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Python for Customer Success Managers

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Understanding customer trends is crucial for Customer Success Managers who want to proactively engage customers and drive value. Data visualization helps you quickly identify patterns, shifts, and anomalies in customer behavior, making it easier to communicate findings to your team and stakeholders. By visualizing data, you can spot trends such as increased product usage, declining engagement, or the impact of new features. This chapter focuses on using Python's matplotlib library to create visualizations that highlight customer trends, enabling you to make informed, data-driven decisions and present insights clearly.


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import matplotlib.pyplot as plt
import pandas as pd

# Sample customer login data
data = {
    "date": pd.date_range(start="2024-06-01", periods=10, freq="D"),
    "logins": [15, 18, 22, 20, 30, 28, 25, 27, 22, 18]
}
df = pd.DataFrame(data)

# Plotting customer login frequency over time
plt.figure(figsize=(8, 4))
plt.plot(df["date"], df["logins"], marker="o", linestyle="-", color="blue")
plt.title("Customer Login Frequency Over Time")
plt.xlabel("Date")
plt.ylabel("Number of Logins")
plt.grid(True)
plt.tight_layout()
plt.show()

The line plot above shows how customer login frequency changes over a ten-day period. On the x-axis, you see the dates, and on the y-axis, the number of logins for each day. Peaks in the plot—such as the highest point on the fifth day—indicate days when customer engagement was especially high. Troughs, or low points, reveal days with fewer logins, which may require further investigation. By observing the overall trend, you can determine whether engagement is increasing, decreasing, or remaining steady. Consistent peaks might suggest successful customer outreach or product updates, while a downward trend could signal a need for intervention.


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import matplotlib.pyplot as plt

# Sample feature usage data by customer segment
segments = ["Basic", "Pro", "Enterprise"]
feature_usage = [120, 200, 340]

plt.figure(figsize=(6, 4))
plt.bar(segments, feature_usage, color=["orange", "green", "purple"])
plt.title("Feature Usage by Customer Segment")
plt.xlabel("Customer Segment")
plt.ylabel("Feature Usage Count")
plt.tight_layout()
plt.show()

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Abschnitt 1. Kapitel 3

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Abschnitt 1. Kapitel 3

Visualizing Customer Trends with Matplotlib

1. What type of chart is best for showing changes in customer engagement over time?

2. Which matplotlib function is used to create a line plot?

3. How can visualizations help Customer Success Managers make data-driven decisions?