House ads conversion by language

Now that you've ruled out natural fluctuations across the day of the week a user saw our marketing assets as they cause for decreasing house ads conversion, you will take a look at conversion by language over time. Perhaps the new marketing campaign does not apply broadly across different cultures.

Ideally, the marketing team will consider cultural differences prior to launching a campaign, but sometimes mistakes are made, and it will be your job to identify the cause. Often data scientists are the first line of defense to determine what went wrong with a marketing campaign. It's your job to think creatively to identify the cause.

This exercise is part of the course

Analyzing Marketing Campaigns with pandas

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Exercise instructions

Create a new DataFrame house_ads by isolating the rows from marketing where marketing_channel equals 'House Ads'.
Run your conversion_rate() function on the house_ads DataFrame grouping by date_served and language_displayed.
Use the plotting_conv() function on conv_lang_df to display your results.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Isolate the rows where marketing channel is House Ads
house_ads = ____

# Calculate conversion by date served, and language displayed
conv_lang_channel = ____

# Unstack conv_lang_channel
conv_lang_df = pd.DataFrame(conv_lang_channel.unstack(level=1))

# Use your plotting function to display results
____

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