How do I use the "Perform Comprehensive Cohort Analysis" prompt?

Copy the prompt template, fill in the variables (DATA_TYPE, TIME_PERIOD, SAMPLE_SIZE, AVAILABLE_METRICS, ANALYSIS_OBJECTIVES, COHORT_GROUPING, COHORT_TIME_FRAME, BUSINESS_CONTEXT), and paste it into ChatGPT, Claude, or Gemini.

What AI tools work with this data prompt?

This prompt works with chatgpt, claude, gemini. Simply copy and paste the template into any of these AI assistants.

Tips for getting the best results?

Ensure your dataset has consistent time intervals and clean user identifiers before running the analysis Focus on statistical significance - small cohorts may show misleading patterns due to sample size limitations Compare cohorts acquired during similar external conditions (seasonality, marketing campaigns, economic factors) Look for inflection points where retention dramatically changes to identify critical intervention opportunities Combine cohort analysis with qualitative data (surveys, support tickets) to understand the 'why' behind retention patterns

data advanced

Perform Comprehensive Cohort Analysis

Advanced AI prompt to analyze customer cohorts, retention rates, and behavioral patterns. Get actionable insights from your data.

Works with: chatgptclaudegemini

Prompt Template

You are an expert data analyst specializing in cohort analysis. I need you to help me perform a comprehensive cohort analysis based on the following dataset and parameters. **Dataset Information:** - Data Type: [DATA_TYPE] - Time Period: [TIME_PERIOD] - Sample Size: [SAMPLE_SIZE] - Key Metrics Available: [AVAILABLE_METRICS] **Analysis Objectives:** [ANALYSIS_OBJECTIVES] **Cohort Definition:** - Cohort Grouping: [COHORT_GROUPING] - Cohort Time Frame: [COHORT_TIME_FRAME] Please provide: 1. **Cohort Setup & Segmentation:** - Define the cohort groups based on the specified criteria - Explain the rationale for this segmentation approach - Identify any potential limitations or biases 2. **Retention Analysis:** - Calculate retention rates for each cohort across time periods - Identify patterns in user/customer retention - Highlight significant drop-off points 3. **Behavioral Insights:** - Analyze differences between cohorts - Identify high-performing vs. low-performing cohorts - Examine seasonal or temporal trends 4. **Key Findings & Recommendations:** - Summarize the most important insights - Provide actionable recommendations for [BUSINESS_CONTEXT] - Suggest areas for further investigation 5. **Visualization Recommendations:** - Suggest appropriate chart types for presenting findings - Recommend key metrics to highlight in dashboards - Propose color coding and formatting guidelines Format your response with clear headers, bullet points, and specific numerical insights where applicable. Focus on actionable business intelligence rather than just statistical summaries.

Variables to Customize

[DATA_TYPE]

Type of data being analyzed

Example: E-commerce customer transaction data with user IDs, purchase dates, order values, and product categories

[TIME_PERIOD]

Time range of the dataset

Example: January 2022 to December 2023 (24 months)

[SAMPLE_SIZE]

Size of the dataset

Example: 50,000 unique customers with 180,000 total transactions

[AVAILABLE_METRICS]

Key metrics and data points available

Example: Customer acquisition date, purchase frequency, average order value, product categories, geographic location, marketing channel

[ANALYSIS_OBJECTIVES]

Primary goals of the cohort analysis

Example: Understand customer lifetime value trends, identify optimal retention strategies, and evaluate the impact of onboarding changes implemented in Q2 2022

[COHORT_GROUPING]

How cohorts should be grouped

Example: Monthly acquisition cohorts (customers grouped by the month they made their first purchase)

[COHORT_TIME_FRAME]

Time periods for tracking cohort behavior

Example: Track behavior over 12 months post-acquisition in monthly intervals

[BUSINESS_CONTEXT]

Business context for recommendations

Example: subscription SaaS platform looking to reduce churn and increase customer lifetime value

Example Output

# Cohort Analysis Results ## 1. Cohort Setup & Segmentation **Cohort Groups:** 24 monthly acquisition cohorts from Jan 2022 - Dec 2023 **Rationale:** Monthly grouping allows for seasonal trend identification while maintaining sufficient sample sizes (average 2,100 customers per cohort) **Limitations:** Holiday months show acquisition spikes that may skew comparisons ## 2. Retention Analysis **Month 1 Retention:** 68% average across all cohorts **Month 6 Retention:** 34% average (50% drop from month 1) **Month 12 Retention:** 22% average **Key Finding:** Q2 2022 cohorts show 15% higher retention rates, likely due to onboarding improvements ## 3. Behavioral Insights - **High-performing cohorts:** March-May 2022 (45% month-6 retention) - **Seasonal pattern:** Winter cohorts consistently underperform by 12% - **Spending behavior:** Retained customers increase AOV by 35% after month 3 ## 4. Key Recommendations - Implement Q2 2022 onboarding features across all new acquisitions - Develop winter-specific retention campaigns - Focus retention efforts on months 1-3 critical period - Create loyalty programs targeting the 22% long-term retained segment ## 5. Visualization Recommendations - Heat map showing retention rates by cohort and month - Line chart comparing top vs. bottom performing cohorts - Stacked bar chart showing revenue contribution by cohort age

Pro Tips for Best Results

Ensure your dataset has consistent time intervals and clean user identifiers before running the analysis
Focus on statistical significance - small cohorts may show misleading patterns due to sample size limitations
Compare cohorts acquired during similar external conditions (seasonality, marketing campaigns, economic factors)
Look for inflection points where retention dramatically changes to identify critical intervention opportunities
Combine cohort analysis with qualitative data (surveys, support tickets) to understand the 'why' behind retention patterns