Dashboard creation for marketing KPIs in Digital Marketing - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a KPI card showing total website visits.

Digital Marketing

total_visits = df['[1]'].sum()

Drag options to blanks, or click blank then click option'

Aclicks

Bwebsite_visits

Cbounce_rate

Dconversion_rate

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'clicks' instead of visits

Using 'bounce_rate' which is a percentage, not a count

The column website_visits contains the total visits data, so summing it gives total visits.

2fill in blank

medium

Complete the code to calculate the average conversion rate.

Digital Marketing

avg_conversion = df['[1]'].mean()

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Abounce_rate

Bclicks

Cimpressions

Dconversion_rate

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'clicks' which is a count, not a rate

Using 'bounce_rate' which is a different metric

The conversion_rate column holds the conversion percentages, so averaging it gives the average conversion rate.

3fill in blank

hard

Fix the error in the code to filter data for the last 30 days.

Digital Marketing

recent_data = df[df['date'] [1] pd.Timestamp.today() - pd.Timedelta(days=30)]

Drag options to blanks, or click blank then click option'

A!=

B==

C>=

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<=' which selects older dates

Using '==' which selects only one date

To get data from the last 30 days, we select dates greater than or equal to today minus 30 days.

4fill in blank

hard

Fill both blanks to create a dictionary of average clicks and impressions per campaign.

Digital Marketing

avg_metrics = {campaign: {'clicks': [1], 'impressions': [2] for campaign in campaigns}

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Adf[df['campaign'] == campaign]['clicks'].mean()

Bdf[df['campaign'] == campaign]['bounce_rate'].mean()

Cdf[df['campaign'] == campaign]['impressions'].mean()

Ddf[df['campaign'] == campaign]['conversion_rate'].mean()

Attempts:

3 left

💡 Hint

Common Mistakes

Using bounce_rate or conversion_rate instead of clicks or impressions

Mixing up clicks and impressions columns

We calculate the mean clicks and impressions for each campaign by filtering the dataframe and taking the mean of respective columns.

5fill in blank

hard

Fill all three blanks to create a KPI summary with total visits, average conversion rate, and max bounce rate.

Digital Marketing

kpi_summary = {'total_visits': [1], 'avg_conversion': [2], 'max_bounce': [3]

Drag options to blanks, or click blank then click option'

Adf['website_visits'].sum()

Bdf['conversion_rate'].mean()

Cdf['bounce_rate'].max()

Ddf['clicks'].sum()

Attempts:

3 left

💡 Hint

Common Mistakes

Using clicks instead of website_visits for total visits

Using min instead of max for bounce rate

The KPI summary dictionary holds total visits (sum of website_visits), average conversion rate (mean of conversion_rate), and maximum bounce rate (max of bounce_rate).