ChatGPT answers, unedited

A strong approach is to combine attribute-based personalization (who the customer is) with behavior-based personalization (what they recently did). This typically produces better engagement than relying on either alone.

Subject line personalization strategy

Create a subject line using multiple signals instead of just inserting the customer's name.

Examples:

• Name + interest: "Emma, new arrivals in your favorite collection"
• Loyalty status: "Gold members get first access today"
• Recent browsing: "Still thinking about these headphones?"
• Recent purchase: "Complete your setup with these accessories"
• Location: "Ready for cooler weather in Seattle?"

Use dynamic rules such as:

IF abandoned_cart
    Subject = "Your items are waiting"
ELSE IF browsed_category
    Subject = "New arrivals in {favorite_category}"
ELSE IF loyalty_tier = Gold
    Subject = "Exclusive early access for Gold members"
ELSE
    Subject = "Products we picked just for you"

Product recommendation strategy

Combine several recommendation models rather than relying on a single algorithm.

1. Recently viewed

• Show products the customer viewed but didn't purchase.

2. Similar products

• Recommend items with similar attributes (brand, style, category, price).

3. Frequently bought together

• Suggest complementary products based on historical purchase patterns.

4. Personalized ranking
Score each product using signals such as:

• affinity to category
• recent browsing
• purchase history
• inventory availability
• popularity
• profit margin
• business priorities

Example scoring function:

Recommendation Score =
0.40 × Category Affinity +
0.30 × Recent Activity +
0.15 × Purchase Similarity +
0.10 × Popularity +
0.05 × Business Priority

Customer attributes to use

Useful profile data includes:

• First name
• Age group (where appropriate and compliant)
• Gender preference (if collected)
• Geographic location
• Loyalty tier
• Preferred brands
• Favorite categories
• Average order value
• Purchase frequency
• Customer lifetime value

Recent activity signals

Behavioral signals often outperform static profile data:

• Products viewed
• Searches performed
• Items added to cart
• Cart abandonment
• Purchases
• Email opens
• Email clicks
• Website visits
• Time since last purchase
• Time since last visit

End-to-end example

Customer

• Gold member
• Favorite category: Running
• Viewed three pairs of shoes yesterday
• Purchased socks last month

Subject line

Alex, new running shoes picked just for you

Email content

1. Recently viewed running shoes
2. Similar running shoes in stock
3. Matching performance apparel
4. Running socks and accessories
5. Gold member discount banner

Best practices

• Use behavioral data from the last 7–30 days to keep recommendations relevant.
• Fall back to best sellers or trending products if recent activity is sparse.
• Limit recommendations to 4–8 high-confidence products rather than showing many lower-quality options.
• Continuously A/B test subject lines and recommendation strategies, measuring open rate, click-through rate, conversion rate, and revenue per email.
• Respect user privacy and consent preferences, and avoid using sensitive personal attributes for personalization unless appropriate, compliant, and explicitly permitted.

This hybrid approach—combining customer attributes with recent behavior—typically delivers more relevant subject lines and product recommendations while remaining resilient when either profile or activity data is limited.

A strong approach is to combine attribute-based personalization (who the customer is) with behavior-based personalization (what they recently did). This typically produces better engagement than relying on either alone.

Subject line personalization strategy

Create a subject line using multiple signals instead of just inserting the customer's name.

Examples:

• Name + interest: "Emma, new arrivals in your favorite collection"
• Loyalty status: "Gold members get first access today"
• Recent browsing: "Still thinking about these headphones?"
• Recent purchase: "Complete your setup with these accessories"
• Location: "Ready for cooler weather in Seattle?"

Use dynamic rules such as:

IF abandoned_cart
    Subject = "Your items are waiting"
ELSE IF browsed_category
    Subject = "New arrivals in {favorite_category}"
ELSE IF loyalty_tier = Gold
    Subject = "Exclusive early access for Gold members"
ELSE
    Subject = "Products we picked just for you"

Product recommendation strategy

Combine several recommendation models rather than relying on a single algorithm.

1. Recently viewed

• Show products the customer viewed but didn't purchase.

2. Similar products

• Recommend items with similar attributes (brand, style, category, price).

3. Frequently bought together

• Suggest complementary products based on historical purchase patterns.

4. Personalized ranking
Score each product using signals such as:

• affinity to category
• recent browsing
• purchase history
• inventory availability
• popularity
• profit margin
• business priorities

Example scoring function:

Recommendation Score =
0.40 × Category Affinity +
0.30 × Recent Activity +
0.15 × Purchase Similarity +
0.10 × Popularity +
0.05 × Business Priority

Customer attributes to use

Useful profile data includes:

• First name
• Age group (where appropriate and compliant)
• Gender preference (if collected)
• Geographic location
• Loyalty tier
• Preferred brands
• Favorite categories
• Average order value
• Purchase frequency
• Customer lifetime value

Recent activity signals

Behavioral signals often outperform static profile data:

• Products viewed
• Searches performed
• Items added to cart
• Cart abandonment
• Purchases
• Email opens
• Email clicks
• Website visits
• Time since last purchase
• Time since last visit

End-to-end example

Customer

• Gold member
• Favorite category: Running
• Viewed three pairs of shoes yesterday
• Purchased socks last month

Subject line

Alex, new running shoes picked just for you

Email content

1. Recently viewed running shoes
2. Similar running shoes in stock
3. Matching performance apparel
4. Running socks and accessories
5. Gold member discount banner

Best practices

• Use behavioral data from the last 7–30 days to keep recommendations relevant.
• Fall back to best sellers or trending products if recent activity is sparse.
• Limit recommendations to 4–8 high-confidence products rather than showing many lower-quality options.
• Continuously A/B test subject lines and recommendation strategies, measuring open rate, click-through rate, conversion rate, and revenue per email.
• Respect user privacy and consent preferences, and avoid using sensitive personal attributes for personalization unless appropriate, compliant, and explicitly permitted.

This hybrid approach—combining customer attributes with recent behavior—typically delivers more relevant subject lines and product recommendations while remaining resilient when either profile or activity data is limited.

A strong approach is to combine attribute-based personalization (who the customer is) with behavior-based personalization (what they recently did). This typically produces better engagement than relying on either alone.

Subject line personalization strategy

Create a subject line using multiple signals instead of just inserting the customer's name.

Examples:

• Name + interest: "Emma, new arrivals in your favorite collection"
• Loyalty status: "Gold members get first access today"
• Recent browsing: "Still thinking about these headphones?"
• Recent purchase: "Complete your setup with these accessories"
• Location: "Ready for cooler weather in Seattle?"

Use dynamic rules such as:

IF abandoned_cart
    Subject = "Your items are waiting"
ELSE IF browsed_category
    Subject = "New arrivals in {favorite_category}"
ELSE IF loyalty_tier = Gold
    Subject = "Exclusive early access for Gold members"
ELSE
    Subject = "Products we picked just for you"

Product recommendation strategy

Combine several recommendation models rather than relying on a single algorithm.

1. Recently viewed

• Show products the customer viewed but didn't purchase.

2. Similar products

• Recommend items with similar attributes (brand, style, category, price).

3. Frequently bought together

• Suggest complementary products based on historical purchase patterns.

4. Personalized ranking
Score each product using signals such as:

• affinity to category
• recent browsing
• purchase history
• inventory availability
• popularity
• profit margin
• business priorities

Example scoring function:

Recommendation Score =
0.40 × Category Affinity +
0.30 × Recent Activity +
0.15 × Purchase Similarity +
0.10 × Popularity +
0.05 × Business Priority

Customer attributes to use

Useful profile data includes:

• First name
• Age group (where appropriate and compliant)
• Gender preference (if collected)
• Geographic location
• Loyalty tier
• Preferred brands
• Favorite categories
• Average order value
• Purchase frequency
• Customer lifetime value

Recent activity signals

Behavioral signals often outperform static profile data:

• Products viewed
• Searches performed
• Items added to cart
• Cart abandonment
• Purchases
• Email opens
• Email clicks
• Website visits
• Time since last purchase
• Time since last visit

End-to-end example

Customer

• Gold member
• Favorite category: Running
• Viewed three pairs of shoes yesterday
• Purchased socks last month

Subject line

Alex, new running shoes picked just for you

Email content

1. Recently viewed running shoes
2. Similar running shoes in stock
3. Matching performance apparel
4. Running socks and accessories
5. Gold member discount banner

Best practices

• Use behavioral data from the last 7–30 days to keep recommendations relevant.
• Fall back to best sellers or trending products if recent activity is sparse.
• Limit recommendations to 4–8 high-confidence products rather than showing many lower-quality options.
• Continuously A/B test subject lines and recommendation strategies, measuring open rate, click-through rate, conversion rate, and revenue per email.
• Respect user privacy and consent preferences, and avoid using sensitive personal attributes for personalization unless appropriate, compliant, and explicitly permitted.

This hybrid approach—combining customer attributes with recent behavior—typically delivers more relevant subject lines and product recommendations while remaining resilient when either profile or activity data is limited.

A strong approach is to combine attribute-based personalization (who the customer is) with behavior-based personalization (what they recently did). This typically produces better engagement than relying on either alone.

Subject line personalization strategy

Create a subject line using multiple signals instead of just inserting the customer's name.

Examples:

• Name + interest: "Emma, new arrivals in your favorite collection"
• Loyalty status: "Gold members get first access today"
• Recent browsing: "Still thinking about these headphones?"
• Recent purchase: "Complete your setup with these accessories"
• Location: "Ready for cooler weather in Seattle?"

Use dynamic rules such as:

IF abandoned_cart
    Subject = "Your items are waiting"
ELSE IF browsed_category
    Subject = "New arrivals in {favorite_category}"
ELSE IF loyalty_tier = Gold
    Subject = "Exclusive early access for Gold members"
ELSE
    Subject = "Products we picked just for you"

Product recommendation strategy

Combine several recommendation models rather than relying on a single algorithm.

1. Recently viewed

• Show products the customer viewed but didn't purchase.

2. Similar products

• Recommend items with similar attributes (brand, style, category, price).

3. Frequently bought together

• Suggest complementary products based on historical purchase patterns.

4. Personalized ranking
Score each product using signals such as:

• affinity to category
• recent browsing
• purchase history
• inventory availability
• popularity
• profit margin
• business priorities

Example scoring function:

Recommendation Score =
0.40 × Category Affinity +
0.30 × Recent Activity +
0.15 × Purchase Similarity +
0.10 × Popularity +
0.05 × Business Priority

Customer attributes to use

Useful profile data includes:

• First name
• Age group (where appropriate and compliant)
• Gender preference (if collected)
• Geographic location
• Loyalty tier
• Preferred brands
• Favorite categories
• Average order value
• Purchase frequency
• Customer lifetime value

Recent activity signals

Behavioral signals often outperform static profile data:

• Products viewed
• Searches performed
• Items added to cart
• Cart abandonment
• Purchases
• Email opens
• Email clicks
• Website visits
• Time since last purchase
• Time since last visit

End-to-end example

Customer

• Gold member
• Favorite category: Running
• Viewed three pairs of shoes yesterday
• Purchased socks last month

Subject line

Alex, new running shoes picked just for you

Email content

1. Recently viewed running shoes
2. Similar running shoes in stock
3. Matching performance apparel
4. Running socks and accessories
5. Gold member discount banner

Best practices

• Use behavioral data from the last 7–30 days to keep recommendations relevant.
• Fall back to best sellers or trending products if recent activity is sparse.
• Limit recommendations to 4–8 high-confidence products rather than showing many lower-quality options.
• Continuously A/B test subject lines and recommendation strategies, measuring open rate, click-through rate, conversion rate, and revenue per email.
• Respect user privacy and consent preferences, and avoid using sensitive personal attributes for personalization unless appropriate, compliant, and explicitly permitted.

This hybrid approach—combining customer attributes with recent behavior—typically delivers more relevant subject lines and product recommendations while remaining resilient when either profile or activity data is limited.

A strong approach is to combine attribute-based personalization (who the customer is) with behavior-based personalization (what they recently did). This typically produces better engagement than relying on either alone.

Subject line personalization strategy

Create a subject line using multiple signals instead of just inserting the customer's name.

Examples:

• Name + interest: "Emma, new arrivals in your favorite collection"
• Loyalty status: "Gold members get first access today"
• Recent browsing: "Still thinking about these headphones?"
• Recent purchase: "Complete your setup with these accessories"
• Location: "Ready for cooler weather in Seattle?"

Use dynamic rules such as:

IF abandoned_cart
    Subject = "Your items are waiting"
ELSE IF browsed_category
    Subject = "New arrivals in {favorite_category}"
ELSE IF loyalty_tier = Gold
    Subject = "Exclusive early access for Gold members"
ELSE
    Subject = "Products we picked just for you"

Product recommendation strategy

Combine several recommendation models rather than relying on a single algorithm.

1. Recently viewed

• Show products the customer viewed but didn't purchase.

2. Similar products

• Recommend items with similar attributes (brand, style, category, price).

3. Frequently bought together

• Suggest complementary products based on historical purchase patterns.

4. Personalized ranking
Score each product using signals such as:

• affinity to category
• recent browsing
• purchase history
• inventory availability
• popularity
• profit margin
• business priorities

Example scoring function:

Recommendation Score =
0.40 × Category Affinity +
0.30 × Recent Activity +
0.15 × Purchase Similarity +
0.10 × Popularity +
0.05 × Business Priority

Customer attributes to use

Useful profile data includes:

• First name
• Age group (where appropriate and compliant)
• Gender preference (if collected)
• Geographic location
• Loyalty tier
• Preferred brands
• Favorite categories
• Average order value
• Purchase frequency
• Customer lifetime value

Recent activity signals

Behavioral signals often outperform static profile data:

• Products viewed
• Searches performed
• Items added to cart
• Cart abandonment
• Purchases
• Email opens
• Email clicks
• Website visits
• Time since last purchase
• Time since last visit

End-to-end example

Customer

• Gold member
• Favorite category: Running
• Viewed three pairs of shoes yesterday
• Purchased socks last month

Subject line

Alex, new running shoes picked just for you

Email content

1. Recently viewed running shoes
2. Similar running shoes in stock
3. Matching performance apparel
4. Running socks and accessories
5. Gold member discount banner

Best practices

• Use behavioral data from the last 7–30 days to keep recommendations relevant.
• Fall back to best sellers or trending products if recent activity is sparse.
• Limit recommendations to 4–8 high-confidence products rather than showing many lower-quality options.
• Continuously A/B test subject lines and recommendation strategies, measuring open rate, click-through rate, conversion rate, and revenue per email.
• Respect user privacy and consent preferences, and avoid using sensitive personal attributes for personalization unless appropriate, compliant, and explicitly permitted.

This hybrid approach—combining customer attributes with recent behavior—typically delivers more relevant subject lines and product recommendations while remaining resilient when either profile or activity data is limited.

A strong approach is to combine attribute-based personalization (who the customer is) with behavior-based personalization (what they recently did). This typically produces better engagement than relying on either alone.

Subject line personalization strategy

Create a subject line using multiple signals instead of just inserting the customer's name.

Examples:

• Name + interest: "Emma, new arrivals in your favorite collection"
• Loyalty status: "Gold members get first access today"
• Recent browsing: "Still thinking about these headphones?"
• Recent purchase: "Complete your setup with these accessories"
• Location: "Ready for cooler weather in Seattle?"

Use dynamic rules such as:

IF abandoned_cart
    Subject = "Your items are waiting"
ELSE IF browsed_category
    Subject = "New arrivals in {favorite_category}"
ELSE IF loyalty_tier = Gold
    Subject = "Exclusive early access for Gold members"
ELSE
    Subject = "Products we picked just for you"

Product recommendation strategy

Combine several recommendation models rather than relying on a single algorithm.

1. Recently viewed

• Show products the customer viewed but didn't purchase.

2. Similar products

• Recommend items with similar attributes (brand, style, category, price).

3. Frequently bought together

• Suggest complementary products based on historical purchase patterns.

4. Personalized ranking
Score each product using signals such as:

• affinity to category
• recent browsing
• purchase history
• inventory availability
• popularity
• profit margin
• business priorities

Example scoring function:

Recommendation Score =
0.40 × Category Affinity +
0.30 × Recent Activity +
0.15 × Purchase Similarity +
0.10 × Popularity +
0.05 × Business Priority

Customer attributes to use

Useful profile data includes:

• First name
• Age group (where appropriate and compliant)
• Gender preference (if collected)
• Geographic location
• Loyalty tier
• Preferred brands
• Favorite categories
• Average order value
• Purchase frequency
• Customer lifetime value

Recent activity signals

Behavioral signals often outperform static profile data:

• Products viewed
• Searches performed
• Items added to cart
• Cart abandonment
• Purchases
• Email opens
• Email clicks
• Website visits
• Time since last purchase
• Time since last visit

End-to-end example

Customer

• Gold member
• Favorite category: Running
• Viewed three pairs of shoes yesterday
• Purchased socks last month

Subject line

Alex, new running shoes picked just for you

Email content

1. Recently viewed running shoes
2. Similar running shoes in stock
3. Matching performance apparel
4. Running socks and accessories
5. Gold member discount banner

Best practices

• Use behavioral data from the last 7–30 days to keep recommendations relevant.
• Fall back to best sellers or trending products if recent activity is sparse.
• Limit recommendations to 4–8 high-confidence products rather than showing many lower-quality options.
• Continuously A/B test subject lines and recommendation strategies, measuring open rate, click-through rate, conversion rate, and revenue per email.
• Respect user privacy and consent preferences, and avoid using sensitive personal attributes for personalization unless appropriate, compliant, and explicitly permitted.

This hybrid approach—combining customer attributes with recent behavior—typically delivers more relevant subject lines and product recommendations while remaining resilient when either profile or activity data is limited.

A strong approach is to combine attribute-based personalization (who the customer is) with behavior-based personalization (what they recently did). This typically produces better engagement than relying on either alone.

Subject line personalization strategy

Create a subject line using multiple signals instead of just inserting the customer's name.

Examples:

• Name + interest: "Emma, new arrivals in your favorite collection"
• Loyalty status: "Gold members get first access today"
• Recent browsing: "Still thinking about these headphones?"
• Recent purchase: "Complete your setup with these accessories"
• Location: "Ready for cooler weather in Seattle?"

Use dynamic rules such as:

IF abandoned_cart
    Subject = "Your items are waiting"
ELSE IF browsed_category
    Subject = "New arrivals in {favorite_category}"
ELSE IF loyalty_tier = Gold
    Subject = "Exclusive early access for Gold members"
ELSE
    Subject = "Products we picked just for you"

Product recommendation strategy

Combine several recommendation models rather than relying on a single algorithm.

1. Recently viewed

• Show products the customer viewed but didn't purchase.

2. Similar products

• Recommend items with similar attributes (brand, style, category, price).

3. Frequently bought together

• Suggest complementary products based on historical purchase patterns.

4. Personalized ranking
Score each product using signals such as:

• affinity to category
• recent browsing
• purchase history
• inventory availability
• popularity
• profit margin
• business priorities

Example scoring function:

Recommendation Score =
0.40 × Category Affinity +
0.30 × Recent Activity +
0.15 × Purchase Similarity +
0.10 × Popularity +
0.05 × Business Priority

Customer attributes to use

Useful profile data includes:

• First name
• Age group (where appropriate and compliant)
• Gender preference (if collected)
• Geographic location
• Loyalty tier
• Preferred brands
• Favorite categories
• Average order value
• Purchase frequency
• Customer lifetime value

Recent activity signals

Behavioral signals often outperform static profile data:

• Products viewed
• Searches performed
• Items added to cart
• Cart abandonment
• Purchases
• Email opens
• Email clicks
• Website visits
• Time since last purchase
• Time since last visit

End-to-end example

Customer

• Gold member
• Favorite category: Running
• Viewed three pairs of shoes yesterday
• Purchased socks last month

Subject line

Alex, new running shoes picked just for you

Email content

1. Recently viewed running shoes
2. Similar running shoes in stock
3. Matching performance apparel
4. Running socks and accessories
5. Gold member discount banner

Best practices

• Use behavioral data from the last 7–30 days to keep recommendations relevant.
• Fall back to best sellers or trending products if recent activity is sparse.
• Limit recommendations to 4–8 high-confidence products rather than showing many lower-quality options.
• Continuously A/B test subject lines and recommendation strategies, measuring open rate, click-through rate, conversion rate, and revenue per email.
• Respect user privacy and consent preferences, and avoid using sensitive personal attributes for personalization unless appropriate, compliant, and explicitly permitted.

This hybrid approach—combining customer attributes with recent behavior—typically delivers more relevant subject lines and product recommendations while remaining resilient when either profile or activity data is limited.

A strong approach is to combine rule-based personalization (for guaranteed business logic) with AI-driven ranking and subject line generation (for relevance and engagement). This gives you control while still adapting to each customer's behavior.

Recommended architecture

Personalizing subject lines

Use multiple signals instead of just the customer's first name.

Customer attributes

• First name
• Loyalty status
• Preferred category
• Geographic location
• Average order value
• Membership anniversary
• Preferred brand

Example:

Emma, your Gold rewards are waiting

or

New arrivals from Patagonia you'll love

Recent activity

• Recently viewed products
• Abandoned cart
• Recently purchased complementary item
• Wishlist additions
• Browsing frequency
• Price drop alerts

Examples:

Viewed running shoes:

Still thinking about those running shoes?

Added coffee machine to cart:

Your coffee machine is almost yours

Recently bought a camera:

Accessories picked just for your new camera

Combining both

Instead of using only one attribute:

{Name},
because you recently viewed {Category},
here are our top picks for {Preferred Brand}.

Example:

Sarah, since you loved Nike, here are our newest running arrivals

Product recommendation strategy

Use a layered ranking approach.

1. Behavioral recommendations

Based on

• recently viewed
• frequently viewed
• abandoned cart
• recent purchases

Weight these most heavily.

2. Attribute-based recommendations

Recommend based on

• gender
• size
• location
• season
• loyalty tier

Example

Customer in Colorado:

Recommend

• winter jackets
• hiking gear

Customer in Florida:

Recommend

• sandals
• beachwear

3. Collaborative filtering

Recommend products purchased by similar customers.

Example

Customers who bought

Noise-cancelling headphones

also bought

Travel case

4. Business rules

Override recommendations when necessary.

Examples

• Exclude out-of-stock products
• Exclude recently purchased items
• Prioritize high-margin inventory
• Promote seasonal campaigns

Example recommendation logic

Score =
0.40 × Recent Activity
+ 0.25 × Purchase History
+ 0.15 × Customer Preferences
+ 0.10 × Similar Customers
+ 0.10 × Business Priority

AI-generated subject lines

Provide the model with structured context such as:

{
  "customer_name": "Emily",
  "loyalty": "Gold",
  "favorite_category": "Running",
  "recent_activity": [
    "Viewed Nike Pegasus",
    "Added socks to cart"
  ],
  "recommended_products": [
    "Nike Pegasus 42",
    "Running Socks",
    "Hydration Belt"
  ],
  "campaign": "New arrivals"
}

Possible outputs:

• Emily, your running favorites just got an upgrade
• Gold members get first access to these running picks
• Complete your running kit today
• Your Nike favorites are back in stock
• Ready for your next run?

Best practices

• Keep subject lines under 40–60 characters for better mobile visibility.
• Limit personalization to one or two key signals to avoid sounding intrusive.
• Don't recommend products the customer recently purchased unless they're replenishable or have natural complements.
• Fall back gracefully when data is sparse (e.g., use trending products or category best sellers).
• Continuously A/B test subject lines, recommendation algorithms, and send times to improve engagement.

This hybrid strategy—using customer attributes for context, recent activity for intent, business rules for control, and AI for generating natural language—typically produces more relevant subject lines and higher-performing product recommendations than relying on any single personalization method.

A strong approach is to combine rule-based personalization (for guaranteed business logic) with AI-driven ranking and subject line generation (for relevance and engagement). This gives you control while still adapting to each customer's behavior.

Recommended architecture

Personalizing subject lines

Use multiple signals instead of just the customer's first name.

Customer attributes

• First name
• Loyalty status
• Preferred category
• Geographic location
• Average order value
• Membership anniversary
• Preferred brand

Example:

Emma, your Gold rewards are waiting

or

New arrivals from Patagonia you'll love

Recent activity

• Recently viewed products
• Abandoned cart
• Recently purchased complementary item
• Wishlist additions
• Browsing frequency
• Price drop alerts

Examples:

Viewed running shoes:

Still thinking about those running shoes?

Added coffee machine to cart:

Your coffee machine is almost yours

Recently bought a camera:

Accessories picked just for your new camera

Combining both

Instead of using only one attribute:

{Name},
because you recently viewed {Category},
here are our top picks for {Preferred Brand}.

Example:

Sarah, since you loved Nike, here are our newest running arrivals

Product recommendation strategy

Use a layered ranking approach.

1. Behavioral recommendations

Based on

• recently viewed
• frequently viewed
• abandoned cart
• recent purchases

Weight these most heavily.

2. Attribute-based recommendations

Recommend based on

• gender
• size
• location
• season
• loyalty tier

Example

Customer in Colorado:

Recommend

• winter jackets
• hiking gear

Customer in Florida:

Recommend

• sandals
• beachwear

3. Collaborative filtering

Recommend products purchased by similar customers.

Example

Customers who bought

Noise-cancelling headphones

also bought

Travel case

4. Business rules

Override recommendations when necessary.

Examples

• Exclude out-of-stock products
• Exclude recently purchased items
• Prioritize high-margin inventory
• Promote seasonal campaigns

Example recommendation logic

Score =
0.40 × Recent Activity
+ 0.25 × Purchase History
+ 0.15 × Customer Preferences
+ 0.10 × Similar Customers
+ 0.10 × Business Priority

AI-generated subject lines

Provide the model with structured context such as:

{
  "customer_name": "Emily",
  "loyalty": "Gold",
  "favorite_category": "Running",
  "recent_activity": [
    "Viewed Nike Pegasus",
    "Added socks to cart"
  ],
  "recommended_products": [
    "Nike Pegasus 42",
    "Running Socks",
    "Hydration Belt"
  ],
  "campaign": "New arrivals"
}

Possible outputs:

• Emily, your running favorites just got an upgrade
• Gold members get first access to these running picks
• Complete your running kit today
• Your Nike favorites are back in stock
• Ready for your next run?

Best practices

• Keep subject lines under 40–60 characters for better mobile visibility.
• Limit personalization to one or two key signals to avoid sounding intrusive.
• Don't recommend products the customer recently purchased unless they're replenishable or have natural complements.
• Fall back gracefully when data is sparse (e.g., use trending products or category best sellers).
• Continuously A/B test subject lines, recommendation algorithms, and send times to improve engagement.

This hybrid strategy—using customer attributes for context, recent activity for intent, business rules for control, and AI for generating natural language—typically produces more relevant subject lines and higher-performing product recommendations than relying on any single personalization method.

A strong approach is to combine rule-based personalization (for guaranteed business logic) with AI-driven ranking and subject line generation (for relevance and engagement). This gives you control while still adapting to each customer's behavior.

Recommended architecture

Personalizing subject lines

Use multiple signals instead of just the customer's first name.

Customer attributes

• First name
• Loyalty status
• Preferred category
• Geographic location
• Average order value
• Membership anniversary
• Preferred brand

Example:

Emma, your Gold rewards are waiting

or

New arrivals from Patagonia you'll love

Recent activity

• Recently viewed products
• Abandoned cart
• Recently purchased complementary item
• Wishlist additions
• Browsing frequency
• Price drop alerts

Examples:

Viewed running shoes:

Still thinking about those running shoes?

Added coffee machine to cart:

Your coffee machine is almost yours

Recently bought a camera:

Accessories picked just for your new camera

Combining both

Instead of using only one attribute:

{Name},
because you recently viewed {Category},
here are our top picks for {Preferred Brand}.

Example:

Sarah, since you loved Nike, here are our newest running arrivals

Product recommendation strategy

Use a layered ranking approach.

1. Behavioral recommendations

Based on

• recently viewed
• frequently viewed
• abandoned cart
• recent purchases

Weight these most heavily.

2. Attribute-based recommendations

Recommend based on

• gender
• size
• location
• season
• loyalty tier

Example

Customer in Colorado:

Recommend

• winter jackets
• hiking gear

Customer in Florida:

Recommend

• sandals
• beachwear

3. Collaborative filtering

Recommend products purchased by similar customers.

Example

Customers who bought

Noise-cancelling headphones

also bought

Travel case

4. Business rules

Override recommendations when necessary.

Examples

• Exclude out-of-stock products
• Exclude recently purchased items
• Prioritize high-margin inventory
• Promote seasonal campaigns

Example recommendation logic

Score =
0.40 × Recent Activity
+ 0.25 × Purchase History
+ 0.15 × Customer Preferences
+ 0.10 × Similar Customers
+ 0.10 × Business Priority

AI-generated subject lines

Provide the model with structured context such as:

{
  "customer_name": "Emily",
  "loyalty": "Gold",
  "favorite_category": "Running",
  "recent_activity": [
    "Viewed Nike Pegasus",
    "Added socks to cart"
  ],
  "recommended_products": [
    "Nike Pegasus 42",
    "Running Socks",
    "Hydration Belt"
  ],
  "campaign": "New arrivals"
}

Possible outputs:

• Emily, your running favorites just got an upgrade
• Gold members get first access to these running picks
• Complete your running kit today
• Your Nike favorites are back in stock
• Ready for your next run?

Best practices

• Keep subject lines under 40–60 characters for better mobile visibility.
• Limit personalization to one or two key signals to avoid sounding intrusive.
• Don't recommend products the customer recently purchased unless they're replenishable or have natural complements.
• Fall back gracefully when data is sparse (e.g., use trending products or category best sellers).
• Continuously A/B test subject lines, recommendation algorithms, and send times to improve engagement.

This hybrid strategy—using customer attributes for context, recent activity for intent, business rules for control, and AI for generating natural language—typically produces more relevant subject lines and higher-performing product recommendations than relying on any single personalization method.

A good solution is a real-time personalization layer that combines customer attributes with recent behavioral events, then feeds those signals into both subject-line generation and product-ranking models.

Recommended architecture

1. Unified customer profile
Combine relatively stable attributes such as:

• Location, language, customer tier
• Purchase history and categories
• Price sensitivity
• Lifecycle stage
• Stated preferences

with behavioral signals such as:

• Products viewed recently
• Searches
• Cart additions/abandons
• Purchases and returns
• Email opens/clicks
• Recency and frequency of engagement

2. Feature + decisioning layer
Create features such as:

• days_since_last_purchase
• views_last_7_days
• favorite_category
• cart_items_last_24h
• discount_affinity
• recent_product_interest

Use these to determine what product to recommend and what message angle to use.

3. Product recommendation engine
Start with a hybrid approach:

• Collaborative filtering for "customers like you"
• Content-based recommendations for product/category affinity
• Recent-activity weighting so current intent dominates older behavior
• Business rules for inventory, margin, eligibility, and exclusions

A useful scoring formulation is:

recommendation_score = behavioral_affinity × recency_weight × customer_fit × business_constraints

4. Subject-line personalization
Rather than letting an LLM independently invent subjects, give it structured inputs from the decisioning layer:

Customer: high-value, returning customer
Recent activity: viewed running shoes 3 times this week
Recommendation: running shoe X
Preferred category: running
Discount affinity: high
Objective: maximize click-through

The LLM can then generate several subject-line candidates, while a ranking model chooses among them based on predicted engagement.

5. Continuous learning
Log impressions, opens, clicks, purchases, unsubscribes, and recommendation conversions. Use those outcomes for experimentation and model retraining.

Practical stack

For an initial implementation, I'd use:

Customer/event data → warehouse/CDP → feature store → recommendation/ranking service → LLM subject-line generator → email platform

Keep customer data and behavioral signals in your own controlled data layer, and send the personalization service only the minimum information needed for generation.

Important design choice

I'd avoid making the LLM the recommendation engine. Let a deterministic/ML ranking system decide what to recommend, and use the LLM primarily for how to express the recommendation. This gives you much better control over relevance, inventory, compliance, and measurable experimentation.

If you're building this for an existing email stack, the next step would be to map this architecture onto your specific CRM/email platform and data warehouse.

A strong approach is to combine rule-based personalization (for guaranteed business logic) with AI-driven ranking and subject line generation (for relevance and engagement). This gives you control while still adapting to each customer's behavior.

Recommended architecture

Personalizing subject lines

Use multiple signals instead of just the customer's first name.

Customer attributes

• First name
• Loyalty status
• Preferred category
• Geographic location
• Average order value
• Membership anniversary
• Preferred brand

Example:

Emma, your Gold rewards are waiting

or

New arrivals from Patagonia you'll love

Recent activity

• Recently viewed products
• Abandoned cart
• Recently purchased complementary item
• Wishlist additions
• Browsing frequency
• Price drop alerts

Examples:

Viewed running shoes:

Still thinking about those running shoes?

Added coffee machine to cart:

Your coffee machine is almost yours

Recently bought a camera:

Accessories picked just for your new camera

Combining both

Instead of using only one attribute:

{Name},
because you recently viewed {Category},
here are our top picks for {Preferred Brand}.

Example:

Sarah, since you loved Nike, here are our newest running arrivals

Product recommendation strategy

Use a layered ranking approach.

1. Behavioral recommendations

Based on

• recently viewed
• frequently viewed
• abandoned cart
• recent purchases

Weight these most heavily.

2. Attribute-based recommendations

Recommend based on

• gender
• size
• location
• season
• loyalty tier

Example

Customer in Colorado:

Recommend

• winter jackets
• hiking gear

Customer in Florida:

Recommend

• sandals
• beachwear

3. Collaborative filtering

Recommend products purchased by similar customers.

Example

Customers who bought

Noise-cancelling headphones

also bought

Travel case

4. Business rules

Override recommendations when necessary.

Examples

• Exclude out-of-stock products
• Exclude recently purchased items
• Prioritize high-margin inventory
• Promote seasonal campaigns

Example recommendation logic

Score =
0.40 × Recent Activity
+ 0.25 × Purchase History
+ 0.15 × Customer Preferences
+ 0.10 × Similar Customers
+ 0.10 × Business Priority

AI-generated subject lines

Provide the model with structured context such as:

{
  "customer_name": "Emily",
  "loyalty": "Gold",
  "favorite_category": "Running",
  "recent_activity": [
    "Viewed Nike Pegasus",
    "Added socks to cart"
  ],
  "recommended_products": [
    "Nike Pegasus 42",
    "Running Socks",
    "Hydration Belt"
  ],
  "campaign": "New arrivals"
}

Possible outputs:

• Emily, your running favorites just got an upgrade
• Gold members get first access to these running picks
• Complete your running kit today
• Your Nike favorites are back in stock
• Ready for your next run?

Best practices

• Keep subject lines under 40–60 characters for better mobile visibility.
• Limit personalization to one or two key signals to avoid sounding intrusive.
• Don't recommend products the customer recently purchased unless they're replenishable or have natural complements.
• Fall back gracefully when data is sparse (e.g., use trending products or category best sellers).
• Continuously A/B test subject lines, recommendation algorithms, and send times to improve engagement.

This hybrid strategy—using customer attributes for context, recent activity for intent, business rules for control, and AI for generating natural language—typically produces more relevant subject lines and higher-performing product recommendations than relying on any single personalization method.

I’d recommend a hybrid real-time personalization stack: use structured ML for product recommendations and an LLM for subject-line generation, with both driven by the same customer profile and recent-activity features.

Recommended architecture

1. Build a unified customer profile
Combine relatively stable attributes with behavioral signals:

• Customer attributes: location, lifecycle stage, loyalty tier, preferences, price sensitivity
• Recent activity: products viewed, searches, purchases, cart additions, email clicks, category affinity
• Context: device, channel, time since last activity, current campaign

Keep these features in a customer data platform/warehouse and expose a low-latency profile to your marketing system.

2. Product recommendations: use a recommendation model
Rather than asking an LLM to choose products, use a recommender optimized for ranking:

customer + recent events → candidate generation → ranking → top 3–10 products

A good starting point is a hybrid model combining:

• Collaborative filtering ("customers like you bought...")
• Content similarity ("you recently viewed running shoes...")
• Business rules (inventory, margin, eligibility, exclusions)
• Recency weighting so yesterday's behavior matters more than six-month-old behavior

This gives you measurable recommendation quality and avoids hallucinated products.

3. Subject lines: use an LLM with structured inputs
Give the LLM the campaign objective plus selected customer features and recommendation context, e.g.:

Customer:
- Lifecycle: returning customer
- Preferred category: running
- Recently viewed: trail shoes
- Last purchase: 45 days ago

Campaign:
- Goal: drive a second purchase
- Recommended products: [A, B, C]

Generate 5 subject lines.
Constraints:
- Don't mention attributes that could feel sensitive
- Don't claim a discount unless provided
- Maximum 50 characters
- Match brand voice

Then use an experimentation layer to determine which variants actually perform best.

The important part: personalize jointly

Instead of independently generating a subject line and recommendations, pass the recommendation context into subject-line generation.

For example:

"Still thinking about the trails?"
→ Trail shoes ranked highly because the customer recently viewed them.

versus a generic:

"New products you'll love"

This makes the personalization feel coherent rather than like two separate systems.

Production flow

Customer events
      ↓
Customer profile + behavioral features
      ↓
 ┌───────────────┬────────────────┐
 ↓               ↓                ↓
Recommendation  Audience/        Eligibility
model           segmentation      rules
 ↓               ↓
Top products ──────────→ LLM subject-line generator
                              ↓
                       Safety/brand checks
                              ↓
                       Email/SMS campaign
                              ↓
                    Opens/clicks/purchases
                              ↓
                         Experimentation
                              ↺

What I'd implement first

For an initial version, I'd avoid an overly complicated AI platform:

1. Warehouse/CDP for customer profiles and events.
2. Recommendation service using behavioral + product features.
3. LLM service for subject-line variants, constrained by structured campaign data.
4. Rules/guardrails for privacy, inventory, pricing, frequency caps, and brand voice.
5. A/B or multi-armed-bandit experimentation to continuously measure incremental revenue rather than optimizing just opens.
6. Real-time event ingestion so a product viewed five minutes ago can influence the next message.

The key design principle is LLM for language, ML for ranking. Let the recommender decide what to show and let the LLM decide how to talk about it. That tends to be more controllable, measurable, and scalable than putting the entire personalization decision into an LLM.

A strong solution is a real-time personalization layer that feeds both email subject-line generation and product recommendations from the same customer profile.

Recommended architecture

1. Unified customer profile
Combine:

• Demographics and preferences
• Purchase history and customer lifecycle stage
• Recent browsing/search activity
• Products viewed, carted, or purchased
• Email engagement
• Context such as device, location, and time

Store both relatively stable attributes and recent behavioral events.

2. Feature + decisioning layer
Create features such as:

• days_since_last_purchase
• categories_viewed_7d
• products_viewed_24h
• cart_items
• preferred_category
• price_affinity
• email_engagement_score

Then use a decision engine to determine the customer's current intent.

3. Product recommendations
Start with a hybrid recommender:

• Behavioral: "customers who viewed/bought X also liked Y"
• Content-based: match products to categories/brands the customer recently interacted with
• Personalized ranking: rerank candidates using customer attributes, recency, price affinity, inventory, and business rules

For example:

Customer recently viewed running shoes + historically buys athletic apparel → recommend complementary running socks and apparel, rather than generic best sellers.

4. Subject-line personalization
Use the same features but constrain the output to a small set of tested templates rather than letting an LLM freely invent marketing copy.

Examples:

• "Still thinking about {{product}}?"
• "New {{category}} picks for you"
• "Your {{category}} favorites are back"
• "Complete your {{product}} setup"

A model can select or fill the best template based on predicted open/click probability.

5. Continuous experimentation
Run A/B or multi-armed-bandit experiments on:

• Personalized vs. generic subject lines
• Different recommendation strategies
• Degree of personalization
• Send timing

Optimize for incremental revenue/conversion, not merely opens.

A practical MVP

Customer events
      ↓
Customer profile + recent-activity features
      ↓
 ┌───────────────┬────────────────┐
 │               │                │
Subject-line   Candidate        Business
model          recommender      rules
 │               │                │
 └───────┬───────┴────────────────┘
         ↓
   Personalized email
         ↓
  Click / purchase events
         ↓
       Feedback

I'd implement the first version with rules + a recommender model + a lightweight ML ranking model, then add an LLM specifically for controlled subject-line generation. This tends to be easier to evaluate, safer for brand voice, and less expensive than putting an LLM directly in the entire personalization loop.

If you're choosing a concrete stack, I can also recommend an architecture using Snowflake/Databricks + a CDP + a recommendation service + an LLM, including the data model and APIs.