Learn Constructing Conversion Funnels

Conversion funnels are a foundational tool in business analytics, helping you visualize and optimize the steps users take on their journey from initial interaction to a desired outcome, such as a purchase. In a typical digital product, users might first visit a website, then sign up for an account, and finally make a purchase. Each of these steps represents a stage in the funnel. By tracking how many users move from one stage to the next, you can identify where potential customers drop off and focus your efforts on improving those specific areas. This approach enables continuous optimization of the user experience and maximizes the efficiency of your business processes.


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import pandas as pd

# Simulate loading a product usage log dataset
data = {
    "user_id": [1, 1, 2, 2, 2, 3, 4, 4, 5],
    "event_type": [
        "visit", "signup", "visit", "signup", "purchase",
        "visit", "visit", "signup", "visit"
    ],
    "timestamp": [
        "2024-06-01 10:00", "2024-06-01 10:05", "2024-06-01 11:00",
        "2024-06-01 11:10", "2024-06-01 11:30", "2024-06-01 12:00",
        "2024-06-01 13:00", "2024-06-01 13:10", "2024-06-01 14:00"
    ]
}

logs = pd.DataFrame(data)
logs["timestamp"] = pd.to_datetime(logs["timestamp"])
print(logs)


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# Identify the funnel stages for each user
stages = ["visit", "signup", "purchase"]

# Get the unique stages completed by each user
user_stages = logs.groupby("user_id")["event_type"].unique().reset_index()

# Build a table: for each stage, count users who reached it
funnel_counts = {}
for stage in stages:
    funnel_counts[stage] = user_stages["event_type"].apply(lambda x: stage in x).sum()

# Convert to DataFrame for easy viewing
funnel_df = pd.DataFrame(list(funnel_counts.items()), columns=["Stage", "User Count"])
print(funnel_df)

Funnel drop-offs are revealed by the decrease in user counts from one stage to the next. For example, if 100 users visit your site but only 40 sign up, the drop-off between these stages is 60%. Such drop-offs pinpoint friction or barriers in your user experience, such as a confusing signup process or lack of perceived value. By measuring and analyzing these drop-offs, you can prioritize improvements that will have the greatest impact on conversion rates and overall business performance.

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Section 2. Chapter 1

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              12345678910111213141516171819
            
import pandas as pd

# Simulate loading a product usage log dataset
data = {
    "user_id": [1, 1, 2, 2, 2, 3, 4, 4, 5],
    "event_type": [
        "visit", "signup", "visit", "signup", "purchase",
        "visit", "visit", "signup", "visit"
    ],
    "timestamp": [
        "2024-06-01 10:00", "2024-06-01 10:05", "2024-06-01 11:00",
        "2024-06-01 11:10", "2024-06-01 11:30", "2024-06-01 12:00",
        "2024-06-01 13:00", "2024-06-01 13:10", "2024-06-01 14:00"
    ]
}

logs = pd.DataFrame(data)
logs["timestamp"] = pd.to_datetime(logs["timestamp"])
print(logs)


              1234567891011121314
            
# Identify the funnel stages for each user
stages = ["visit", "signup", "purchase"]

# Get the unique stages completed by each user
user_stages = logs.groupby("user_id")["event_type"].unique().reset_index()

# Build a table: for each stage, count users who reached it
funnel_counts = {}
for stage in stages:
    funnel_counts[stage] = user_stages["event_type"].apply(lambda x: stage in x).sum()

# Convert to DataFrame for easy viewing
funnel_df = pd.DataFrame(list(funnel_counts.items()), columns=["Stage", "User Count"])
print(funnel_df)

Everything was clear?

Thanks for your feedback!

Section 2. Chapter 1