Calculating conversion rate
In this exercise, you will practice calculating conversion rate, which is often the first metric you'll want to calculate when evaluating how a marketing campaign performed.
On marketing teams, conversion rate is typically the most important metric. It is one of the best ways to determine how effective a marketing team was at gaining new customers.
As a reminder, conversion rate is the percentage of the users who saw our marketing assets and subsequently became subscribers.
The formula for conversion rate is:
$$ \frac{\text{Number of people who convert}}{\text{Total number of people who we market to}} $$
This is a part of the course
“Analyzing Marketing Campaigns with pandas”
Exercise instructions
- Calculate the number of unique
user_ids inmarketingDataFrame. - Calculate the number of people who subscribed using the
convertedcolumn. - Calculate the conversion rate.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Calculate the number of people we marketed to
total = ____
# Calculate the number of people who subscribed
subscribers = marketing[____]['user_id'].____
# Calculate the conversion rate
conversion_rate = ____
print(round(conversion_rate*100, 2), "%")This exercise is part of the course
Analyzing Marketing Campaigns with pandas
Build up your pandas skills and answer marketing questions by merging, slicing, visualizing, and more!
In this chapter, you will learn about common marketing metrics and how to calculate them using pandas. You will also visualize your results and practice user segmentation.
Exercise 1: Introduction to common marketing metricsExercise 2: Calculating conversion rateExercise 3: Calculating retention rateExercise 4: Customer segmentationExercise 5: Comparing language conversion rate (I)Exercise 6: Comparing language conversion rate (II)Exercise 7: Aggregating by dateExercise 8: Plotting campaign results (I)Exercise 9: Visualize conversion rate by languageExercise 10: Creating daily conversion rate DataFrameExercise 11: Setting up our data to visualize daily conversionExercise 12: Visualize daily conversion rateExercise 13: Plotting campaign results (II)Exercise 14: Marketing channels across age groupsExercise 15: Grouping and counting by multiple columnsExercise 16: Analyzing retention rates for the campaignWhat is DataCamp?
Learn the data skills you need online at your own pace—from non-coding essentials to data science and machine learning.