← Back to Analysis Directory Sample Report: Customer Retention Cohort Analysis

Context and Data Preparation

Analysis Overview and Data Quality

OV

Analysis Overview

Retention Cohort Analysis Configuration

Analysis overview and configuration

Retention Cohort
E-commerce Store
Analyze customer retention patterns using cohort analysis
Module Configuration
cohort_period month
retention_periods 6
Processing ID
test_1766346865
analysis type: Retention Cohort
company: E-commerce Store
objective: Analyze customer retention patterns using cohort analysis
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Key Insights

Analysis Overview

Purpose

This section provides insights into the key metrics and data characteristics of the Shopify Customer Retention Cohort Analysis, aiding in understanding customer retention patterns.

Key Findings

  • Total Orders: 41 - Indicates the number of orders in the analysis period.
  • Overall Retention Rate: 43.33% - Shows the average rate at which customers are retained over time.
  • Strongest Cohort: 2024-06 - Represents the cohort with the highest retention rate.

Interpretation

The analysis reveals that the overall retention rate is moderate at 43.33%, with the strongest cohort in June 2024. Understanding these metrics helps gauge customer loyalty and identify cohorts that exhibit better retention, aiding in targeted retention strategies.

Context

The analysis focuses on cohort-based retention tracking, assuming unique customer identification by email and using first purchase dates for cohort assignment. Limitations include the lack of consideration for customer lifetime value differences and the need for sufficient historical data for accurate tracking.

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Key Insights

Analysis Overview

Purpose

This section provides insights into the key metrics and data characteristics of the Shopify Customer Retention Cohort Analysis, aiding in understanding customer retention patterns.

Key Findings

  • Total Orders: 41 - Indicates the number of orders in the analysis period.
  • Overall Retention Rate: 43.33% - Shows the average rate at which customers are retained over time.
  • Strongest Cohort: 2024-06 - Represents the cohort with the highest retention rate.

Interpretation

The analysis reveals that the overall retention rate is moderate at 43.33%, with the strongest cohort in June 2024. Understanding these metrics helps gauge customer loyalty and identify cohorts that exhibit better retention, aiding in targeted retention strategies.

Context

The analysis focuses on cohort-based retention tracking, assuming unique customer identification by email and using first purchase dates for cohort assignment. Limitations include the lack of consideration for customer lifetime value differences and the need for sufficient historical data for accurate tracking.

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Data Preprocessing

Data Quality & Cohort Assignment

41
Final Customers

Data preprocessing and column mapping

Data Pipeline
80
Initial Records
41
Clean Records
Column Mapping
customer_email
→
Email
order_date
→
Created at
order_total
→
Total
financial_status
→
Financial Status
41 Records
MCP Analytics
41 Records
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Key Insights

Data Preprocessing

Purpose

This section outlines the data preprocessing steps taken, including data quality checks, retention rate calculation, and the impact of data cleaning on the dataset.

Key Findings

  • Initial Rows: 80 - The original dataset size.
  • Final Rows: 41 - Number of rows retained after cleaning.
  • Rows Removed: 39 - Instances removed during preprocessing.
  • Retention Rate: 51.2% - Percentage of data retained after cleaning.

Interpretation

The data preprocessing resulted in a 51.2% retention rate, indicating significant data cleaning. Removing 39 rows suggests the initial dataset had quality issues or missing values that could impact the analysis.

Context

The high retention rate post-cleaning is crucial for accurate cohort analysis. However, the removal of 39 rows may have implications for the representativeness of the dataset and could affect the reliability of the insights derived from the analysis.

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Key Insights

Data Preprocessing

Purpose

This section outlines the data preprocessing steps taken, including data quality checks, retention rate calculation, and the impact of data cleaning on the dataset.

Key Findings

  • Initial Rows: 80 - The original dataset size.
  • Final Rows: 41 - Number of rows retained after cleaning.
  • Rows Removed: 39 - Instances removed during preprocessing.
  • Retention Rate: 51.2% - Percentage of data retained after cleaning.

Interpretation

The data preprocessing resulted in a 51.2% retention rate, indicating significant data cleaning. Removing 39 rows suggests the initial dataset had quality issues or missing values that could impact the analysis.

Context

The high retention rate post-cleaning is crucial for accurate cohort analysis. However, the removal of 39 rows may have implications for the representativeness of the dataset and could affect the reliability of the insights derived from the analysis.

Executive Summary

Key Findings and Recommendations

TLDR

Executive Summary

Key Findings & Recommendations

12
Overall Retention Rate

Key Performance Indicators

Total customers
12
Total cohorts
5
Overall retention rate
43.33
Avg cohort size
2.4
Strongest cohort
2024-06
Weakest cohort
2024-05

Key Findings

Key findings

Metric Value
Total Customers 12
Total Cohorts 5
Avg Cohort Size 2.4 customers
Period 1 Retention 43.33%
Strongest Cohort 2024-06
Weakest Cohort 2024-05
Tracking Period 6 months
Date Range 2024-05-04 to 2024-11-25

Executive Summary

Bottom Line: Cohort retention analysis of 12 customers across 5 monthly cohorts shows 43.33% overall retention rate at period 1.

Cohort Insights:
• Date Range: 2024-05-04 to 2024-11-25
• Tracking Period: monthly cohorts over 6 periods
• Strongest Cohort: 2024-06 (focus on replicating success)
• Weakest Cohort: 2024-05 (investigate root causes)

Data Quality:
• Analyzed 41 orders from 12 unique customers
• Average cohort size: 2.4 customers

Strategic Recommendation:
Excellent retention performance. Your customers are coming back at a healthy rate. Focus on identifying what makes your strongest cohorts successful and replicate those strategies. Consider implementing VIP programs for repeat purchasers.

Executive Summary
Key Insights
Action Items
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Key Insights

Executive Summary

Purpose

This section provides a concise summary of the key findings from the cohort retention analysis, focusing on metrics that matter for decision-making and understanding the overall analysis context.

Key Findings

  • Total Customers: 12 - Represents the customer base analyzed in the cohort retention study.
  • Total Cohorts: 5 - Indicates the number of distinct cohorts tracked over the analysis period.
  • Overall Retention Rate: 43.33% - Reflects the average retention rate across all cohorts at period 1.
  • Avg Cohort Size: 2.4 customers - Shows the average number of customers per cohort.
  • Strongest Cohort: 2024-06 - Identifies the cohort with the highest retention rate.
  • Weakest Cohort: 2024-05 - Highlights the cohort with the lowest retention rate.

Interpretation

The analysis reveals a moderate overall retention rate of 43.33% at period 1, with varying performance across cohorts. Understanding the characteristics of the strongest cohort (2024-06) can guide strategies for improving retention rates across other cohorts.

Context

The analysis is based on assumptions like unique customer identification by email and retention measured through repeat purchase activity. Limitations include the need for historical data for accurate tracking and potential biases in recent cohorts due to limited observation periods.

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Key Insights

Executive Summary

Purpose

This section provides a concise summary of the key findings from the cohort retention analysis, focusing on metrics that matter for decision-making and understanding the overall analysis context.

Key Findings

  • Total Customers: 12 - Represents the customer base analyzed in the cohort retention study.
  • Total Cohorts: 5 - Indicates the number of distinct cohorts tracked over the analysis period.
  • Overall Retention Rate: 43.33% - Reflects the average retention rate across all cohorts at period 1.
  • Avg Cohort Size: 2.4 customers - Shows the average number of customers per cohort.
  • Strongest Cohort: 2024-06 - Identifies the cohort with the highest retention rate.
  • Weakest Cohort: 2024-05 - Highlights the cohort with the lowest retention rate.

Interpretation

The analysis reveals a moderate overall retention rate of 43.33% at period 1, with varying performance across cohorts. Understanding the characteristics of the strongest cohort (2024-06) can guide strategies for improving retention rates across other cohorts.

Context

The analysis is based on assumptions like unique customer identification by email and retention measured through repeat purchase activity. Limitations include the need for historical data for accurate tracking and potential biases in recent cohorts due to limited observation periods.

Retention Heatmap

Visual Matrix of Cohort Retention Over Time

HM

Retention Heatmap

Cohort Retention Matrix Over Time

Retention rate heatmap showing how each cohort retains customers over time

Heatmap
Retention Rates
Cohort Analysis
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Key Insights

Retention Heatmap

Purpose

This section displays a cohort retention matrix tracking 5 cohorts over 6 months. It illustrates how well each cohort retains customers over time, with darker colors indicating higher retention rates. Period 0 always shows 100% retention, representing the initial purchase.

Key Findings

  • Retention Rate: The average retention rate across all cohorts is 23.81%, with variations over different periods.
  • Cohort Size: The average cohort size is 0.57, indicating small cohorts being tracked.
  • Retention Patterns: Cohorts exhibit varying retention rates over the 6-month period, with some cohorts showing consistent retention and others declining sharply.

Interpretation

The cohort heatmap helps identify which cohorts have strong or weak retention trajectories over time. Understanding these patterns can guide strategies to improve customer retention and loyalty, ultimately impacting revenue and customer lifetime value.

Context

This section complements the overall analysis by providing a visual representation of how different cohorts behave in terms of customer retention. It helps in pinpointing specific cohorts that may require targeted retention efforts and understanding the overall retention dynamics of the business.

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Key Insights

Retention Heatmap

Purpose

This section displays a cohort retention matrix tracking 5 cohorts over 6 months. It illustrates how well each cohort retains customers over time, with darker colors indicating higher retention rates. Period 0 always shows 100% retention, representing the initial purchase.

Key Findings

  • Retention Rate: The average retention rate across all cohorts is 23.81%, with variations over different periods.
  • Cohort Size: The average cohort size is 0.57, indicating small cohorts being tracked.
  • Retention Patterns: Cohorts exhibit varying retention rates over the 6-month period, with some cohorts showing consistent retention and others declining sharply.

Interpretation

The cohort heatmap helps identify which cohorts have strong or weak retention trajectories over time. Understanding these patterns can guide strategies to improve customer retention and loyalty, ultimately impacting revenue and customer lifetime value.

Context

This section complements the overall analysis by providing a visual representation of how different cohorts behave in terms of customer retention. It helps in pinpointing specific cohorts that may require targeted retention efforts and understanding the overall retention dynamics of the business.

Retention Trajectories

Comparative Analysis of Cohort Behavior

LC

Retention Curves

Cohort Retention Trajectories Over Time

Retention curves comparing how different cohorts behave over time

Retention Curves
Trend Analysis
Cohort Comparison
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Key Insights

Retention Curves

Purpose

This section displays the retention trajectories of different cohorts over time, highlighting how customer retention evolves post-acquisition. Understanding these curves helps identify which acquisition periods yield the most loyal customers and the overall retention trend.

Key Findings

  • Overall Retention Rate at Period 1: 43.33% - Indicates the average percentage of customers retained after the first period.
  • Steeper Declines: Faster customer churn, while flatter curves suggest stronger retention.
  • Best Performing Cohort: 2024-06 - This cohort exhibited the highest retention rate over time.

Interpretation

The retention curves provide insights into how different cohorts behave post-acquisition. A higher initial retention rate and slower decline indicate better customer loyalty. The best performing cohort, 2024-06, could offer valuable insights into effective acquisition strategies or customer engagement tactics.

Context

These retention curves complement the overall analysis by showcasing how customer retention varies across cohorts. Understanding these patterns can guide marketing strategies, customer engagement initiatives, and overall business decisions to improve long-term customer loyalty and profitability.

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Key Insights

Retention Curves

Purpose

This section displays the retention trajectories of different cohorts over time, highlighting how customer retention evolves post-acquisition. Understanding these curves helps identify which acquisition periods yield the most loyal customers and the overall retention trend.

Key Findings

  • Overall Retention Rate at Period 1: 43.33% - Indicates the average percentage of customers retained after the first period.
  • Steeper Declines: Faster customer churn, while flatter curves suggest stronger retention.
  • Best Performing Cohort: 2024-06 - This cohort exhibited the highest retention rate over time.

Interpretation

The retention curves provide insights into how different cohorts behave post-acquisition. A higher initial retention rate and slower decline indicate better customer loyalty. The best performing cohort, 2024-06, could offer valuable insights into effective acquisition strategies or customer engagement tactics.

Context

These retention curves complement the overall analysis by showcasing how customer retention varies across cohorts. Understanding these patterns can guide marketing strategies, customer engagement initiatives, and overall business decisions to improve long-term customer loyalty and profitability.

Cohort Comparison

Size and Initial Order Value Analysis

CC

Cohort Size Comparison

Initial Customer Count and First Order Value

Cohort size comparison and average first order value across cohorts

Cohort Size
First Order
Acquisition
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Key Insights

Cohort Size Comparison

Purpose

This section compares cohort sizes and average first order values across different cohorts. Understanding these metrics is crucial for assessing the impact of cohort quality and initial spending behavior on customer retention analysis.

Key Findings

  • Average Cohort Size: 2.4 customers - Indicates the typical number of customers in each cohort, influencing the reliability of retention data.
  • Initial Customers: Ranges from 1 to 6 - Shows the variation in cohort sizes, impacting the representativeness of each group.
  • Average First Order Value: Ranges from $260.32 to $356.4 - Reflects the average spending behavior of customers in different cohorts.

Interpretation

The average cohort size of 2.4 customers suggests varying group sizes, potentially affecting the robustness of retention insights. Differences in initial customer counts and first order values across cohorts may influence long-term retention patterns and revenue generation.

Context

Understanding cohort sizes and initial spending behavior provides insights into how acquisition strategies impact customer retention and overall business performance. These metrics help contextualize the effectiveness of marketing campaigns and customer acquisition efforts within the cohort analysis framework.

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Key Insights

Cohort Size Comparison

Purpose

This section compares cohort sizes and average first order values across different cohorts. Understanding these metrics is crucial for assessing the impact of cohort quality and initial spending behavior on customer retention analysis.

Key Findings

  • Average Cohort Size: 2.4 customers - Indicates the typical number of customers in each cohort, influencing the reliability of retention data.
  • Initial Customers: Ranges from 1 to 6 - Shows the variation in cohort sizes, impacting the representativeness of each group.
  • Average First Order Value: Ranges from $260.32 to $356.4 - Reflects the average spending behavior of customers in different cohorts.

Interpretation

The average cohort size of 2.4 customers suggests varying group sizes, potentially affecting the robustness of retention insights. Differences in initial customer counts and first order values across cohorts may influence long-term retention patterns and revenue generation.

Context

Understanding cohort sizes and initial spending behavior provides insights into how acquisition strategies impact customer retention and overall business performance. These metrics help contextualize the effectiveness of marketing campaigns and customer acquisition efforts within the cohort analysis framework.

Revenue Analysis

Customer Spending Patterns Over Lifecycle

RA

Revenue by Cohort

Revenue Contribution Over Customer Lifecycle

Revenue contribution by cohort and time period showing spending patterns over customer lifecycle

Revenue
Spending Patterns
Customer Value
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Key Insights

Revenue by Cohort

Purpose

This section displays the average and total revenue by cohort and retention period, providing insights into customer spending patterns over their lifecycle. Understanding how revenue changes over time can help identify trends in customer behavior, such as one-time purchases or increasing engagement.

Key Findings

  • Average Revenue: Mean of 314.55 - Indicates the typical spending level per customer across cohorts and periods.
  • Total Revenue: Mean of 1251.15 - Reflects the overall revenue generated by customers within the analyzed cohorts.
  • Revenue Patterns: Varied revenue values across cohorts and periods suggest differing customer spending behaviors.

Interpretation

The average and total revenue metrics offer a snapshot of customer purchasing behavior within each cohort and over time. Higher average revenue may indicate more valuable customers, while changes in total revenue can signal shifts in overall sales performance.

Context

These revenue insights complement the customer retention analysis by providing a financial perspective on customer behavior. Understanding revenue trends alongside retention rates can offer a comprehensive view of customer engagement and business performance.

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Key Insights

Revenue by Cohort

Purpose

This section displays the average and total revenue by cohort and retention period, providing insights into customer spending patterns over their lifecycle. Understanding how revenue changes over time can help identify trends in customer behavior, such as one-time purchases or increasing engagement.

Key Findings

  • Average Revenue: Mean of 314.55 - Indicates the typical spending level per customer across cohorts and periods.
  • Total Revenue: Mean of 1251.15 - Reflects the overall revenue generated by customers within the analyzed cohorts.
  • Revenue Patterns: Varied revenue values across cohorts and periods suggest differing customer spending behaviors.

Interpretation

The average and total revenue metrics offer a snapshot of customer purchasing behavior within each cohort and over time. Higher average revenue may indicate more valuable customers, while changes in total revenue can signal shifts in overall sales performance.

Context

These revenue insights complement the customer retention analysis by providing a financial perspective on customer behavior. Understanding revenue trends alongside retention rates can offer a comprehensive view of customer engagement and business performance.

Cohort Performance

Comprehensive Metrics for Each Cohort

CP

Cohort Performance Summary

Comprehensive Cohort Metrics

5
Total Cohorts

Comprehensive summary of each cohort's performance including size, retention rates, and revenue

cohort_label customer_count avg_first_order period_0_retention period_1_retention avg_retention total_revenue
2024-05 1.000 356.400 100.000 0.000 28.570 884.520
2024-06 1.000 356.400 100.000 100.000 28.570 475.200
2024-09 6.000 324.540 100.000 50.000 23.810 4173.120
2024-10 3.000 345.520 100.000 66.670 23.810 5416.740
2024-11 1.000 260.320 100.000 0.000 14.290 1561.950
Performance Summary
Cohort Metrics
Retention Analysis
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Key Insights

Cohort Performance Summary

Purpose

This section presents a detailed overview of each cohort’s performance, focusing on metrics like customer count, initial acquisition (period 0) retention, early retention (period 1), average retention across all periods, and total revenue contribution. Understanding these metrics helps identify the strongest and weakest cohorts, providing insights into what drives success in customer retention and revenue generation.

Key Findings

  • Period 0 Retention: Cohort 2024-05 had 100% retention at the initial purchase stage.
  • Period 1 Retention: Cohort 2024-06 showed 100% retention in the early stages, while cohort 2024-11 had lower early retention at 0%.
  • Average Retention: Cohort 2024-06 had the highest average retention at 28.57%, while cohort 2024-11 had the lowest at 14.29%.
  • Total Revenue: Cohort 2024-10 contributed the highest total revenue of $5416.74.

Interpretation

The data reveals that cohort 2024-06 stands out as the strongest cohort with high initial and early retention rates, leading to the highest average retention and a significant total revenue contribution. In contrast, cohort 2024-11 shows lower retention rates across all periods, impacting its total revenue generation potential.

Context

These cohort performance metrics provide valuable insights into customer retention and revenue generation patterns, helping the e-commerce store understand which cohorts are most successful and where improvements may be needed. The limitations of this analysis, such as not accounting for customer lifetime value differences, should be considered when interpreting the results.

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Key Insights

Cohort Performance Summary

Purpose

This section presents a detailed overview of each cohort’s performance, focusing on metrics like customer count, initial acquisition (period 0) retention, early retention (period 1), average retention across all periods, and total revenue contribution. Understanding these metrics helps identify the strongest and weakest cohorts, providing insights into what drives success in customer retention and revenue generation.

Key Findings

  • Period 0 Retention: Cohort 2024-05 had 100% retention at the initial purchase stage.
  • Period 1 Retention: Cohort 2024-06 showed 100% retention in the early stages, while cohort 2024-11 had lower early retention at 0%.
  • Average Retention: Cohort 2024-06 had the highest average retention at 28.57%, while cohort 2024-11 had the lowest at 14.29%.
  • Total Revenue: Cohort 2024-10 contributed the highest total revenue of $5416.74.

Interpretation

The data reveals that cohort 2024-06 stands out as the strongest cohort with high initial and early retention rates, leading to the highest average retention and a significant total revenue contribution. In contrast, cohort 2024-11 shows lower retention rates across all periods, impacting its total revenue generation potential.

Context

These cohort performance metrics provide valuable insights into customer retention and revenue generation patterns, helping the e-commerce store understand which cohorts are most successful and where improvements may be needed. The limitations of this analysis, such as not accounting for customer lifetime value differences, should be considered when interpreting the results.

Overall Statistics

Summary Metrics and Benchmarks

OS

Overall Statistics

Aggregate Retention Metrics

7
Total Orders

Overall retention metrics and cohort analysis summary statistics

metric_name value
total_orders 41
total_customers 12
total_cohorts 5
avg_cohort_size 2.4
overall_retention_rate 43.33
strongest_cohort 2024-06
weakest_cohort 2024-05
Summary Stats
Key Metrics
Benchmarks
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Key Insights

Overall Statistics

Purpose

This section provides a snapshot of key metrics from the overall cohort analysis, including total orders, unique customers, cohort count, average cohort size, and the overall retention rate at period 1. These metrics serve as benchmarks for evaluating customer retention patterns over time.

Key Findings

  • Total Orders: 41 - Indicates the volume of orders within the analyzed cohorts.
  • Unique Customers: 12 - Represents the number of distinct customers contributing to the orders.
  • Average Cohort Size: 2.4 - Shows the average number of customers per cohort.
  • Overall Retention Rate: 43.33% - Reflects the percentage of customers retained from the initial cohort to period 1.

Interpretation

The metrics reveal that the analysis covers 5 cohorts with varying sizes and retention rates. The average cohort size of 2.4 suggests relatively small cohorts, while the retention rate of 43.33% at period 1 indicates moderate success in retaining customers across cohorts.

Context

These metrics provide a foundational understanding of customer retention dynamics within the analyzed cohorts. The insights derived from these metrics can guide strategies to enhance customer loyalty and improve overall retention rates over time.

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Key Insights

Overall Statistics

Purpose

This section provides a snapshot of key metrics from the overall cohort analysis, including total orders, unique customers, cohort count, average cohort size, and the overall retention rate at period 1. These metrics serve as benchmarks for evaluating customer retention patterns over time.

Key Findings

  • Total Orders: 41 - Indicates the volume of orders within the analyzed cohorts.
  • Unique Customers: 12 - Represents the number of distinct customers contributing to the orders.
  • Average Cohort Size: 2.4 - Shows the average number of customers per cohort.
  • Overall Retention Rate: 43.33% - Reflects the percentage of customers retained from the initial cohort to period 1.

Interpretation

The metrics reveal that the analysis covers 5 cohorts with varying sizes and retention rates. The average cohort size of 2.4 suggests relatively small cohorts, while the retention rate of 43.33% at period 1 indicates moderate success in retaining customers across cohorts.

Context

These metrics provide a foundational understanding of customer retention dynamics within the analyzed cohorts. The insights derived from these metrics can guide strategies to enhance customer loyalty and improve overall retention rates over time.

Period Trends

Cross-Cohort Retention Patterns

PT

Period Trends

Cross-Cohort Retention Patterns

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Periods Tracked

Average retention rates by period across all cohorts showing typical customer lifecycle patterns

period avg_retention_rate min_retention max_retention cohort_count
0.000 100.000 100.000 100.000 5.000
1.000 43.330 0.000 100.000 5.000
2.000 23.330 0.000 100.000 5.000
3.000 0.000 0.000 0.000 5.000
4.000 0.000 0.000 0.000 5.000
5.000 0.000 0.000 0.000 5.000
6.000 0.000 0.000 0.000 5.000
Period Analysis
Retention Trends
Lifecycle Patterns
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Key Insights

Period Trends

Purpose

This section displays the average, minimum, and maximum retention rates for each period across all cohorts. It highlights typical customer lifecycle patterns, especially the initial drop-off between period 0 and 1, followed by potential stabilization. Understanding these trends helps in setting realistic retention benchmarks and identifying crucial points in the customer journey.

Key Findings

  • Average Retention Rate: Period 0 starts with 100% retention, but drops to 43.33% in period 1, then declines further.
  • Minimum Retention: Some cohorts show 0% retention in later periods, indicating customer churn.
  • Maximum Retention: No cohort maintains 100% retention beyond period 0, suggesting challenges in retaining all customers over time.

Interpretation

The decline in retention rates from period 0 to subsequent periods signifies the challenge of retaining customers over time. This data can guide strategies to improve retention, such as targeted marketing or enhancing customer experience post-purchase.

Context

These period trends provide a snapshot of how retention changes over time across all cohorts. Understanding these patterns can help in refining customer retention strategies and predicting customer behavior in subsequent periods.

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Key Insights

Period Trends

Purpose

This section displays the average, minimum, and maximum retention rates for each period across all cohorts. It highlights typical customer lifecycle patterns, especially the initial drop-off between period 0 and 1, followed by potential stabilization. Understanding these trends helps in setting realistic retention benchmarks and identifying crucial points in the customer journey.

Key Findings

  • Average Retention Rate: Period 0 starts with 100% retention, but drops to 43.33% in period 1, then declines further.
  • Minimum Retention: Some cohorts show 0% retention in later periods, indicating customer churn.
  • Maximum Retention: No cohort maintains 100% retention beyond period 0, suggesting challenges in retaining all customers over time.

Interpretation

The decline in retention rates from period 0 to subsequent periods signifies the challenge of retaining customers over time. This data can guide strategies to improve retention, such as targeted marketing or enhancing customer experience post-purchase.

Context

These period trends provide a snapshot of how retention changes over time across all cohorts. Understanding these patterns can help in refining customer retention strategies and predicting customer behavior in subsequent periods.

Job ID: test_commerce__shopify__customers__retention_cohort_20251221_115458
Secret Hash: 36f2d40ab80e