Definition
A recommendation engine in email marketing is a system that analyses subscriber data — purchase history, browsing behaviour, engagement patterns, and demographic information — to predict what products, content, or offers are most relevant to each individual and dynamically inserts those recommendations into emails.
Recommendation engines power the "You might also like," "Customers who bought this also bought," and "Based on your browsing history" sections common in ecommerce and content marketing emails.
Types of Recommendation Engines
| Type | How It Works | Best For |
|---|---|---|
| Collaborative filtering | "People like you also liked..." | Ecommerce, broad catalogues |
| Content-based filtering | "More of what you've liked before" | Content, media, publishing |
| Hybrid | Combines collaborative and content-based | Most robust performance |
| Rule-based | Manual rules (category, price, brand) | Simple catalogues, control |
| Real-time | Recommendations generated at open | Personalised at moment of view |
| Predictive | ML models predicting next best action | Advanced personalization |
Data Requirements
| Data Type | Importance | Source |
|---|---|---|
| Purchase history | Critical | Transaction system |
| Browse history | High | Analytics/website tracking |
| Product catalogue | Required | Product database |
| Subscriber demographics | Medium | CRM or signup data |
| Engagement history | Medium | ESP click/open data |
| Real-time context | Low (optimisation) | Location, device, time |
Implementation in Email
- Product recommendation blocks: Dynamic content sections in email templates
- Post-purchase cross-sells: "Complete the look" or "Frequently bought together"
- Browse abandonment: "Still interested?" with recently viewed items
- Content recommendations: "Based on what you read" for publishers
- Personalised offers: Individual discount levels based on predicted sensitivity
Measuring Recommendation Performance
- Recommendation click rate: % of clicks on recommended items
- Recommendation conversion rate: % of recommendation clicks that convert
- Revenue per recommendation email: Total revenue / number of recommendation emails sent
- Lift vs control: Improvement over non-personalised equivalent
- Coverage: % of sent emails that contained personalised recommendations
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
For basic collaborative filtering, you need at least 100-500 interactions per product and a catalogue of 100+ items. For content-based filtering, detailed product/content metadata (category, tags, attributes) can compensate for sparse interaction data.
Yes. Many ESPs (Klaviyo, Mailchimp, ActiveCampaign) include built-in product recommendation features that use your transaction data without requiring a separate engine. For very small catalogues (under 50 products), rule-based recommendations often perform as well as algorithmic ones.
The cold start problem occurs when a recommendation engine has insufficient data about a new subscriber or new product to make accurate predictions. Solutions include using popularity-based fallbacks (best sellers), demographic-based recommendations, or collecting preference data during signup.