Definition
Personalised recommendations in email marketing dynamically surface products, articles, or offers tailored to each individual subscriber based on their unique behaviour and attributes. Instead of sending the same content to everyone, recommendation engines analyse data to determine what each person is most likely to be interested in.
This approach significantly improves click-through rates, conversion rates, and revenue per email because subscribers receive offers that match their demonstrated interests.
Types of Recommendations
| Recommendation Type | Logic | Example |
|---|---|---|
| Collaborative Filtering | People like you also liked | "Customers who bought X also bought Y" |
| Content-Based Filtering | Similar to what you viewed | "More items from this category" |
| Trending / Popular | What others are engaging with | "Best sellers this week" |
| Recently Viewed | Based on browsing history | "You recently viewed" |
| Complementary | Items that go together | "Complete the look" |
| Replenishment | Previously purchased consumables | "Time to reorder" |
How to Implement
- Collect behavioural data: Track product views, purchases, wishlist adds, and content clicks
- Choose a recommendation engine: Many email platforms have built-in recommendation blocks. For advanced needs, use dedicated tools like Nosto, Releva, or Dynamic Yield
- Set fallback rules: Define what to show when there is insufficient data for personalisation (e.g. best sellers or new arrivals)
- Test recommendation placement: Above-the-fold recommendations often outperform footer placements
- Measure recommendation attribution: Track which revenue is directly attributable to recommended products
Impact on Metrics
Emails with personalised recommendations typically see:
- 30-50% higher click-through rates compared to non-personalised emails
- 20-40% higher conversion rates
- 15-30% higher revenue per email
- Lower unsubscribe rates due to increased relevance
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
Effective recommendations can start with basic data like browsing history and past purchases. As more behavioural data accumulates, recommendations become more accurate. Even new subscribers can receive popular-item recommendations as a fallback.
Yes. Content publishers can recommend articles, videos, or guides based on reading history. Media and education companies use content-based recommendation engines to increase engagement and time spent.
Segmentation groups subscribers into broad categories (e.g. "women's clothing buyers"). Personalised recommendations go further by showing unique products to each individual within that segment based on their specific browsing and purchase history.