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()

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

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}

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

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).

Practice

1. What is the main purpose of a marketing KPI dashboard?

easy

A. To create complex formulas for data analysis only

B. To store raw marketing data without visualization

C. To display key marketing numbers clearly in one place

D. To replace all marketing reports with text summaries

Solution

1. Step 1: Understand the role of a dashboard

   A dashboard is designed to show important information clearly and quickly.

2. Step 2: Identify the main function in marketing context

   Marketing KPI dashboards focus on showing key numbers in one place for easy review.

3. Final Answer:

   To display key marketing numbers clearly in one place -> Option C

4. Quick Check:

   Dashboard = Clear key numbers [OK]

Hint: Dashboards summarize key info visually [OK]

Common Mistakes:

• Confusing dashboards with raw data storage
• Thinking dashboards only store data without visuals
• Assuming dashboards replace all reports with text

2. Which of the following is the correct way to add a filter for 'Date' in a marketing dashboard tool?

easy

A. Create a text box with date values

B. Write a SQL query to delete dates from the data

C. Use a pie chart to show dates

D. Add a slicer visual and connect it to the Date field

Solution

1. Step 1: Identify how filters work in dashboards

   Filters let users select data subsets, often using slicers connected to fields.

2. Step 2: Choose the correct method for date filtering

   Adding a slicer visual connected to the Date field allows interactive filtering by date.

3. Final Answer:

   Add a slicer visual and connect it to the Date field -> Option D

4. Quick Check:

   Filter by slicer on Date [OK]

Hint: Use slicers for interactive filters [OK]

Common Mistakes:

• Deleting data instead of filtering
• Using pie charts to filter data
• Using text boxes instead of filter controls

3. Given a dashboard showing total website visits by channel, if the data is: Organic=5000, Paid=3000, Referral=2000, what will the pie chart show for Paid visits percentage?

medium

A. 30%

B. 25%

C. 50%

D. 20%

Solution

1. Step 1: Calculate total visits

   Total = 5000 (Organic) + 3000 (Paid) + 2000 (Referral) = 10000 visits.

2. Step 2: Calculate Paid visits percentage

   Paid % = (3000 / 10000) * 100 = 30%.

3. Final Answer:

   30% -> Option A

4. Quick Check:

   Paid visits = 30% of total [OK]

Hint: Divide part by total, multiply by 100 [OK]

Common Mistakes:

• Adding percentages instead of calculating
• Using wrong total for percentage
• Confusing counts with percentages

4. You created a dashboard but the filter for 'Campaign' does not change the charts. What is the most likely cause?

medium

A. The data source is offline

B. The filter is not connected to the data model

C. The dashboard has too many visuals

D. The charts are using the wrong color scheme

Solution

1. Step 1: Understand filter connections

   Filters must be linked to the data model fields used in visuals to affect them.

2. Step 2: Identify why filter has no effect

   If the filter is not connected properly, selecting values won't update charts.

3. Final Answer:

   The filter is not connected to the data model -> Option B

4. Quick Check:

   Filter connection missing = no effect [OK]

Hint: Check filter connections to data fields [OK]

Common Mistakes:

• Blaming colors for filter issues
• Assuming too many visuals cause filter failure
• Ignoring filter-data model link

5. You want to create a dashboard showing monthly revenue and conversion rate by marketing channel with a filter for last 6 months. Which combination is best?

hard

A. Use a line chart for monthly revenue, a card for conversion rate, and a date slicer for last 6 months

B. Use a pie chart for monthly revenue, a table for conversion rate, and no filter

C. Use a bar chart for total revenue only, a text box for conversion rate, and a filter for all time

D. Use a scatter plot for revenue and conversion, and a slicer for last year

Solution

1. Step 1: Choose visuals matching data types

   Line charts show trends over time well; cards highlight single KPIs like conversion rate.

2. Step 2: Select appropriate filter for time range

   A date slicer filtered to last 6 months allows focused analysis on recent data.

3. Final Answer:

   Use a line chart for monthly revenue, a card for conversion rate, and a date slicer for last 6 months -> Option A

4. Quick Check:

   Visuals + filter match data needs [OK]

Hint: Match chart type to data and add time slicer [OK]

Common Mistakes:

• Using pie charts for time series data
• Not adding time filters
• Choosing visuals that don't highlight KPIs clearly