Email Segmentation Best Practices: Leveraging Purchase History for Enhanced Targeting

Email segmentation is a cornerstone of effective email marketing, allowing you to deliver highly relevant messages to specific groups of your audience. This article focuses on leveraging purchase history as a powerful segmentation tool. We’ll explore how to segment your email list based on past purchases, product categories, order frequency, and spending habits, providing actionable strategies and practical examples to boost engagement, drive sales, and build stronger customer relationships.

• Segmenting by Product Category
• Segmenting by Purchase Frequency
• Segmenting by Monetary Value (RFM)
• Segmenting by Lapsed Purchases (Win-Back Campaigns)

Segmenting by Product Category

One of the most intuitive and effective ways to segment your email list based on purchase history is by product category. This allows you to send targeted promotions and product recommendations to customers who have already shown an interest in specific types of items. For instance, if a customer frequently purchases running shoes, they’re more likely to be interested in new running shoe models, athletic apparel, or fitness trackers than they are in, say, kitchen appliances.

Here’s how you can implement product category segmentation:

• Tag your products: Ensure each product in your catalog is accurately tagged with relevant categories (e.g., “Running Shoes,” “Coffee Makers,” “Gardening Tools”). This tagging is crucial for data accuracy.
• Track customer purchases: Your e-commerce platform or CRM should automatically track which products each customer purchases. This data is the foundation of your segmentation strategy.
• Create dynamic email lists: Use your email marketing platform to create dynamic lists that automatically add customers who have purchased products within specific categories.

Example 1: Promoting New Running Shoes

Let’s say you’re launching a new line of running shoes. Instead of sending a mass email to your entire list, segment your audience by identifying customers who have previously purchased running shoes. You can then craft a personalized email highlighting the features and benefits of the new shoes, specifically tailored to their interest in running.

Email Subject: Step Up Your Run: Introducing the New [Shoe Model Name]

Email Body (Snippet):

Hi [Customer Name],

We know you’re a runner, and we’re excited to introduce our latest innovation: the [Shoe Model Name]! These shoes are designed for [specific benefits, e.g., increased cushioning, improved stability, faster speed].

Because you’ve purchased running shoes from us before, we thought you’d be particularly interested. Check them out here: [Link to Product Page]

// Example SQL query to identify customers who purchased running shoes
SELECT DISTINCT customer_id
FROM orders
JOIN order_items ON orders.order_id = order_items.order_id
JOIN products ON order_items.product_id = products.product_id
WHERE products.category = 'Running Shoes';

This SQL query demonstrates how you could pull data from your database to identify the specific customers for your segmented campaign. You would then use this list to target your email.

Example 2: Cross-Selling Related Products

If a customer recently purchased a coffee maker, you can send them an email promoting coffee beans, filters, or mugs. This is a great way to increase your average order value and provide value to your customers by suggesting complementary products.

Email Subject: Enjoy the Perfect Cup: Enhance Your Coffee Experience

Email Body (Snippet):

Hi [Customer Name],

We hope you’re enjoying your new coffee maker! To help you get the most out of it, we’ve curated a selection of premium coffee beans, filters, and stylish mugs that we think you’ll love.

Explore our recommendations here: [Link to Category Page]

// Example logic in your email marketing platform to trigger cross-sell email
IF customer.purchased("Coffee Maker") AND email_sent_today("Cross-Sell Coffee Accessories") == FALSE THEN
  send_email("Cross-Sell Coffee Accessories", customer.email)
ENDIF

This example illustrates how to trigger an automated email based on a purchase event. The `email_sent_today` check prevents sending the same cross-sell email multiple times within a short period.

Expert Tip: Continuously analyze the performance of your product category segments. Track click-through rates, conversion rates, and revenue generated to identify underperforming segments and refine your messaging or product recommendations.

By segmenting your email list by product category, you can deliver highly targeted and relevant messages that resonate with your audience, ultimately leading to increased engagement and sales.

Segmenting by Purchase Frequency

Purchase frequency is another crucial dimension for email segmentation. Understanding how often customers make purchases allows you to tailor your communication strategy to nurture relationships and encourage repeat business. You can identify your most loyal customers, those at risk of churning, and everyone in between, then craft targeted campaigns accordingly.

Here’s how to segment your email list based on purchase frequency:

• Define frequency segments: Establish clear criteria for defining different frequency segments (e.g., “High-Frequency” – purchases monthly, “Mid-Frequency” – purchases quarterly, “Low-Frequency” – purchases annually).
• Track purchase intervals: Calculate the average time between purchases for each customer. This metric is the foundation of your segmentation.
• Automate list updates: Use your CRM or email marketing platform to automatically move customers between frequency segments based on their recent purchase behavior.

Example 1: Rewarding High-Frequency Customers

Identify your high-frequency customers (e.g., those who purchase at least once a month) and reward them with exclusive offers, early access to new products, or personalized thank-you messages. Showing appreciation for their loyalty can strengthen your relationship and encourage them to continue purchasing from you.

Email Subject: Thank You for Being a Valued Customer!

Email Body (Snippet):

Hi [Customer Name],

We truly appreciate your continued support! As a valued customer, we’d like to offer you an exclusive [discount code] on your next purchase.

Thank you for being a part of our community!

// Example Python code to identify high-frequency customers
import pandas as pd

# Assume 'df' is a Pandas DataFrame with customer data and purchase dates
# Calculate time between purchases for each customer
df['time_since_last_purchase'] = df.groupby('customer_id')['purchase_date'].diff().dt.days

# Calculate average time between purchases for each customer
avg_time_between_purchases = df.groupby('customer_id')['time_since_last_purchase'].mean()

# Identify high-frequency customers (e.g., average time less than 30 days)
high_frequency_customers = avg_time_between_purchases[avg_time_between_purchases <= 30].index.tolist()

print(high_frequency_customers)

This Python code uses the Pandas library to calculate the average time between purchases for each customer and identifies those who purchase frequently. This list would be used to populate your “High-Frequency” customer segment.

Example 2: Encouraging Repeat Purchases from Mid-Frequency Customers

For mid-frequency customers (e.g., those who purchase quarterly), you can send targeted promotions or product recommendations based on their past purchases to encourage them to buy more often. Consider highlighting limited-time offers or exclusive deals to create a sense of urgency.

Email Subject: Don’t Miss Out! Exclusive Offer Just For You

Email Body (Snippet):

Hi [Customer Name],

We’ve got a special offer just for you! For a limited time, get [discount percentage] off your next purchase using code [discount code].

Shop now and save: [Link to Website]

// Example SQL query to identify customers who haven't purchased in the last 60 days but purchased before
SELECT customer_id
FROM orders
WHERE purchase_date < DATE('now', '-60 days')
AND customer_id IN (SELECT customer_id FROM orders WHERE purchase_date < DATE('now', '-60 days'))
GROUP BY customer_id;

This SQL query identifies customers who haven’t made a purchase in the last 60 days, indicating they might be slipping into a lower frequency segment, warranting a targeted offer.

By segmenting your email list by purchase frequency, you can tailor your communication strategy to nurture relationships, encourage repeat purchases, and ultimately increase customer lifetime value.

Segmenting by Monetary Value (RFM)

Segmenting by monetary value is a crucial aspect of email marketing that focuses on the amount of money customers spend with your business. A powerful technique for this is using the RFM (Recency, Frequency, Monetary) model. RFM allows you to identify your most valuable customers and tailor your marketing efforts to maximize their potential.

The RFM model assesses customers based on three key factors:

• Recency (R): How recently a customer made a purchase.
• Frequency (F): How often a customer makes purchases.
• Monetary Value (M): How much money a customer spends in total.

Here’s how to implement RFM segmentation:

• Calculate RFM scores: Assign scores to each customer based on their recency, frequency, and monetary value. Typically, scores range from 1 to 5, with 5 being the best.
• Combine RFM scores: Combine the individual RFM scores to create customer segments (e.g., a customer with scores of 5-5-5 would be a “Champion,” while a customer with scores of 1-1-1 would be a “Lost Customer”).
• Develop targeted campaigns: Create email campaigns tailored to the specific needs and behaviors of each RFM segment.

Example 1: Targeting “Champion” Customers (5-5-5)

Your “Champion” customers are your most valuable assets. Reward them with exclusive offers, early access to new products, and personalized thank-you messages. Focus on strengthening their loyalty and encouraging them to advocate for your brand.

Email Subject: Exclusive Offer Just For Our Best Customers!

Email Body (Snippet):

Hi [Customer Name],

As one of our most valued customers, we’d like to offer you an exclusive [discount percentage] discount on your next purchase, plus early access to our new [Product Name]!

Thank you for your continued support!

// Example Python code using pandas to calculate RFM scores
import pandas as pd

# Assume 'df' is a Pandas DataFrame with customer data (customer_id, purchase_date, total_spent)
# Calculate recency
df['recency'] = (df['purchase_date'].max() - df['purchase_date']).dt.days

# Calculate frequency
df['frequency'] = df.groupby('customer_id')['purchase_date'].count()

# Calculate monetary value
df['monetary_value'] = df.groupby('customer_id')['total_spent'].sum()

# Create RFM segments (example: quantiles)
r_labels = range(5, 0, -1)
f_labels = range(1, 6)
m_labels = range(1, 6)

r_groups = pd.qcut(df['recency'], q=5, labels=r_labels)
f_groups = pd.qcut(df['frequency'], q=5, labels=f_labels)
m_groups = pd.qcut(df['monetary_value'], q=5, labels=m_labels)

df['r_score'] = r_groups.values
df['f_score'] = f_groups.values
df['m_score'] = m_groups.values

# Combine RFM scores
df['rfm_segment'] = df['r_score'].astype(str) + df['f_score'].astype(str) + df['m_score'].astype(str)

print(df[['customer_id', 'r_score', 'f_score', 'm_score', 'rfm_segment']].head())

This Python code demonstrates how to calculate RFM scores using quantiles and then combine them into an RFM segment string. This provides a structured way to segment customers based on their RFM behavior.

Example 2: Targeting “Potential Loyalists” (e.g., 4-4-3)

These customers have good recency and frequency scores but a lower monetary value. Encourage them to spend more by offering incentives to increase their average order value, such as free shipping on orders over a certain amount or bundling products together at a discounted price.

Email Subject: Unlock Free Shipping on Your Next Order!

Email Body (Snippet):

Hi [Customer Name],

We noticed you’ve been enjoying our products, and we want to make it even easier for you to shop! For a limited time, get free shipping on orders over $[Amount].

Shop now and take advantage of this offer: [Link to Website]

// Example A/B test using RFM segmentation

// Segment A: Champions (5-5-5) - Send personalized thank you email with exclusive product sneak peek
// Segment B: Potential Loyalists (4-4-3) - Send email offering free shipping on orders over $50

IF customer.rfm_segment == "555" THEN
  send_email("Champion Exclusive Sneak Peek", customer.email)
ELSIF customer.rfm_segment == "443" THEN
  send_email("Potential Loyalist Free Shipping Offer", customer.email)
ENDIF

This example shows a simple A/B test using RFM segmentation to send different email offers to different customer segments. Tracking the results of each email allows you to optimize your campaigns for maximum effectiveness.

By segmenting your email list using the RFM model, you can gain a deeper understanding of your customers’ value and tailor your marketing efforts to maximize their potential, leading to increased revenue and customer loyalty.

Segmenting by Lapsed Purchases (Win-Back Campaigns)

Customers who have stopped making purchases represent a significant opportunity for reactivation. These “lapsed” customers have already shown an interest in your products or services, making them easier to convert than completely new prospects. Segmenting your email list based on lapsed purchase behavior allows you to create targeted “win-back” campaigns designed to re-engage these customers and bring them back into the fold.

Here’s how to segment your email list based on lapsed purchases:

• Define “lapsed”: Establish a clear definition of what constitutes a “lapsed” customer (e.g., hasn’t made a purchase in 6 months, 1 year, etc.). This definition should be based on your industry, product lifecycle, and typical customer behavior.
• Track last purchase date: Your CRM or e-commerce platform should track the last purchase date for each customer. This is the key data point for identifying lapsed customers.
• Automate segmentation: Use your email marketing platform to automatically move customers into a “lapsed” segment when they meet your defined criteria.

Example 1: Offering a Discount to Re-Engage Lapsed Customers

A common and effective win-back strategy is to offer a discount or special promotion to encourage lapsed customers to make another purchase. This provides a tangible incentive for them to revisit your store and try your products again.

Email Subject: We Miss You! Here’s [Discount Percentage] Off Your Next Order

Email Body (Snippet):

Hi [Customer Name],

We haven’t seen you in a while, and we wanted to let you know we miss you! As a special welcome back, we’re offering you [discount percentage] off your next order. Use code [discount code] at checkout.

We hope to see you again soon!

// Example SQL query to identify lapsed customers (last purchase over 6 months ago)
SELECT customer_id
FROM orders
WHERE purchase_date < DATE('now', '-6 months')
AND customer_id NOT IN (SELECT customer_id FROM orders WHERE purchase_date >= DATE('now', '-6 months'))
GROUP BY customer_id;

This SQL query identifies customers whose last purchase was more than 6 months ago, making them candidates for a win-back campaign. The second part of the query ensures they haven’t made any recent purchases.

Example 2: Reminding Lapsed Customers of Their Past Purchases

Sometimes, a simple reminder of past positive experiences can be enough to re-engage a lapsed customer. Send them an email highlighting the products they previously purchased and suggesting similar or complementary items.

Email Subject: Remember These? Your Favorites Are Waiting!

Email Body (Snippet):

Hi [Customer Name],

We noticed you previously purchased [Product Name] and [Product Name] from us. We thought you might be interested in these similar or complementary items:

[Product Recommendations with Images and Links]

Come back and rediscover your favorites!

//Example logic to trigger a win-back email sequence

IF customer.last_purchase_date < (TODAY - 180 days) THEN
  //Customer is lapsed - start win-back sequence
  IF NOT customer.winback_email_sent THEN
     send_email("Winback Email 1 - Discount", customer.email)
     customer.winback_email_sent = TRUE
     customer.winback_email_date = TODAY
  ELSIF customer.winback_email_date < (TODAY - 30 days) THEN
      send_email("Winback Email 2 - Product Reminder", customer.email)
  ENDIF
ENDIF

This example demonstrates the logic behind a win-back email sequence, checking the last purchase date and whether a win-back email has already been sent. It can then trigger different emails after a specified delay.

Expert Tip: Personalization is key to win-back campaigns. Use the data you have about each customer’s past purchases and preferences to create highly relevant and compelling offers that will resonate with them and encourage them to return.

By effectively segmenting your email list based on lapsed purchase behavior and crafting targeted win-back campaigns, you can re-engage valuable customers and recover lost revenue.