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DEFINITION AND PURPOSE OF CUSTOMER SEGMENTATION
¡ Customer segmentation involves dividing a company's customer base into distinct groups or segments based on
shared characteristics. The purpose of customer segmentation is to better understand the diverse needs,
preferences, behaviors, and demographics of your customers. By grouping similar customers together, businesses
can tailor their marketing strategies and offerings to address the unique requirements of each segment, resulting in
more effective and personalized campaigns.
¡ Example: Let's consider an online clothing retailer. Instead of treating all customers the same, the retailer can
segment its customer base. For instance, they might identify segments like "Young Professionals," "Parents," and
"Fitness Enthusiasts." This segmentation allows the retailer to create targeted promotions, such as sending workout
gear recommendations to the "Fitness Enthusiasts" and formal wear options to the "Young Professionals."
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BENEFITS OF EFFECTIVE CUSTOMER SEGMENTATION
1. Personalized Marketing: Segmentation enables businesses to deliver personalized messages and offers to different
groups, increasing the likelihood of engagement and conversion.
2. Improved Customer Satisfaction: Understanding customer needs leads to better-tailored products and services,
resulting in happier and more loyal customers.
3. Higher ROI: Targeted marketing efforts are more efficient, reducing wasted resources and increasing the return on
investment (ROI) of marketing campaigns.
4. Market Expansion: Segmentation can uncover new market opportunities and niches that may have been overlooked
when treating all customers as a single group.
5. Better Resource Allocation: By knowing which segments are most profitable or have the most potential,
businesses can allocate resources more strategically.
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TYPES OF CUSTOMER SEGMENTATION
1. Demographic Segmentation: Dividing customers based on demographic attributes like age, gender, income,
education, and family size.
2. Psychographic Segmentation: Grouping customers based on psychological and lifestyle characteristics, such as
values, interests, attitudes, and personality traits.
3. Behavioral Segmentation: Segmenting based on customer behaviors and interactions with the brand, such as
purchase history, frequency, loyalty, and usage patterns.
4. Geographic Segmentation: Dividing customers by their geographical location, such as country, region, city, or even
climate.
Example: Consider a coffee shop chain. They could use geographic segmentation to offer different types of drinks
based on climate. In colder regions, they might promote warm beverages, while in warmer areas, they could highlight
refreshing iced drinks.
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SEGMENTATION METHOD
Demographic Segmentation: This method involves dividing customers based on demographic characteristics such as
age, gender, income, education, marital status, and more. It's one of the most basic forms of segmentation.
¡ Example: A company selling skincare products might use demographic segmentation to target their products. They
might create products for different age groups like teenagers, young adults, and middle-aged individuals, each with
tailored marketing messages and product features.
¡ Advantages:
• Easy to obtain and understand data.
• Provides a simple way to categorize customers.
¡ Limitations:
• Doesn't capture behavioral or psychographic nuances.
• Assumes that people within the same demographic group have similar preferences, which might not always be
accurate.
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SEGMENTATION METHOD
Psychographic Segmentation: This method involves categorizing customers based on their attitudes, values, interests,
and lifestyle.
¡ Example: An outdoor apparel company might use psychographic segmentation to target adventure enthusiasts.
They could identify customers who value outdoor activities, environmental conservation, and an active lifestyle. This
helps the company tailor their marketing message to resonate with these specific values.
¡ Advantages:
• Offers deeper insights into customer motivations.
• Allows for more targeted messaging.
¡ Limitations:
• Data collection can be more challenging compared to demographics.
• Requires understanding complex human behavior.
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SEGMENTATION METHOD
Behavioral Segmentation: This method divides customers based on their interactions, behaviors, and purchasing
patterns with a company's products or services.
¡ Example: An e-commerce platform might use behavioral segmentation to categorize customers into segments like
"Frequent Shoppers," "Window Shoppers," and "Deal Seekers." This helps them create personalized offers and
recommendations based on individual buying behavior.
¡ Advantages:
• Focuses on actual customer actions.
• Enables precise customization of marketing strategies.
¡ Limitations:
• Might not explain underlying motivations for behaviors.
• Doesn't account for potential changes in behavior.
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SEGMENTATION METHOD
Geographic Segmentation: This method involves segmenting customers based on their geographic location, such as
country, region, city, or climate.
¡ Example: A beverage company might use geographic segmentation to market different products based on climate.
They might promote hot beverages in colder regions and refreshing drinks in warmer climates.
¡ Advantages:
• Useful for tailoring products to local preferences.
• Factors in regional cultural differences.
¡ Limitations:
• Ignores other important factors that influence customer behavior.
• Might oversimplify customer preferences.
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SEGMENTATION METHOD
Combining Methods: To create a comprehensive understanding of customers, combining multiple segmentation
methods can be powerful. For instance, a luxury car manufacturer might combine demographic, psychographic, and
behavioral data to target a specific segment: affluent individuals (demographic) who value status and exclusivity
(psychographic) and have a history of purchasing high-end products (behavioral).
¡ Advantages:
• Provides a more holistic view of customers.
• Increases accuracy in targeting specific segments.
¡ Limitations:
• Can be complex to manage and analyze.
• Requires substantial data and resources.
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CREATING CUSTOMER PROFILES BASED ON SEGMENTS
In a retail context, customer profiling involves grouping customers into segments based on common characteristics,
behaviors, and preferences. These segments can help retailers understand their customer base better and tailor their
marketing efforts accordingly.
Imagine a retail clothing store that wants to create customer profiles for its different segments:
Segment 1: Young Urban Professionals
• Characteristics: Age 25-35, working professionals in urban areas.
• Behaviors: Frequent shoppers, interested in trendy and fashionable clothing.
• Preferences: Value quality and latest fashion trends.
• Marketing Strategy: Send them emails about new arrivals, offer exclusive discounts on premium items, and
showcase outfits suitable for the workplace and social events.
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CREATING CUSTOMER PROFILES BASED ON SEGMENTS
Segment 2: Budget-Conscious Shoppers
• Characteristics: Diverse age range, looking for deals and discounts.
• Behaviors: Careful spenders, search for sales and bargains.
• Preferences: Affordable clothing without compromising too much on quality.
• Marketing Strategy: Send them alerts about ongoing sales, promote clearance items, and offer bundle deals to
maximize value for their budget.
Segment 3: Outdoor Enthusiasts
• Characteristics: Active lifestyle, interested in outdoor activities.
• Behaviors: Purchase activewear and gear for outdoor adventures.
• Preferences: Functionality, durability, and comfort.
• Marketing Strategy: Highlight outdoor-specific clothing and accessories, provide content on how to choose the
right gear, and partner with outdoor event organizers for cross-promotions.
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HOW CUSTOMER PROFILES DRIVE PERSONALIZED MARKETING
STRATEGIES
Customer profiles empower retailers to create personalized marketing strategies that resonate with each segment's
unique characteristics and preferences. Instead of sending generic messages to all customers, retailers can tailor their
communications and offerings to specific groups.
Continuing with the retail clothing store example:
• The store can use the customer profiles to send personalized email campaigns that showcase products and
promotions that are most likely to appeal to each segment. For example, emails to the "Young Urban Professionals"
segment might focus on the latest fashion trends and high-quality items.
• Personalized recommendations can be provided on the store's website or app. When a customer from the "Outdoor
Enthusiasts" segment logs in, the site could display recommendations for activewear, hiking shoes, and other
outdoor gear.
• Social media ads can also be targeted to specific segments based on their interests. The "Budget-Conscious
Shoppers" segment might see ads highlighting ongoing sales and discounts, while the "Outdoor Enthusiasts"
segment might see ads featuring products suitable for their outdoor activities.
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SEGMENTATION TECHNIQUES – RFM ANALYSIS
Top Customer Highest Sales Value
Customer
RFM Analysis solves this issues by taking multiple factors into consideration
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SEGMENTATION TECHNIQUES
RFM Analysis (Recency, Frequency, Monetary Value)
RFM analysis is a powerful segmentation technique used to classify customers based on their behavior. It evaluates
three key metrics:
1. Recency: Measures the time since the customer's last purchase. Customers who made recent purchases are more
likely to be engaged and active.
2. Frequency: Counts the number of transactions within a specific period. Customers with higher purchase frequency
may represent loyal or engaged customers.
3. Monetary Value: Calculates the total amount spent by each customer. High monetary value customers are valuable
for the business.
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STEPS FOR DOING RFM ANALYSIS
Step-1: Understanding of Data
Example: We have data for 9 customers with date of purchase, frequency of purchase and sales volume of each
purchase.
Customer ID Last Date of Purchase
(Recency)
Number of Time
Purchase (Frequency)
Total Sales Value
(Monetary)
Customer-1 10-04-2018 6 2139.54
Customer-1 24-04-2018 4 1786.75
Customer-1 17-04-2018 3 7468.26
Customer-1 30-04-2018 13 11381.64
Customer-1 27-04-2018 5 3872.91
Customer-1 13-04-2018 7 7513.99
Customer-1 06-04-2018 8 7410.30
Customer-1 20-04-2018 4 3726.77
Customer-1 05-04-2018 1 395.69
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STEPS FOR DOING RFM ANALYSIS
Step-2: Assigning Recency Score
¡ Arrange Data in descending order basic
last purchase date.
¡ Assign 3 Score to top 33%customers
¡ Assign 2 Score to next 33%customers and
¡ Assign 1 Score to bottom 33%customers
Customer ID Last Date of
Purchase
(Recency)
Number of
Time
Purchase
(Frequency)
Total Sales
Value
(Monetary)
Recency
Customer-4 30-04-2018 13 11381.64 3
Customer-5 27-04-2018 5 3872.91 3
Customer-2 24-04-2018 4 1786.75 3
Customer-8 20-04-2018 4 3726.77 2
Customer-3 17-04-2018 3 7468.26 2
Customer-6 13-04-2018 7 7513.99 2
Customer-1 10-04-2018 6 2139.54 1
Customer-7 06-04-2018 8 7410.30 1
Customer-9 05-04-2018 1 395.69 1
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STEPS FOR DOING RFM ANALYSIS
Step-3: Assigning Frequency Score
¡ Arrange Data in descending order basic
number of purchase
¡ Assign 3 Score to top 33%customers
¡ Assign 2 Score to next 33%customers and
¡ Assign 1 Score to bottom 33%customers
Customer
ID
Last Date
of
Purchase
(Recency)
Number of
Time
Purchase
(Frequency
)
Total Sales
Value
(Monetary)
Recency
(R)
Frequency
(F)
Customer-4 30-04-2018 13 11381.64 3 3
Customer-7 06-04-2018 8 7410.30 1 3
Customer-6 13-04-2018 7 7513.99 2 3
Customer-1 10-04-2018 6 2139.54 1 2
Customer-5 27-04-2018 5 3872.91 3 2
Customer-2 24-04-2018 4 1786.75 3 2
Customer-8 20-04-2018 4 3726.77 2 1
Customer-3 17-04-2018 3 7468.26 2 1
Customer-9 05-04-2018 1 395.69 1 1
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STEPS FOR DOING RFM ANALYSIS
Customer ID Last Date of
Purchase
(Recency)
Number of Time
Purchase
(Frequency)
Total Sales
Value
(Monetary)
Recency
(R)
Frequency
(F)
Monetary
(M)
Customer-4 30-04-2018 13 11381.64 3 3 3
Customer-6 13-04-2018 7 7513.99 2 3 3
Customer-3 17-04-2018 3 7468.26 2 1 3
Customer-7 06-04-2018 8 7410.30 1 3 2
Customer-5 27-04-2018 5 3872.91 3 2 2
Customer-8 20-04-2018 4 3726.77 2 1 2
Customer-1 10-04-2018 6 2139.54 1 2 1
Customer-2 24-04-2018 4 1786.75 3 2 1
Customer-9 05-04-2018 1 395.69 1 1 1
Step-4: Assigning Monetary Score
A) Arrange Data in descending order basic Total Sales Value
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STEPS FOR DOING RFM ANALYSIS
Customer ID Last Date of
Purchase
(Recency)
Number of
Time Purchase
(Frequency)
Total Sales
Value
(Monetary)
Recency
(R)
Frequency
(F)
Monetary
(M)
FRM Score
Customer-4 30-04-2018 13 11381.64 3 3 3 333
Customer-6 13-04-2018 7 7513.99 2 3 3 233
Customer-3 17-04-2018 3 7468.26 2 1 3 213
Customer-7 06-04-2018 8 7410.30 1 3 2 132
Customer-5 27-04-2018 5 3872.91 3 2 2 322
Customer-8 20-04-2018 4 3726.77 2 1 2 212
Customer-1 10-04-2018 6 2139.54 1 2 1 121
Customer-2 24-04-2018 4 1786.75 3 2 1 231
Customer-9 05-04-2018 1 395.69 1 1 1 111
Step-5: FRM Score Calculation
Formula: R*100+F*10+M*1
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¡ Best Customer
¡ Loyalty Customer
¡ Big Customer
¡ New Customer
¡ Lost Customer
¡ Dead Customer
Step-6: Customer Classification Basic RFM Score
STEPS FOR DOING RFM ANALYSIS
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CUSTOMER LIFETIME VALUE (CLV) ANALYSIS
Customer Lifetime Value (CLV) is a crucial metric in sales analysis for distribution businesses. It helps determine the
value of a customer over the entire duration of their relationship with the company. By calculating CLV for different
customer segments, distribution businesses can identify their most valuable customers, make informed decisions about
resource allocation, and tailor their sales and marketing strategies to maximize profitability and customer satisfaction.
Calculate CLV for Each Segment: There are various approaches to calculating CLV, but one common method is using
the following formula:
CLV = (Average Order Value) × (Purchase Frequency) × (Customer Lifespan)
1. Average Order Value: The average revenue generated from a customer's purchase.
2. Purchase Frequency: The average number of purchases made by a customer within a specific time period.
3. Customer Lifespan: The average duration a customer remains loyal to the company.
