generic subject line — does first name in subject improve open rate for your audience?
• A/B test: dynamic product recommendations vs editorial curated products — which drives higher CTR and revenue?
• A/B test: send time optimisation vs fixed send time — does per-subscriber timing improve engagement metrics?

Not all personalisation delivers measurable improvement — particularly basic name personalisation in subject lines, where results are mixed in the research. Measure before scaling.

Authentic Sources