Apprendre Understanding Customer Segmentation | Growth, Marketing, and Customer Insights

Understanding who your customers are and how they differ is essential for any startup aiming to grow efficiently. Customer segmentation is the process of dividing your customer base into groups that share similar characteristics, such as age, spending habits, or product preferences. By tailoring your marketing and product strategies to specific segments, you can improve engagement, increase sales, and allocate resources more effectively. Python makes it easy to analyze and segment your customer data, allowing you to quickly identify patterns and opportunities.


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import pandas as pd

# Sample customer data
data = {
    "customer_id": [1, 2, 3, 4, 5, 6],
    "age": [25, 40, 22, 35, 28, 45],
    "spend": [120, 340, 80, 260, 150, 400],
    "segment": ["Young", "Professional", "Young", "Professional", "Young", "Professional"]
}

customers = pd.DataFrame(data)
print(customers)

With a customer DataFrame available, you can use pandas to group your data by segments and summarize key metrics. Grouping customers by segment helps you see patterns in behavior and preferences within each group. For example, you might want to know how much each segment spends on average or how many customers belong to each segment. This information helps you make informed decisions about where to focus your marketing efforts or how to develop your product offerings.


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# Calculate average spend per segment
avg_spend_per_segment = customers.groupby("segment")["spend"].mean()
print(avg_spend_per_segment)

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Section 3. Chapitre 1

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Suggested prompts:

Can you explain how to interpret the average spend per segment?

What other metrics can I calculate using this data?

How can I visualize the spending differences between segments?

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              123456789101112
            
import pandas as pd

# Sample customer data
data = {
    "customer_id": [1, 2, 3, 4, 5, 6],
    "age": [25, 40, 22, 35, 28, 45],
    "spend": [120, 340, 80, 260, 150, 400],
    "segment": ["Young", "Professional", "Young", "Professional", "Young", "Professional"]
}

customers = pd.DataFrame(data)
print(customers)


              123
            
# Calculate average spend per segment
avg_spend_per_segment = customers.groupby("segment")["spend"].mean()
print(avg_spend_per_segment)

Tout était clair ?

Merci pour vos commentaires !

Section 3. Chapitre 1

Understanding Customer Segmentation

1. Why is customer segmentation important for startups?

2. How can pandas help group customers by segment?

3. What insight can be gained from average spend per segment?

Understanding Customer Segmentation

1. Why is customer segmentation important for startups?

2. How can pandas help group customers by segment?

3. What insight can be gained from average spend per segment?