Learn Generating Executive Summaries | Reporting and Visualization for Clients

Executive summaries play a crucial role in agency-client communication by providing a clear, high-level overview of campaign results and recommendations. Clients often do not have the time or need to review every detail within a full report. Instead, they rely on the executive summary to quickly understand what worked, what needs attention, and what actions are recommended. This concise summary helps clients make informed decisions without getting overwhelmed by data, and it reinforces the agency’s value by highlighting successes and strategic insights.


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import pandas as pd

# Sample campaign results DataFrame
data = {
    "Channel": ["Email", "Social", "Search", "Display"],
    "Impressions": [12000, 25000, 18000, 9000],
    "Clicks": [300, 1200, 900, 200],
    "Conversions": [25, 60, 40, 8],
    "Spend": [400, 1200, 900, 300]
}
df = pd.DataFrame(data)

# Summarize key findings
total_impressions = df["Impressions"].sum()
total_clicks = df["Clicks"].sum()
total_conversions = df["Conversions"].sum()
total_spend = df["Spend"].sum()
top_channel = df.loc[df["Conversions"].idxmax(), "Channel"]

# Format summary as bullet points
summary = [
    f"- Total impressions: {total_impressions:,}",
    f"- Total clicks: {total_clicks:,}",
    f"- Total conversions: {total_conversions:,}",
    f"- Total spend: ${total_spend:,}",
    f"- Top performing channel: {top_channel}"
]

for line in summary:
    print(line)

When creating an executive summary, it is essential to select the most important metrics that align with the client’s goals. These typically include total impressions, clicks, conversions, and spend, as well as identifying which channels or campaigns performed best. Presenting these metrics clearly—using bullet points, concise language, and highlighting standout results—ensures that clients can quickly grasp the most relevant insights. Avoiding unnecessary details keeps the summary focused and actionable.


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def generate_summary(client_name, df):
    total_impressions = df["Impressions"].sum()
    total_clicks = df["Clicks"].sum()
    total_conversions = df["Conversions"].sum()
    total_spend = df["Spend"].sum()
    top_channel = df.loc[df["Conversions"].idxmax(), "Channel"]
    summary = (
        f"Executive Summary for {client_name}:\n"
        f"- Total impressions: {total_impressions:,}\n"
        f"- Total clicks: {total_clicks:,}\n"
        f"- Total conversions: {total_conversions:,}\n"
        f"- Total spend: ${total_spend:,}\n"
        f"- Top performing channel: {top_channel}\n"
    )
    return summary

# Example use for two clients
client_a_data = {
    "Channel": ["Email", "Social"],
    "Impressions": [8000, 15000],
    "Clicks": [200, 700],
    "Conversions": [20, 50],
    "Spend": [300, 900]
}
client_b_data = {
    "Channel": ["Search", "Display"],
    "Impressions": [10000, 5000],
    "Clicks": [600, 150],
    "Conversions": [35, 5],
    "Spend": [700, 200]
}

df_a = pd.DataFrame(client_a_data)
df_b = pd.DataFrame(client_b_data)

print(generate_summary("Acme Corp", df_a))
print(generate_summary("Beta Inc", df_b))

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Section 3. Chapter 6

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import pandas as pd

# Sample campaign results DataFrame
data = {
    "Channel": ["Email", "Social", "Search", "Display"],
    "Impressions": [12000, 25000, 18000, 9000],
    "Clicks": [300, 1200, 900, 200],
    "Conversions": [25, 60, 40, 8],
    "Spend": [400, 1200, 900, 300]
}
df = pd.DataFrame(data)

# Summarize key findings
total_impressions = df["Impressions"].sum()
total_clicks = df["Clicks"].sum()
total_conversions = df["Conversions"].sum()
total_spend = df["Spend"].sum()
top_channel = df.loc[df["Conversions"].idxmax(), "Channel"]

# Format summary as bullet points
summary = [
    f"- Total impressions: {total_impressions:,}",
    f"- Total clicks: {total_clicks:,}",
    f"- Total conversions: {total_conversions:,}",
    f"- Total spend: ${total_spend:,}",
    f"- Top performing channel: {top_channel}"
]

for line in summary:
    print(line)


              12345678910111213141516171819202122232425262728293031323334353637
            
def generate_summary(client_name, df):
    total_impressions = df["Impressions"].sum()
    total_clicks = df["Clicks"].sum()
    total_conversions = df["Conversions"].sum()
    total_spend = df["Spend"].sum()
    top_channel = df.loc[df["Conversions"].idxmax(), "Channel"]
    summary = (
        f"Executive Summary for {client_name}:\n"
        f"- Total impressions: {total_impressions:,}\n"
        f"- Total clicks: {total_clicks:,}\n"
        f"- Total conversions: {total_conversions:,}\n"
        f"- Total spend: ${total_spend:,}\n"
        f"- Top performing channel: {top_channel}\n"
    )
    return summary

# Example use for two clients
client_a_data = {
    "Channel": ["Email", "Social"],
    "Impressions": [8000, 15000],
    "Clicks": [200, 700],
    "Conversions": [20, 50],
    "Spend": [300, 900]
}
client_b_data = {
    "Channel": ["Search", "Display"],
    "Impressions": [10000, 5000],
    "Clicks": [600, 150],
    "Conversions": [35, 5],
    "Spend": [700, 200]
}

df_a = pd.DataFrame(client_a_data)
df_b = pd.DataFrame(client_b_data)

print(generate_summary("Acme Corp", df_a))
print(generate_summary("Beta Inc", df_b))

Everything was clear?

Thanks for your feedback!

Section 3. Chapter 6

Generating Executive Summaries

1. What is the main goal of an executive summary?

2. How can Python help automate summary generation?

3. Why is it important to focus on key metrics in summaries?

Generating Executive Summaries

1. What is the main goal of an executive summary?

2. How can Python help automate summary generation?

3. Why is it important to focus on key metrics in summaries?