Leer Drivers of Revenue Growth & Decline

Understanding what drives revenue growth or decline is essential for making informed business decisions. Revenue driver analysis involves breaking down total revenue changes into their underlying causes, such as shifts in product sales, regional performance, or customer segments. Businesses use these insights to identify which areas are contributing most to growth, spot early signs of decline, and prioritize initiatives for improvement. By quantifying the impact of different drivers, you can more effectively allocate resources, set targets, and monitor the results of strategic actions.


              123456789101112131415161718
            
import pandas as pd

# Sample revenue data by product and period
data = {
    "period": ["2023-Q1", "2023-Q1", "2023-Q2", "2023-Q2"],
    "product": ["A", "B", "A", "B"],
    "revenue": [50000, 30000, 65000, 25000]
}
df = pd.DataFrame(data)

# Pivot to get periods as columns and products as rows
pivot = df.pivot(index="product", columns="period", values="revenue")

# Calculate period-over-period revenue change by product
pivot["change_Q2_vs_Q1"] = pivot["2023-Q2"] - pivot["2023-Q1"]
pivot["change_pct"] = (pivot["change_Q2_vs_Q1"] / pivot["2023-Q1"]) * 100

print(pivot)


              12345678910111213141516171819202122
            
import matplotlib.pyplot as plt

# Continuing from the previous pivot DataFrame
products = pivot.index.tolist()
revenue_q1 = pivot["2023-Q1"].tolist()
revenue_q2 = pivot["2023-Q2"].tolist()
changes = pivot["change_Q2_vs_Q1"].tolist()

fig, ax = plt.subplots(figsize=(8, 5))

# Plot revenue for each period
ax.bar(products, revenue_q1, label="2023-Q1", alpha=0.7)
ax.bar(products, revenue_q2, label="2023-Q2", alpha=0.7, bottom=revenue_q1)

# Highlight top contributor to growth or decline
top_contributor = products[changes.index(max(changes))]
ax.bar(top_contributor, revenue_q2[products.index(top_contributor)], color="orange", label="Top Contributor")

ax.set_ylabel("Revenue ($)")
ax.set_title("Revenue Trends by Product")
ax.legend()
plt.show()

By analyzing revenue changes at the product or regional level, you can pinpoint the main sources of growth or decline. These insights allow you to adjust business strategies, such as focusing marketing efforts on high-growth products or addressing issues in declining regions. Regularly monitoring revenue drivers helps you react quickly to market changes, optimize resource allocation, and set data-driven targets for future periods.

Was alles duidelijk?

Bedankt voor je feedback!

Sectie 4. Hoofdstuk 2

Vraag AI

Vraag wat u wilt of probeer een van de voorgestelde vragen om onze chat te starten.

Suggested prompts:

Can you explain how the revenue change percentages are calculated?

How can I adapt this analysis for more products or additional periods?

What are some common pitfalls to avoid when interpreting revenue driver analysis?

Veeg om het menu te tonen


              123456789101112131415161718
            
import pandas as pd

# Sample revenue data by product and period
data = {
    "period": ["2023-Q1", "2023-Q1", "2023-Q2", "2023-Q2"],
    "product": ["A", "B", "A", "B"],
    "revenue": [50000, 30000, 65000, 25000]
}
df = pd.DataFrame(data)

# Pivot to get periods as columns and products as rows
pivot = df.pivot(index="product", columns="period", values="revenue")

# Calculate period-over-period revenue change by product
pivot["change_Q2_vs_Q1"] = pivot["2023-Q2"] - pivot["2023-Q1"]
pivot["change_pct"] = (pivot["change_Q2_vs_Q1"] / pivot["2023-Q1"]) * 100

print(pivot)


              12345678910111213141516171819202122
            
import matplotlib.pyplot as plt

# Continuing from the previous pivot DataFrame
products = pivot.index.tolist()
revenue_q1 = pivot["2023-Q1"].tolist()
revenue_q2 = pivot["2023-Q2"].tolist()
changes = pivot["change_Q2_vs_Q1"].tolist()

fig, ax = plt.subplots(figsize=(8, 5))

# Plot revenue for each period
ax.bar(products, revenue_q1, label="2023-Q1", alpha=0.7)
ax.bar(products, revenue_q2, label="2023-Q2", alpha=0.7, bottom=revenue_q1)

# Highlight top contributor to growth or decline
top_contributor = products[changes.index(max(changes))]
ax.bar(top_contributor, revenue_q2[products.index(top_contributor)], color="orange", label="Top Contributor")

ax.set_ylabel("Revenue ($)")
ax.set_title("Revenue Trends by Product")
ax.legend()
plt.show()

Was alles duidelijk?

Bedankt voor je feedback!

Sectie 4. Hoofdstuk 2