Common Analysis Scenarios

Business, Marketing, Operations, and Personal Analysis

This chapter walks through analysis approaches for common real-world scenarios.

Sales Analysis

Key Questions

  • What's selling? (products, categories, regions)
  • Who's buying? (customer segments)
  • When do sales peak? (time patterns)
  • What's changing? (trends, growth/decline)
  • Why? (drivers of performance)

Common Analyses

Revenue breakdown: Sales by product, category, region, channel

Trend analysis: Month-over-month, year-over-year changes

Customer analysis: New vs. returning, average order value, purchase frequency

Product performance: Best/worst sellers, margin analysis, cross-selling patterns

AI Prompt: Sales Analysis

Help me analyze sales data.

Data includes: [Describe columns — date, product, customer, amount, etc.]
Time period: [Date range]
Key questions: [What you need to understand]

Please analyze:
1. Overall performance and trends
2. Top/bottom performers
3. Notable patterns or changes
4. Segment differences
5. Areas for deeper investigation

Marketing Analysis

Key Questions

  • Which channels drive results?
  • What's our cost per acquisition?
  • What's the return on marketing spend?
  • Which campaigns perform best?
  • How is customer awareness/behavior changing?

Common Analyses

Channel attribution: Which touchpoints drive conversions

Campaign performance: Results by campaign, creative, audience

Funnel analysis: Conversion at each stage

Customer journey: How customers move from awareness to purchase

AI Prompt: Marketing Analysis

Help me analyze marketing performance.

Data includes: [Describe — campaign, channel, spend, impressions, clicks, conversions, etc.]
Goal: [What you're trying to understand]

Please analyze:
1. Channel/campaign performance comparison
2. Efficiency metrics (CPA, ROAS, conversion rate)
3. Trends over time
4. Opportunities for improvement
5. Recommendations

Customer Analysis

Key Questions

  • Who are our best customers?
  • What predicts customer value?
  • Who's at risk of leaving?
  • How do segments differ?
  • How is our customer base changing?

Common Analyses

Segmentation: Divide customers by behavior, value, demographics

Lifetime value: What's a customer worth over time

Churn analysis: Who leaves and why

Cohort analysis: How different customer groups perform over time

AI Prompt: Customer Analysis

Help me analyze customer data.

Data includes: [Describe — customer ID, transactions, dates, demographics, etc.]
Business context: [Type of business, customer relationship]
Focus: [Segmentation, churn, value, etc.]

Please analyze:
1. Customer segments and their characteristics
2. Value distribution
3. Behavior patterns
4. Risk indicators
5. Actionable insights

Operations Analysis

Key Questions

  • How efficient are our processes?
  • Where are bottlenecks?
  • What drives quality issues?
  • How can we improve throughput?
  • What's the capacity outlook?

Common Analyses

Process metrics: Cycle time, throughput, utilization

Quality analysis: Defect rates, causes, trends

Resource analysis: Capacity, scheduling, optimization

Root cause analysis: Why problems occur

AI Prompt: Operations Analysis

Help me analyze operational data.

Data includes: [Describe — timestamps, durations, quantities, quality metrics, etc.]
Process context: [What process this represents]
Key concerns: [Efficiency, quality, capacity, etc.]

Please analyze:
1. Key performance metrics
2. Trends and patterns
3. Bottlenecks or issues
4. Variation and its causes
5. Improvement opportunities

Financial Analysis

Key Questions

  • Are we profitable?
  • Where does money come from and go?
  • What's our financial trajectory?
  • How do we compare to benchmarks?
  • What scenarios should we plan for?

Common Analyses

Profitability analysis: Margin by product, customer, channel

Trend analysis: Revenue, cost, profit over time

Variance analysis: Actual vs. budget, what changed

Scenario modeling: What if projections

AI Prompt: Financial Analysis

Help me analyze financial data.

Data includes: [Describe — revenue, costs, categories, time periods, etc.]
Focus: [Profitability, trends, variance, etc.]
Context: [Business type, relevant constraints]

Please analyze:
1. Overall financial performance
2. Key drivers and trends
3. Areas of concern
4. Comparison to expectations
5. Forward-looking insights

Personal Data Analysis

Examples

  • Personal budget and spending
  • Fitness/health tracking
  • Productivity patterns
  • Investment performance
  • Any personal metrics you track

AI Prompt: Personal Analysis

Help me analyze my personal data.

Data: [What you've tracked — spending, workouts, time, etc.]
Time period: [How long]
Goal: [What you want to understand or improve]

Please analyze:
1. Patterns in my behavior
2. Progress toward goals
3. Areas for improvement
4. Comparisons (to targets, past periods)
5. Actionable suggestions

What's Next

Tools make analysis practical.

Next chapter: Tools and workflows — spreadsheets, AI, and when to use what.

Communicating InsightsTools and Workflows