Data Analysis Examples

Leverage AI for powerful data analysis with these StickyPrompts templates. From exploratory analysis to predictive modeling, these examples help you extract meaningful insights from your data.

Data Analysis Templates

Exploratory Data Analysis

General Data Analysis:

Analyze the {{DATASET_TYPE}} data and provide insights on {{ANALYSIS_FOCUS}}.

## Data Specifications:
- Dataset: {{DATASET_DESCRIPTION}}
- Time Period: {{TIME_RANGE}}
- Key Variables: {{KEY_VARIABLES}}
- Data Format: {{DATA_FORMAT}}

## Analysis Requirements:
- Primary Objective: {{ANALYSIS_OBJECTIVE}}
- Statistical Methods: {{STATISTICAL_APPROACHES}}
- Visualization Types: {{VISUALIZATION_TYPES}}
- Comparisons Needed: {{COMPARISON_POINTS}}

## Deliverable Format:
- Structure: {{REPORT_STRUCTURE}}
- Detail Level: {{DETAIL_LEVEL}}
- Technical Language: {{TECHNICAL_LEVEL}}
- Visualization Emphasis: {{VISUALIZATION_EMPHASIS}}

## Additional Considerations:
- Audience: {{TARGET_AUDIENCE}}
- Key Questions: {{KEY_QUESTIONS}}
- Previous Findings: {{PREVIOUS_INSIGHTS}}
- Business Context: {{BUSINESS_CONTEXT}}

Dashboard Design Concept:

Create a data dashboard concept for tracking {{METRIC_CATEGORY}} with the following specifications:

## Dashboard Purpose:
- Primary Users: {{PRIMARY_USERS}}
- Key Decisions Supported: {{DECISION_SUPPORT}}
- Update Frequency: {{UPDATE_FREQUENCY}}
- Access Level: {{ACCESS_REQUIREMENTS}}

## Key Metrics:
- Performance Indicators: {{PERFORMANCE_KPIS}}
- Trend Analyses: {{TREND_METRICS}}
- Comparative Measures: {{COMPARISON_METRICS}}
- Alert Thresholds: {{ALERT_THRESHOLDS}}

## Visualization Components:
- Primary Charts: {{PRIMARY_VISUALIZATIONS}}
- Secondary Displays: {{SECONDARY_VISUALIZATIONS}}
- Data Tables: {{TABLE_REQUIREMENTS}}
- Interactive Elements: {{INTERACTIVE_FEATURES}}

## Technical Specifications:
- Data Sources: {{DATA_SOURCES}}
- Integration Requirements: {{INTEGRATION_NEEDS}}
- Filtering Capabilities: {{FILTER_OPTIONS}}
- Export Formats: {{EXPORT_FORMATS}}

## Design Considerations:
- Layout Approach: {{LAYOUT_DESIGN}}
- Information Hierarchy: {{INFORMATION_HIERARCHY}}
- Color Scheme: {{COLOR_SYSTEM}}
- Mobile Adaptation: {{MOBILE_REQUIREMENTS}}

Customer Analysis Examples

Segmentation Strategy

Customer Segmentation:

Develop a customer segmentation strategy for {{COMPANY_TYPE}} based on {{DATA_SOURCES}}.

## Segmentation Objectives:
- Business Goals: {{BUSINESS_OBJECTIVES}}
- Marketing Applications: {{MARKETING_APPLICATIONS}}
- Product Development Uses: {{PRODUCT_APPLICATIONS}}
- Customer Experience Improvements: {{CX_APPLICATIONS}}

## Data Parameters:
- Customer Data Available: {{AVAILABLE_DATA}}
- Behavioral Indicators: {{BEHAVIORAL_METRICS}}
- Demographic Factors: {{DEMOGRAPHIC_FACTORS}}
- Transaction History: {{TRANSACTION_DATA}}

## Segmentation Approach:
- Methodology: {{SEGMENTATION_METHODOLOGY}}
- Number of Segments: {{SEGMENT_COUNT}}
- Naming Convention: {{SEGMENT_NAMING}}
- Differentiation Criteria: {{DIFFERENTIATION_FACTORS}}

## Segment Activation:
- Targeting Strategy: {{TARGETING_APPROACH}}
- Communication Channels: {{CHANNEL_STRATEGY}}
- Offer Customization: {{OFFER_CUSTOMIZATION}}
- Measurement Plan: {{MEASUREMENT_APPROACH}}

Churn Analysis and Prediction

Churn Prediction Model:

Develop a churn prediction model for {{SERVICE_TYPE}} using {{DATA_SOURCES}}.

## Business Context:
- Current Churn Rate: {{CURRENT_CHURN_RATE}}
- Churn Definition: {{CHURN_DEFINITION}}
- Impact on Business: {{CHURN_IMPACT}}
- Intervention Capacity: {{INTERVENTION_CAPACITY}}

## Model Specifications:
- Prediction Window: {{PREDICTION_WINDOW}}
- Risk Score Format: {{RISK_SCORE_FORMAT}}
- Update Frequency: {{MODEL_UPDATES}}
- Actionability Threshold: {{ACTION_THRESHOLD}}

## Data Elements:
- Customer Attributes: {{CUSTOMER_ATTRIBUTES}}
- Behavioral Indicators: {{BEHAVIORAL_SIGNALS}}
- Usage Patterns: {{USAGE_PATTERNS}}
- External Factors: {{EXTERNAL_FACTORS}}

## Implementation Plan:
- Model Development: {{DEVELOPMENT_APPROACH}}
- Validation Strategy: {{VALIDATION_STRATEGY}}
- Deployment Method: {{DEPLOYMENT_METHOD}}
- Performance Monitoring: {{MONITORING_PLAN}}

Predictive Modeling Templates

Forecasting Models

Predictive Forecasting:

Design a predictive model to forecast {{PREDICTION_TARGET}} for {{BUSINESS_CONTEXT}}.

## Business Requirements:
- Prediction Objective: {{PREDICTION_OBJECTIVE}}
- Forecast Horizon: {{FORECAST_PERIOD}}
- Accuracy Requirements: {{ACCURACY_NEEDS}}
- Update Frequency: {{MODEL_UPDATES}}

## Data Inputs:
- Historical Data: {{HISTORICAL_DATA}}
- External Variables: {{EXTERNAL_VARIABLES}}
- Data Limitations: {{DATA_LIMITATIONS}}
- Feature Importance: {{IMPORTANT_FEATURES}}

## Modeling Approach:
- Recommended Algorithms: {{ALGORITHM_RECOMMENDATIONS}}
- Ensemble Strategy: {{ENSEMBLE_APPROACH}}
- Validation Method: {{VALIDATION_METHOD}}
- Performance Metrics: {{PERFORMANCE_METRICS}}

## Implementation Plan:
- Development Phases: {{DEVELOPMENT_TIMELINE}}
- Testing Approach: {{TESTING_METHODOLOGY}}
- Deployment Strategy: {{DEPLOYMENT_STRATEGY}}
- Maintenance Requirements: {{MAINTENANCE_NEEDS}}

Marketing Mix Modeling

Marketing Attribution Model:

Create a marketing mix model to optimize {{MARKETING_CHANNELS}} for {{BUSINESS_TYPE}}.

## Modeling Objectives:
- Business Goals: {{BUSINESS_OBJECTIVES}}
- Optimization Focus: {{OPTIMIZATION_FOCUS}}
- Budget Allocation: {{BUDGET_ALLOCATION}}
- Performance Timeframe: {{PERFORMANCE_PERIOD}}

## Data Requirements:
- Marketing Inputs: {{MARKETING_INPUTS}}
- Business Outcomes: {{BUSINESS_OUTCOMES}}
- External Variables: {{EXTERNAL_VARIABLES}}
- Data Granularity: {{DATA_GRANULARITY}}

## Modeling Approach:
- Statistical Method: {{STATISTICAL_METHOD}}
- Attribution Window: {{ATTRIBUTION_WINDOW}}
- Adstock Parameters: {{ADSTOCK_PARAMETERS}}
- Diminishing Returns: {{DIMINISHING_RETURNS}}

## Output Applications:
- Channel Recommendations: {{CHANNEL_RECOMMENDATIONS}}
- Budget Optimization: {{BUDGET_OPTIMIZATION}}
- Scenario Planning: {{SCENARIO_PLANNING}}
- Reporting Dashboards: {{REPORTING_DASHBOARDS}}

Market Research Templates

Competitive Analysis

Competitive Intelligence:

Perform a competitive analysis of {{MARKET_SECTOR}} focusing on {{ANALYSIS_FOCUS}}.

## Analysis Scope:
- Market Definition: {{MARKET_DEFINITION}}
- Key Competitors: {{MAIN_COMPETITORS}}
- Time Period: {{ANALYSIS_TIMEFRAME}}
- Geographic Focus: {{GEOGRAPHIC_SCOPE}}

## Data Collection:
- Data Sources: {{DATA_SOURCES}}
- Metrics to Track: {{COMPETITIVE_METRICS}}
- Benchmarking Approach: {{BENCHMARKING_METHOD}}
- Gap Identification: {{GAP_ANALYSIS}}

## Analysis Framework:
- Competitive Dimensions: {{COMPETITIVE_DIMENSIONS}}
- Scoring Methodology: {{SCORING_SYSTEM}}
- Visualization Approach: {{VISUALIZATION_METHOD}}
- Trend Spotting: {{TREND_ANALYSIS}}

## Strategic Application:
- Key Findings Summary: {{KEY_FINDINGS}}
- Strategic Implications: {{STRATEGIC_IMPLICATIONS}}
- Opportunity Identification: {{OPPORTUNITY_AREAS}}
- Threat Assessment: {{THREAT_ASSESSMENT}}

Pricing Analysis

Pricing Optimization:

Develop a pricing optimization strategy for {{PRODUCT_SERVICE}} based on {{DATA_SOURCES}}.

## Strategy Objectives:
- Business Goals: {{BUSINESS_OBJECTIVES}}
- Pricing Constraints: {{PRICING_CONSTRAINTS}}
- Competitive Positioning: {{COMPETITIVE_POSITIONING}}
- Customer Perception: {{VALUE_PERCEPTION}}

## Data Analysis:
- Price Elasticity: {{ELASTICITY_ANALYSIS}}
- Customer Segments: {{SEGMENT_PRICE_SENSITIVITY}}
- Competitive Pricing: {{COMPETITOR_PRICING}}
- Cost Structure: {{COST_STRUCTURE}}

## Pricing Approach:
- Pricing Models: {{PRICING_MODELS}}
- Discount Strategy: {{DISCOUNT_STRATEGY}}
- Bundle Opportunities: {{BUNDLE_STRATEGY}}
- Dynamic Pricing: {{DYNAMIC_PRICING}}

## Implementation Roadmap:
- Testing Approach: {{PRICE_TESTING}}
- Rollout Plan: {{ROLLOUT_STRATEGY}}
- Monitoring KPIs: {{PRICING_KPIS}}
- Adjustment Triggers: {{ADJUSTMENT_TRIGGERS}}

Experimentation Templates

A/B Testing

A/B Test Design:

Create an A/B testing plan for {{TEST_ELEMENT}} on {{PLATFORM}} to optimize {{OPTIMIZATION_GOAL}}.

## Test Fundamentals:
- Hypothesis: {{TEST_HYPOTHESIS}}
- Primary Metric: {{PRIMARY_METRIC}}
- Secondary Metrics: {{SECONDARY_METRICS}}
- Minimum Detectable Effect: {{MINIMUM_EFFECT}}

## Test Design:
- Variations: {{TEST_VARIATIONS}}
- Traffic Allocation: {{TRAFFIC_SPLIT}}
- Target Audience: {{TEST_AUDIENCE}}
- Test Duration: {{TEST_DURATION}}

## Implementation Details:
- Technical Setup: {{TECHNICAL_SETUP}}
- Quality Assurance: {{QA_PROCESS}}
- Analytics Configuration: {{ANALYTICS_SETUP}}
- Segment Analysis: {{SEGMENT_ANALYSIS}}

## Decision Framework:
- Success Criteria: {{SUCCESS_CRITERIA}}
- Follow-up Actions: {{FOLLOW_UP_ACTIONS}}
- Roll-out Plan: {{ROLLOUT_PLAN}}
- Learning Documentation: {{DOCUMENTATION_PLAN}}

Market Basket Analysis

Purchase Pattern Analysis:

Design a market basket analysis for {{RETAIL_TYPE}} to identify {{ANALYSIS_GOAL}}.

## Analysis Parameters:
- Transaction Data: {{TRANSACTION_DATA}}
- Time Period: {{ANALYSIS_PERIOD}}
- Store Locations: {{LOCATION_SCOPE}}
- Customer Segments: {{CUSTOMER_SEGMENTS}}

## Technical Approach:
- Association Rules: {{ASSOCIATION_METRICS}}
- Support Threshold: {{SUPPORT_THRESHOLD}}
- Confidence Threshold: {{CONFIDENCE_THRESHOLD}}
- Lift Criteria: {{LIFT_CRITERIA}}

## Output Requirements:
- Key Associations: {{ASSOCIATION_FOCUS}}
- Visualization Format: {{VISUALIZATION_FORMAT}}
- Actionable Metrics: {{ACTIONABLE_METRICS}}
- Reporting Frequency: {{REPORTING_FREQUENCY}}

## Business Applications:
- Merchandising Recommendations: {{MERCHANDISING_APPLICATIONS}}
- Promotion Strategy: {{PROMOTION_STRATEGY}}
- Store Layout Implications: {{LAYOUT_IMPLICATIONS}}
- Product Development Insights: {{PRODUCT_INSIGHTS}}

Data Science Workflow Templates

Data Pipeline Design

ETL Process Design:

Create a data pipeline design for {{DATA_TYPE}} processing with the following requirements:

## Business Needs:
- Use Case: {{BUSINESS_USE_CASE}}
- Data Volume: {{DATA_VOLUME}}
- Latency Requirements: {{LATENCY_NEEDS}}
- Compliance Considerations: {{COMPLIANCE_REQUIREMENTS}}

## Data Sources:
- Primary Sources: {{PRIMARY_DATA_SOURCES}}
- Source Formats: {{SOURCE_FORMATS}}
- Update Frequency: {{SOURCE_UPDATE_FREQUENCY}}
- Quality Considerations: {{SOURCE_QUALITY_ISSUES}}

## Processing Requirements:
- Transformations: {{DATA_TRANSFORMATIONS}}
- Enrichment: {{DATA_ENRICHMENT}}
- Validation Rules: {{VALIDATION_RULES}}
- Error Handling: {{ERROR_HANDLING_APPROACH}}

## Output Specifications:
- Target Storage: {{TARGET_STORAGE}}
- Output Format: {{OUTPUT_FORMAT}}
- Access Patterns: {{ACCESS_PATTERNS}}
- Retention Policy: {{RETENTION_POLICY}}

## Architecture Considerations:
- Processing Framework: {{PROCESSING_FRAMEWORK}}
- Scalability Needs: {{SCALABILITY_REQUIREMENTS}}
- Monitoring Approach: {{MONITORING_STRATEGY}}
- Disaster Recovery: {{DISASTER_RECOVERY_PLAN}}

Machine Learning Operations

MLOps Framework:

Design an MLOps framework for {{ML_MODEL_TYPE}} models in {{INDUSTRY_CONTEXT}}.

## Operational Requirements:
- Model Types: {{MODEL_TYPES}}
- Deployment Frequency: {{DEPLOYMENT_FREQUENCY}}
- Performance Monitoring: {{MONITORING_REQUIREMENTS}}
- Governance Requirements: {{GOVERNANCE_NEEDS}}

## Development Workflow:
- Version Control: {{VERSION_CONTROL_STRATEGY}}
- Feature Store: {{FEATURE_STORE_DESIGN}}
- Experiment Tracking: {{EXPERIMENT_TRACKING}}
- Model Registry: {{MODEL_REGISTRY_APPROACH}}

## Deployment Pipeline:
- CI/CD Integration: {{CICD_INTEGRATION}}
- Testing Strategy: {{TESTING_APPROACH}}
- Deployment Patterns: {{DEPLOYMENT_PATTERNS}}
- Rollback Mechanisms: {{ROLLBACK_STRATEGY}}

## Production Operations:
- Monitoring Metrics: {{MONITORING_METRICS}}
- Alerting Framework: {{ALERTING_FRAMEWORK}}
- Performance Evaluation: {{PERFORMANCE_EVALUATION}}
- Retraining Triggers: {{RETRAINING_TRIGGERS}}

## Infrastructure Design:
- Compute Resources: {{COMPUTE_REQUIREMENTS}}
- Scaling Strategy: {{SCALING_APPROACH}}
- Security Controls: {{SECURITY_REQUIREMENTS}}
- Cost Optimization: {{COST_OPTIMIZATION}}

Variable Definitions

Common data analysis variables used in these examples:

Data Context

• {{DATASET_TYPE}} - Type of dataset (e.g., customer, financial, operational)
• {{DATASET_DESCRIPTION}} - Brief description of the dataset content
• {{TIME_RANGE}} - Time period covered by the data
• {{DATA_FORMAT}} - Format of the data (CSV, JSON, database, etc.)

Analysis Parameters

• {{ANALYSIS_FOCUS}} - Specific aspect to analyze (trends, patterns, anomalies)
• {{ANALYSIS_OBJECTIVE}} - Main goal of the analysis
• {{STATISTICAL_APPROACHES}} - Statistical methods to apply
• {{VISUALIZATION_TYPES}} - Types of visualizations to include

Business Context

• {{BUSINESS_CONTEXT}} - Relevant business situation
• {{BUSINESS_OBJECTIVES}} - Goals the business aims to achieve
• {{TARGET_AUDIENCE}} - Who will consume the analysis
• {{DECISION_SUPPORT}} - Decisions the analysis will inform

Technical Parameters

• {{DATA_SOURCES}} - Origin of the data
• {{ALGORITHM_RECOMMENDATIONS}} - Suggested algorithms
• {{PERFORMANCE_METRICS}} - How to measure success
• {{VALIDATION_METHOD}} - How to validate results

Getting Started

1. Select a Template: Choose the example that best matches your data analysis needs
2. Customize Variables: Replace placeholder variables with your specific parameters
3. Refine the Prompt: Adjust the template to your specific use case
4. Review Output: Evaluate the analysis results
5. Iterate: Refine your prompt based on the output quality

These data analysis examples provide a foundation for extracting insights from your data. Adapt them to your specific data sources, business objectives, and analytical requirements.