Comparative & Trend Analysis

The formula is: ((New Value - Old Value) / Old Value) × 100 = (($60K - $50K) / $50K) × 100 = ($10K / $50K) × 100 = 0.20 × 100 = 20%. This shows sales grew by 20%, which is the relative change. Remember: the $10K difference is the absolute change, while 20% is the relative change that provides context for the magnitude of the increase.

Year-over-year comparison accounts for seasonality by comparing the same time period across different years. December often has higher sales due to holidays, so comparing December to January (month-over-month) would be misleading—the drop would reflect normal seasonal patterns, not actual business performance. Always use year-over-year comparisons when strong seasonal patterns exist.

Seasonal trends are predictable, calendar-based patterns that repeat at regular intervals, such as retail sales spiking in November-December for holidays, ice cream sales peaking in summer, or restaurant traffic increasing on weekends. Cyclical trends, by contrast, are longer economic cycles (recession, recovery, expansion) that aren't tied to specific calendar periods and can last several years.

Time series decomposition separates data into three components: 1) Trend (the long-term upward, downward, or stable direction), 2) Seasonality (repeating patterns tied to calendar periods like months or days of the week), and 3) Irregularities (random noise or one-time events like a pandemic spike). Understanding these components helps you distinguish between real business changes and expected fluctuations.

Relative revenue difference = (($12K - $10K) / $10K) × 100 = ($2K / $10K) × 100 = 20%. The question specifically asks for the relative difference, which is the percentage. Option A ($2,000 more) is the absolute difference. Both measures are important: absolute shows the dollar impact, while relative provides context for proportional magnitude. Always include both when doing comparative analysis.

Benchmarking compares your performance to a reference point or standard. Internal benchmarks include your own targets, past performance, or budgeted values. External benchmarks include industry averages, competitors, or best practices. For example: "Our customer satisfaction is 4.2/5, above the industry average of 3.8/5" or "We sold 5,000 units, exceeding our target of 4,500 by 11%." Benchmarking provides essential context to interpret whether your numbers represent good or poor performance.

This is the classic correlation vs. causation mistake. Both ice cream sales and crime rates increase in summer, but that's because they're both influenced by a third factor: warm weather. When it's warm, people are outside more (leading to more interactions and crime), and they also buy more ice cream. Ice cream doesn't cause crime—they just happen to move together due to a common underlying factor. Always look for true causal relationships, not just patterns that move together.

Sales consistently increase each month from $40K in January to $48K in May, showing a clear upward trend. The increase is steady (approximately $2K per month), indicating consistent growth rather than random fluctuation. This type of trend suggests the business is expanding, and if it continues, you can use it to forecast future sales and plan for increased inventory, staffing, or capacity needs.

Absolute comparison shows raw numbers (e.g., "$2,000 more" or "200 additional customers"), while relative comparison shows percentages (e.g., "20% more" or "25% growth") that provide context for the magnitude of change. Both are valuable: absolute tells you the actual dollar or unit impact, while relative helps you understand proportional significance. For example, a $10,000 increase could be 1% growth (large in absolute terms, small relatively) or 100% growth (doubling your business).

A cohort is a group defined by a shared characteristic, typically a time-based event like "all users who signed up in January 2024" or "customers acquired during Q1." Cohort analysis tracks how these groups behave over time, helping you compare retention, engagement, or purchasing patterns across different acquisition periods. For example, you might discover that Q1 customers have better 6-month retention than Q2 customers, revealing insights about seasonal quality differences or campaign effectiveness.