Analyzing Segment Performance
Understanding how different customer segments perform is essential for making informed business decisions. Segment performance analysis allows you to compare groups of customers based on shared characteristics and evaluate their contribution to your business using key metrics. This process helps you identify where to allocate resources, which segments to prioritize, and how to tailor your marketing and product strategies for maximum impact. By focusing on metrics such as revenue, retention, and order frequency, you gain actionable insights into which customer groups are most valuable and which may need targeted interventions.
1234567891011121314151617181920212223242526272829303132import pandas as pd # Sample customer transaction data data = { "customer_id": [1, 2, 3, 4, 5, 1, 2, 3, 4, 5], "segment": ["A", "B", "A", "B", "A", "A", "B", "A", "B", "A"], "order_id": [101, 102, 103, 104, 105, 106, 107, 108, 109, 110], "order_value": [200, 150, 120, 180, 220, 210, 170, 130, 160, 230], "order_date": pd.to_datetime([ "2023-01-10", "2023-01-12", "2023-01-14", "2023-01-15", "2023-01-16", "2023-02-10", "2023-02-12", "2023-02-14", "2023-02-15", "2023-02-16" ]) } df = pd.DataFrame(data) # Calculate revenue per segment revenue_per_segment = df.groupby("segment")["order_value"].sum() # Calculate retention per segment (number of unique customers with more than one order) orders_per_customer = df.groupby(["segment", "customer_id"]).size() retained_customers = orders_per_customer[orders_per_customer > 1].groupby("segment").count() # Calculate order frequency per segment (average orders per customer) orders_count = df.groupby(["segment", "customer_id"]).size().groupby("segment").mean() print("Revenue per segment:") print(revenue_per_segment) print("\nRetained customers per segment:") print(retained_customers) print("\nOrder frequency per segment:") print(orders_count)
123456789101112131415161718192021222324import seaborn as sns import matplotlib.pyplot as plt # Prepare data for plotting revenue per segment revenue_df = revenue_per_segment.reset_index() revenue_df.columns = ["segment", "revenue"] # Bar plot for revenue per segment sns.barplot(data=revenue_df, x="segment", y="revenue") plt.title("Revenue by Segment") plt.ylabel("Total Revenue") plt.xlabel("Segment") plt.show() # Prepare data for plotting order frequency per segment order_freq_df = orders_count.reset_index() order_freq_df.columns = ["segment", "order_frequency"] # Bar plot for order frequency per segment sns.barplot(data=order_freq_df, x="segment", y="order_frequency") plt.title("Order Frequency by Segment") plt.ylabel("Average Orders per Customer") plt.xlabel("Segment") plt.show()
Once you have measured segment performance using these metrics, you can leverage the insights to guide business strategy. High-performing segments—those that generate more revenue, have higher retention, or place more orders—may deserve increased marketing investment or exclusive offers. Segments with lower performance might benefit from targeted campaigns, onboarding improvements, or product changes to boost engagement. By regularly analyzing segment performance, you ensure that your marketing and product decisions are data-driven and aligned with your business goals, leading to more efficient resource allocation and better results.
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Understanding how different customer segments perform is essential for making informed business decisions. Segment performance analysis allows you to compare groups of customers based on shared characteristics and evaluate their contribution to your business using key metrics. This process helps you identify where to allocate resources, which segments to prioritize, and how to tailor your marketing and product strategies for maximum impact. By focusing on metrics such as revenue, retention, and order frequency, you gain actionable insights into which customer groups are most valuable and which may need targeted interventions.
1234567891011121314151617181920212223242526272829303132import pandas as pd # Sample customer transaction data data = { "customer_id": [1, 2, 3, 4, 5, 1, 2, 3, 4, 5], "segment": ["A", "B", "A", "B", "A", "A", "B", "A", "B", "A"], "order_id": [101, 102, 103, 104, 105, 106, 107, 108, 109, 110], "order_value": [200, 150, 120, 180, 220, 210, 170, 130, 160, 230], "order_date": pd.to_datetime([ "2023-01-10", "2023-01-12", "2023-01-14", "2023-01-15", "2023-01-16", "2023-02-10", "2023-02-12", "2023-02-14", "2023-02-15", "2023-02-16" ]) } df = pd.DataFrame(data) # Calculate revenue per segment revenue_per_segment = df.groupby("segment")["order_value"].sum() # Calculate retention per segment (number of unique customers with more than one order) orders_per_customer = df.groupby(["segment", "customer_id"]).size() retained_customers = orders_per_customer[orders_per_customer > 1].groupby("segment").count() # Calculate order frequency per segment (average orders per customer) orders_count = df.groupby(["segment", "customer_id"]).size().groupby("segment").mean() print("Revenue per segment:") print(revenue_per_segment) print("\nRetained customers per segment:") print(retained_customers) print("\nOrder frequency per segment:") print(orders_count)
123456789101112131415161718192021222324import seaborn as sns import matplotlib.pyplot as plt # Prepare data for plotting revenue per segment revenue_df = revenue_per_segment.reset_index() revenue_df.columns = ["segment", "revenue"] # Bar plot for revenue per segment sns.barplot(data=revenue_df, x="segment", y="revenue") plt.title("Revenue by Segment") plt.ylabel("Total Revenue") plt.xlabel("Segment") plt.show() # Prepare data for plotting order frequency per segment order_freq_df = orders_count.reset_index() order_freq_df.columns = ["segment", "order_frequency"] # Bar plot for order frequency per segment sns.barplot(data=order_freq_df, x="segment", y="order_frequency") plt.title("Order Frequency by Segment") plt.ylabel("Average Orders per Customer") plt.xlabel("Segment") plt.show()
Once you have measured segment performance using these metrics, you can leverage the insights to guide business strategy. High-performing segments—those that generate more revenue, have higher retention, or place more orders—may deserve increased marketing investment or exclusive offers. Segments with lower performance might benefit from targeted campaigns, onboarding improvements, or product changes to boost engagement. By regularly analyzing segment performance, you ensure that your marketing and product decisions are data-driven and aligned with your business goals, leading to more efficient resource allocation and better results.
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