1) Sales and Revenue Analysis
Scenario: A business analyzes monthly sales, revenue, and marketing expenses for a product over six months.
Data Example:
Month |
January |
February |
March |
April |
May |
June |
Sales (Units) |
1000 |
1200 |
1400 |
1600 |
1800 |
2000 |
Revenue (USD) |
50000 |
60000 |
70000 |
80000 |
90000 |
100000 |
Marketing Expenses (USD) |
10000 |
12000 |
15000 |
18000 |
20000 |
25000 |
Purpose: The multi axes line graph compares the sales, revenue, and marketing expenses over six months, helping the company identify trends and fluctuations in these key business metrics.
2) Hospital Patient Trends
Scenario: A hospital monitors patient admissions, average length of stay, and resource usage (e.g., beds) over six months to optimize operations.
Data Example:
Month |
January |
February |
March |
April |
May |
June |
Patient Admissions (No of Patients) |
1200 |
1100 |
1300 |
1400 |
1350 |
1250 |
Average Length of Stay (Days) |
5 |
4.8 |
5.2 |
5.5 |
5.3 |
5 |
Bed Usage (%) |
85 |
80 |
90 |
95 |
88 |
82 |
Purpose: To understand how patient flow and average length of stay impact bed availability and hospital resources, supporting improved patient care and operational efficiency.
3) Retail Store Performance
Scenario: A retail chain monitors weekly foot traffic, sales revenue, and average transaction value (ATV) over eight weeks to assess store performance.
Data Example:
Week |
Week 1 |
Week 2 |
Week 3 |
Week 4 |
Week 5 |
Week 6 |
Foot Traffic (Visitors) |
1000 |
1200 |
1300 |
1400 |
1500 |
1600 |
Sales Revenue (USD) |
20000 |
25000 |
27000 |
30000 |
33000 |
35000 |
ATV (USD) |
20 |
21 |
20.8 |
21.4 |
22 |
21.8 |
Purpose: To analyze the relationship between foot traffic, sales revenue, and average transaction value (ATV), helping the company assess store performance and identify trends that can boost sales.