1) Product Sales Comparison
Scenario: An e-commerce business owner wants to track the sales trends of product A, product B, and product C over six months.
Data Example (in Millions):
Month |
January |
February |
March |
April |
May |
June |
Product A |
300 |
400 |
500 |
600 |
700 |
900 |
Product B |
400 |
500 |
600 |
700 |
800 |
1000 |
Product C |
600 |
700 |
900 |
1100 |
1200 |
1400 |
Product D |
800 |
900 |
1200 |
1300 |
1500 |
2000 |
Product E |
1000 |
1200 |
1500 |
2200 |
2300 |
2500 |
Purpose: The graph helps the owner optimize inventory and marketing strategies by identifying top-performing products and seasonal trends.
To visualize sales trends for multiple products, click to download the
file and plot it using a compound line graph.
2) Electricity Consumption and Production
Scenario: An energy analyst tracks daily electricity consumption and production throughout the week to observe short-term energy supply and demand trends.
Data Example (in MWh):
Month |
January |
February |
March |
April |
May |
June |
Consumption |
1200 |
1300 |
1400 |
1500 |
1600 |
1750 |
Production |
1000 |
1100 |
1200 |
1300 |
1400 |
1500 |
Surplus/Deficit |
-200 |
-200 |
-200 |
-200 |
-200 |
-250 |
Average Consumption |
1200 |
1300 |
1400 |
1500 |
1600 |
1750 |
Average Production |
1000 |
1100 |
1200 |
1300 |
1400 |
1500 |
Purpose: The compound line graph tracks daily electricity consumption and production, revealing demand spikes and helping optimize energy management.
Download the
file to evaluate electricity consumption and production using a compound line graph.
3) Website Traffic Comparison
Scenario: A digital marketer wants to compare the website traffic across different marketing campaigns over six months.
Data Example (in Number of Visitors):
Month |
January |
February |
March |
April |
May |
June |
Campaign A |
500 |
600 |
700 |
900 |
1000 |
1200 |
Campaign B |
800 |
900 |
1100 |
1400 |
1700 |
1900 |
Campaign C |
1000 |
1100 |
1300 |
1500 |
1700 |
2000 |
Campaign D |
1500 |
1600 |
1800 |
2200 |
2500 |
2800 |
Campaign E |
2000 |
2200 |
2500 |
3000 |
3500 |
4000 |
Purpose: A compound line graph would allow the digital marketer to compare the performance of all three campaigns simultaneously, helping them determine which campaign generated the most traffic and how traffic trends changed over time.
Retrieve the
file to analyze the traffic performance of various campaigns with a compound line graph.