← Back to Analysis Directory Sample Report: Revenue Trend Analysis

OV

Overview

Analysis overview and configuration

Revenue Trend
Shopify Store
Analyze revenue trends to determine if the business is growing, declining, or stable
Module Configuration
aggregation_period week
Processing ID
test_1766281052
analysis type: Revenue Trend
company: Shopify Store
objective: Analyze revenue trends to determine if the business is growing, declining, or stable
IN

Key Insights

Overview

Purpose

This section provides insights into the revenue trend analysis conducted for a Shopify Store, focusing on key metrics, data characteristics, and the business objective of understanding revenue trends.

Key Findings

  • Trend Direction: Stable - Indicates consistent revenue performance over the analyzed period.
  • Total Revenue: $4,860 - Represents the overall revenue generated during the analysis.
  • Trend Slope: $273.4 per week - Shows the average increase in revenue per week.
  • Model Fit (R-squared): 49.5% - Indicates the proportion of variance in revenue explained by the time index.
  • Growth Rate: Average growth rate of 220.44 - Demonstrates the average change in revenue over time.

Interpretation

The analysis reveals a stable revenue trend with a moderate growth rate and a significant total revenue figure. The model explains around 49.5% of the revenue variation, suggesting a fair fit. The trend slope of $273.4 per week indicates a positive revenue trajectory for the Shopify Store.

Context

The analysis provides valuable insights into revenue trends but may benefit from additional context on external factors influencing revenue fluctuations for a more comprehensive understanding.

IN

Key Insights

Overview

Purpose

This section provides insights into the revenue trend analysis conducted for a Shopify Store, focusing on key metrics, data characteristics, and the business objective of understanding revenue trends.

Key Findings

  • Trend Direction: Stable - Indicates consistent revenue performance over the analyzed period.
  • Total Revenue: $4,860 - Represents the overall revenue generated during the analysis.
  • Trend Slope: $273.4 per week - Shows the average increase in revenue per week.
  • Model Fit (R-squared): 49.5% - Indicates the proportion of variance in revenue explained by the time index.
  • Growth Rate: Average growth rate of 220.44 - Demonstrates the average change in revenue over time.

Interpretation

The analysis reveals a stable revenue trend with a moderate growth rate and a significant total revenue figure. The model explains around 49.5% of the revenue variation, suggesting a fair fit. The trend slope of $273.4 per week indicates a positive revenue trajectory for the Shopify Store.

Context

The analysis provides valuable insights into revenue trends but may benefit from additional context on external factors influencing revenue fluctuations for a more comprehensive understanding.

PP

Data Pipeline

17
Clean Records

Data preprocessing and column mapping

Data Pipeline
80
Initial Records
17
Clean Records
Column Mapping
timestamp
→
Created at
value
→
Total
Filters Applied
Date from
:
2024-11-01
Date to
:
2024-12-15
17 Records
MCP Analytics
17 Records
IN

Key Insights

Data Pipeline

Purpose

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

Key Findings

  • Initial Rows: 80 - The original dataset size before cleaning.
  • Final Rows: 17 - The number of rows retained after data preprocessing.
  • Rows Removed: 63 - The count of observations removed during cleaning.
  • Retention Rate: 21.2% - Percentage of data retained after cleaning.

Interpretation

The data preprocessing significantly reduced the dataset size, retaining only 21.2% of the original observations. This reduction may impact the model’s robustness and generalizability due to the loss of data diversity.

Context

The low retention rate suggests potential data quality issues or outliers in the initial dataset. Understanding the impact of data cleaning on the analysis is crucial for interpreting the results accurately and assessing the model’s reliability.

IN

Key Insights

Data Pipeline

Purpose

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

Key Findings

  • Initial Rows: 80 - The original dataset size before cleaning.
  • Final Rows: 17 - The number of rows retained after data preprocessing.
  • Rows Removed: 63 - The count of observations removed during cleaning.
  • Retention Rate: 21.2% - Percentage of data retained after cleaning.

Interpretation

The data preprocessing significantly reduced the dataset size, retaining only 21.2% of the original observations. This reduction may impact the model’s robustness and generalizability due to the loss of data diversity.

Context

The low retention rate suggests potential data quality issues or outliers in the initial dataset. Understanding the impact of data cleaning on the analysis is crucial for interpreting the results accurately and assessing the model’s reliability.

TLDR

Executive Summary

Too Long; Didn't Read

4860
Total revenue

Key Performance Indicators

Trend direction
Stable
Total revenue
4,860.27
Slope pct
28.13
R squared
49.5%
P value
18.5%

Summary

Key findings

Finding Impact
Revenue trend is Stable Neutral
Total revenue: $4,860 Key Metric
Trend slope: $273.4 per week Positive
Model fit (R-squared): 49.5% Low Confidence

Executive Summary

Bottom Line: Revenue is Stable with no clear statistical significance (p = 0.1851).

Key Findings:
• Total revenue: $4,860
• Trend slope: +28.1% per week
• Model confidence: 49.5% (R²)
• Average growth rate: +220.4%

Recommendation: Revenue is stable. Look for opportunities to accelerate growth.

Statistical
Analysis
IN

Key Insights

Executive Summary

Purpose

This section provides a concise overview of the key findings and their implications from the revenue trend analysis.

Key Findings

  • Total Revenue: $4,860 - Indicates the overall revenue generated during the analyzed period.
  • Trend Slope: +28.1% per week - Shows the rate of revenue growth over time.
  • Model Confidence: 49.5% (R²) - Indicates the level of confidence in the linear regression model.
  • Average Growth Rate: +220.4% - Reflects the average rate of revenue growth observed.

Interpretation

The revenue trend is stable with a moderate growth rate of 28.1% per week. However, the model’s confidence level is relatively low at 49.5%, suggesting some uncertainty in the predictive power of the analysis.

Context

The stability of the revenue trend and the positive growth rate provide insights into the business’s performance. However, the low model confidence indicates the need for further validation or refinement of the analysis methodology.

IN

Key Insights

Executive Summary

Purpose

This section provides a concise overview of the key findings and their implications from the revenue trend analysis.

Key Findings

  • Total Revenue: $4,860 - Indicates the overall revenue generated during the analyzed period.
  • Trend Slope: +28.1% per week - Shows the rate of revenue growth over time.
  • Model Confidence: 49.5% (R²) - Indicates the level of confidence in the linear regression model.
  • Average Growth Rate: +220.4% - Reflects the average rate of revenue growth observed.

Interpretation

The revenue trend is stable with a moderate growth rate of 28.1% per week. However, the model’s confidence level is relatively low at 49.5%, suggesting some uncertainty in the predictive power of the analysis.

Context

The stability of the revenue trend and the positive growth rate provide insights into the business’s performance. However, the low model confidence indicates the need for further validation or refinement of the analysis methodology.

TR

Revenue Trend

Stable

Revenue trend over time with fitted regression line

Stable
trend direction
273.4
slope
28.13
slope pct
trend direction: Stable
slope: 273
slope pct: 28.13
IN

Key Insights

Revenue Trend

Purpose

This section highlights the revenue trend analysis, emphasizing the stability of the trend, the slope of $273.40 per week, the explained variance (R² = 0.4946), and the statistical significance (p = 0.1851). These metrics provide insights into the consistency and direction of revenue growth over time.

Key Findings

  • Trend Direction: Stable - Indicates consistent revenue performance.
  • Slope: $273.40 per week - Represents the average weekly revenue change.
  • R-squared: 0.4946 - Shows the proportion of variance in revenue explained by the model.
  • P-value: 0.1851 - Suggests the trend’s statistical significance is not strong.

Interpretation

The stable trend with a moderate positive slope implies a steady revenue increase over time. The R-squared value indicates that the linear model captures a substantial portion of the revenue variation, despite the p-value suggesting the trend’s statistical significance is not robust.

Context

The analysis provides valuable insights into revenue trends but may benefit from additional data points or alternative models to enhance the predictive power and significance of the findings.

IN

Key Insights

Revenue Trend

Purpose

This section highlights the revenue trend analysis, emphasizing the stability of the trend, the slope of $273.40 per week, the explained variance (R² = 0.4946), and the statistical significance (p = 0.1851). These metrics provide insights into the consistency and direction of revenue growth over time.

Key Findings

  • Trend Direction: Stable - Indicates consistent revenue performance.
  • Slope: $273.40 per week - Represents the average weekly revenue change.
  • R-squared: 0.4946 - Shows the proportion of variance in revenue explained by the model.
  • P-value: 0.1851 - Suggests the trend’s statistical significance is not strong.

Interpretation

The stable trend with a moderate positive slope implies a steady revenue increase over time. The R-squared value indicates that the linear model captures a substantial portion of the revenue variation, despite the p-value suggesting the trend’s statistical significance is not robust.

Context

The analysis provides valuable insights into revenue trends but may benefit from additional data points or alternative models to enhance the predictive power and significance of the findings.

PB

Period Breakdown

4860.27

Revenue breakdown by period with order counts

4860.27
total revenue
17
total orders
972.05
avg period revenue
total revenue: 4860
total orders: 17
avg period revenue: 972
IN

Key Insights

Period Breakdown

Purpose

This section provides a breakdown of revenue by period along with order counts over 5 weeks. It highlights the total revenue, total orders, and average weekly revenue to understand the revenue trends within specific time frames.

Key Findings

  • Total Revenue: $4860.27 - Indicates the overall revenue generated during the analyzed period.
  • Total Orders: 17 - Shows the total number of orders processed in the given time frame.
  • Average Period Revenue: $972.05 - Represents the average revenue earned per week.
  • Period Breakdown: Revenue varied from $118.8 to $1805.76 across the 5 periods.

Interpretation

The data reveals fluctuations in revenue and order counts over the 5-week period, with varying growth rates and order volumes. Understanding these variations can help in assessing the performance of the business over time and identifying potential factors influencing revenue changes.

Context

The period breakdown section offers a detailed view of revenue and order dynamics within specific time intervals, aiding in pinpointing trends and patterns that contribute to the overall revenue trend analysis conducted in the previous sections.

IN

Key Insights

Period Breakdown

Purpose

This section provides a breakdown of revenue by period along with order counts over 5 weeks. It highlights the total revenue, total orders, and average weekly revenue to understand the revenue trends within specific time frames.

Key Findings

  • Total Revenue: $4860.27 - Indicates the overall revenue generated during the analyzed period.
  • Total Orders: 17 - Shows the total number of orders processed in the given time frame.
  • Average Period Revenue: $972.05 - Represents the average revenue earned per week.
  • Period Breakdown: Revenue varied from $118.8 to $1805.76 across the 5 periods.

Interpretation

The data reveals fluctuations in revenue and order counts over the 5-week period, with varying growth rates and order volumes. Understanding these variations can help in assessing the performance of the business over time and identifying potential factors influencing revenue changes.

Context

The period breakdown section offers a detailed view of revenue and order dynamics within specific time intervals, aiding in pinpointing trends and patterns that contribute to the overall revenue trend analysis conducted in the previous sections.

GR

Growth Rates

220.44

Period-over-period growth rate analysis

220.44
avg growth rate
118.8
min revenue
1805.76
max revenue
avg growth rate: 220
min revenue: 119
max revenue: 1806
IN

Key Insights

Growth Rates

Purpose

This section highlights the average week-over-week growth rate (220.4%) and the revenue range ($119 to $1,806) to provide insights into the revenue trends and fluctuations over the analyzed period.

Key Findings

  • Average Growth Rate: 220.44% - Indicates the average percentage change in revenue week-over-week.
  • Revenue Range: $119 to $1,806 - Shows the spread of revenue values observed during the analysis period.
  • Pattern Observed: Revenue experienced significant fluctuations, with both high positive and negative growth rates.

Interpretation

The high average growth rate suggests substantial revenue changes week-to-week, indicating potential volatility in sales. The wide revenue range reflects the variability in income levels, which could impact financial planning and decision-making for the Shopify store.

Context

Understanding the revenue fluctuations and growth rates is crucial for assessing the store’s performance and identifying trends that may influence future business strategies. The data provides valuable insights into revenue dynamics but may require further analysis to uncover underlying factors driving these fluctuations.

IN

Key Insights

Growth Rates

Purpose

This section highlights the average week-over-week growth rate (220.4%) and the revenue range ($119 to $1,806) to provide insights into the revenue trends and fluctuations over the analyzed period.

Key Findings

  • Average Growth Rate: 220.44% - Indicates the average percentage change in revenue week-over-week.
  • Revenue Range: $119 to $1,806 - Shows the spread of revenue values observed during the analysis period.
  • Pattern Observed: Revenue experienced significant fluctuations, with both high positive and negative growth rates.

Interpretation

The high average growth rate suggests substantial revenue changes week-to-week, indicating potential volatility in sales. The wide revenue range reflects the variability in income levels, which could impact financial planning and decision-making for the Shopify store.

Context

Understanding the revenue fluctuations and growth rates is crucial for assessing the store’s performance and identifying trends that may influence future business strategies. The data provides valuable insights into revenue dynamics but may require further analysis to uncover underlying factors driving these fluctuations.

DG

Model Diagnostics

0.495

Model diagnostics and residual analysis

0.495
r squared
0.326
adj r squared
Not Significant
trend significance
r squared: 0.495
adj r squared: 0.326
trend significance: Not Significant
IN

Key Insights

Model Diagnostics

Purpose

This section focuses on model diagnostics and residual analysis to assess the fit of the linear regression model. It helps identify the variability and potential non-linear patterns or outliers in the data, providing insights into the model’s performance.

Key Findings

  • R-squared: 0.495 - Indicates that the model explains about 49.5% of the variance in the data.
  • Adjusted R-squared: 0.326 - This metric adjusts for the number of predictors in the model, showing a slightly lower explanatory power.
  • Trend Significance: Not Significant - Suggests that the trend observed may not be statistically significant.
  • Residuals: The residuals show a mean close to zero, but the skewness indicates potential non-normality in the data distribution.

Interpretation

The R-squared values suggest that the model captures a moderate amount of variation in revenue trends. The non-significant trend significance indicates caution in interpreting the linear relationship. The residual analysis helps identify areas where the model may not fit the data well.

Context

The residual analysis complements the overall analysis by providing insights into the model’s performance beyond just the trend direction. It highlights the need for further investigation into potential non-linear patterns or outliers that could impact the model’s accuracy.

IN

Key Insights

Model Diagnostics

Purpose

This section focuses on model diagnostics and residual analysis to assess the fit of the linear regression model. It helps identify the variability and potential non-linear patterns or outliers in the data, providing insights into the model’s performance.

Key Findings

  • R-squared: 0.495 - Indicates that the model explains about 49.5% of the variance in the data.
  • Adjusted R-squared: 0.326 - This metric adjusts for the number of predictors in the model, showing a slightly lower explanatory power.
  • Trend Significance: Not Significant - Suggests that the trend observed may not be statistically significant.
  • Residuals: The residuals show a mean close to zero, but the skewness indicates potential non-normality in the data distribution.

Interpretation

The R-squared values suggest that the model captures a moderate amount of variation in revenue trends. The non-significant trend significance indicates caution in interpreting the linear relationship. The residual analysis helps identify areas where the model may not fit the data well.

Context

The residual analysis complements the overall analysis by providing insights into the model’s performance beyond just the trend direction. It highlights the need for further investigation into potential non-linear patterns or outliers that could impact the model’s accuracy.

Job ID: test_commerce__shopify__orders__revenue_trend_20251220_173801
Secret Hash: 36f2d40ab80e