Lära Drop-Off Analysis and Bottleneck Detection

Understanding where users or customers drop out of a process is essential for optimizing conversions and improving business outcomes. Drop-off analysis quantifies the proportion of users who fail to move from one stage of a funnel to the next. By measuring and analyzing these drop-offs, you can pinpoint exactly where the largest losses occur, allowing you to focus your efforts on the stages that most impact your overall conversion rate. This approach is crucial for businesses aiming to maximize the efficiency of their sales, onboarding, or engagement funnels.


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import pandas as pd

# Example funnel data: number of users at each stage
data = {
    "Stage": ["Visited Site", "Signed Up", "Activated", "Made Purchase"],
    "Users": [1000, 600, 350, 120]
}
funnel = pd.DataFrame(data)

# Compute drop-off rates and conversion percentages between stages
funnel["Drop-Off"] = funnel["Users"].shift(1) - funnel["Users"]
funnel["Drop-Off Rate (%)"] = (funnel["Drop-Off"] / funnel["Users"].shift(1) * 100).round(2)
funnel["Stage Conversion (%)"] = (funnel["Users"] / funnel["Users"].shift(1) * 100).round(2)

# Fill NaN for the first stage
funnel.fillna({"Drop-Off": 0, "Drop-Off Rate (%)": 0, "Stage Conversion (%)": 100}, inplace=True)

print(funnel)


              12345678910111213141516
            
import pandas as pd

# Using the same funnel DataFrame as above
data = {
    "Stage": ["Visited Site", "Signed Up", "Activated", "Made Purchase"],
    "Users": [1000, 600, 350, 120]
}
funnel = pd.DataFrame(data)
funnel["Drop-Off"] = funnel["Users"].shift(1) - funnel["Users"]

# Identify the bottleneck: stage with the largest drop-off
bottleneck_idx = funnel["Drop-Off"].idxmax()
bottleneck_stage = funnel.loc[bottleneck_idx, "Stage"]
bottleneck_value = funnel.loc[bottleneck_idx, "Drop-Off"]

print(f"The bottleneck is at '{bottleneck_stage}' with a drop-off of {int(bottleneck_value)} users.")

Once you have identified the bottleneck stage with the highest user drop-off, it is important to address the underlying causes. Strategies for tackling bottlenecks include improving user experience or messaging at the problematic stage, simplifying forms or requirements, providing clearer incentives, or offering additional support such as live chat or tutorials. Funnel analytics empower you to test and measure the impact of these interventions, ensuring that resources are focused on the stages with the greatest potential to lift overall conversion rates.

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Avsnitt 2. Kapitel 2

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              12345678910111213141516171819
            
import pandas as pd

# Example funnel data: number of users at each stage
data = {
    "Stage": ["Visited Site", "Signed Up", "Activated", "Made Purchase"],
    "Users": [1000, 600, 350, 120]
}
funnel = pd.DataFrame(data)

# Compute drop-off rates and conversion percentages between stages
funnel["Drop-Off"] = funnel["Users"].shift(1) - funnel["Users"]
funnel["Drop-Off Rate (%)"] = (funnel["Drop-Off"] / funnel["Users"].shift(1) * 100).round(2)
funnel["Stage Conversion (%)"] = (funnel["Users"] / funnel["Users"].shift(1) * 100).round(2)

# Fill NaN for the first stage
funnel.fillna({"Drop-Off": 0, "Drop-Off Rate (%)": 0, "Stage Conversion (%)": 100}, inplace=True)

print(funnel)


              12345678910111213141516
            
import pandas as pd

# Using the same funnel DataFrame as above
data = {
    "Stage": ["Visited Site", "Signed Up", "Activated", "Made Purchase"],
    "Users": [1000, 600, 350, 120]
}
funnel = pd.DataFrame(data)
funnel["Drop-Off"] = funnel["Users"].shift(1) - funnel["Users"]

# Identify the bottleneck: stage with the largest drop-off
bottleneck_idx = funnel["Drop-Off"].idxmax()
bottleneck_stage = funnel.loc[bottleneck_idx, "Stage"]
bottleneck_value = funnel.loc[bottleneck_idx, "Drop-Off"]

print(f"The bottleneck is at '{bottleneck_stage}' with a drop-off of {int(bottleneck_value)} users.")

Var allt tydligt?

Tack för dina kommentarer!

Avsnitt 2. Kapitel 2