Impara Revenue by Segment/Product/Region

Understanding how revenue is distributed across products, customer segments, and geographic regions is fundamental for making informed business decisions. Revenue breakdowns reveal which products are driving growth, which customer groups are most valuable, and which markets offer the greatest potential. This insight enables you to prioritize investments, tailor marketing strategies, and allocate resources effectively to maximize returns and manage risk.


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import pandas as pd

# Simulated sales data
data = {
    "order_id": [1001, 1002, 1003, 1004, 1005, 1006],
    "product": ["Widget", "Gadget", "Widget", "Gizmo", "Gadget", "Gizmo"],
    "segment": ["Retail", "Enterprise", "Retail", "Retail", "Enterprise", "Enterprise"],
    "region": ["North", "South", "East", "West", "North", "South"],
    "revenue": [1200, 2500, 1500, 800, 2700, 1100],
    "date": [
        "2024-01-05", "2024-01-06", "2024-01-07",
        "2024-01-08", "2024-01-09", "2024-01-10"
    ]
}

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


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# Aggregate revenue by product
revenue_by_product = df.groupby("product")["revenue"].sum()
print("Revenue by product:\n", revenue_by_product)

# Aggregate revenue by segment
revenue_by_segment = df.groupby("segment")["revenue"].sum()
print("\nRevenue by segment:\n", revenue_by_segment)

# Aggregate revenue by region using pivot_table
revenue_by_region = pd.pivot_table(df, values="revenue", index="region", aggfunc="sum")
print("\nRevenue by region:\n", revenue_by_region)

When you analyze revenue concentration, you identify if a large portion of revenue comes from a few products, segments, or regions. High concentration may indicate dependency risks, where losing a key customer group or market could significantly impact the business. On the other hand, diversification—where revenue is spread across many products or markets—can make your business more resilient to changes. Interpreting these patterns helps you spot opportunities to expand successful areas or address vulnerabilities in your revenue streams.

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Sezione 4. Capitolo 1

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Suggested prompts:

Can you explain how to interpret the revenue breakdown results?

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              1234567891011121314151617
            
import pandas as pd

# Simulated sales data
data = {
    "order_id": [1001, 1002, 1003, 1004, 1005, 1006],
    "product": ["Widget", "Gadget", "Widget", "Gizmo", "Gadget", "Gizmo"],
    "segment": ["Retail", "Enterprise", "Retail", "Retail", "Enterprise", "Enterprise"],
    "region": ["North", "South", "East", "West", "North", "South"],
    "revenue": [1200, 2500, 1500, 800, 2700, 1100],
    "date": [
        "2024-01-05", "2024-01-06", "2024-01-07",
        "2024-01-08", "2024-01-09", "2024-01-10"
    ]
}

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


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# Aggregate revenue by product
revenue_by_product = df.groupby("product")["revenue"].sum()
print("Revenue by product:\n", revenue_by_product)

# Aggregate revenue by segment
revenue_by_segment = df.groupby("segment")["revenue"].sum()
print("\nRevenue by segment:\n", revenue_by_segment)

# Aggregate revenue by region using pivot_table
revenue_by_region = pd.pivot_table(df, values="revenue", index="region", aggfunc="sum")
print("\nRevenue by region:\n", revenue_by_region)

Tutto è chiaro?

Grazie per i tuoi commenti!

Sezione 4. Capitolo 1