Analysis Overview
Price Elasticity Analysis Configuration
Analysis overview and configuration
test_1766619639
Key Insights
Analysis Overview
Purpose
This section provides insights into the key metrics and data characteristics of the Shopify Product Price Elasticity Analysis conducted by E-commerce Analytics on December 24, 2025.
Key Findings
- Average Elasticity: -1.51 - Indicates that, on average, a 1% increase in price leads to a 1.51% decrease in quantity demanded.
- Percentage of Elastic Products: 83.33% - Shows that the majority of products analyzed are price-sensitive.
- Overall R-squared: 0.869 - Indicates that the regression model explains 86.9% of the variability in quantity demanded.
Interpretation
The analysis successfully estimated price elasticity, highlighting that most products are price-sensitive. The high R-squared value suggests a strong fit of the regression model in explaining demand variation based on price changes.
Context
The analysis assumes a log-linear relationship between price and quantity, constant elasticity, and independent observations across products. Limitations include the lack of historical sales data and consideration of cross-price elasticity between products.
Key Insights
Analysis Overview
Purpose
This section provides insights into the key metrics and data characteristics of the Shopify Product Price Elasticity Analysis conducted by E-commerce Analytics on December 24, 2025.
Key Findings
- Average Elasticity: -1.51 - Indicates that, on average, a 1% increase in price leads to a 1.51% decrease in quantity demanded.
- Percentage of Elastic Products: 83.33% - Shows that the majority of products analyzed are price-sensitive.
- Overall R-squared: 0.869 - Indicates that the regression model explains 86.9% of the variability in quantity demanded.
Interpretation
The analysis successfully estimated price elasticity, highlighting that most products are price-sensitive. The high R-squared value suggests a strong fit of the regression model in explaining demand variation based on price changes.
Context
The analysis assumes a log-linear relationship between price and quantity, constant elasticity, and independent observations across products. Limitations include the lack of historical sales data and consideration of cross-price elasticity between products.
Data Preprocessing
Data Quality & Product Filtering
Data preprocessing and column mapping
Key Insights
Data Preprocessing
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: 173 - The original dataset size before cleaning
- Final Rows: 30 - The number of rows retained after data cleaning
- Rows Removed: 143 - The count of rows removed during preprocessing
- Retention Rate: 17.3% - Percentage of data retained after cleaning
Interpretation
The significant reduction in data points (143 out of 173 removed) indicates a rigorous data cleaning process. The 17.3% retention rate suggests that the analysis is now based on a more refined and potentially more reliable subset of the original data.
Context
The data preprocessing decisions directly impact the accuracy and reliability of the price elasticity analysis. By removing outliers and irrelevant data points, the analysis can focus on a more representative sample, potentially leading to more accurate insights for optimizing pricing strategies.
Key Insights
Data Preprocessing
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: 173 - The original dataset size before cleaning
- Final Rows: 30 - The number of rows retained after data cleaning
- Rows Removed: 143 - The count of rows removed during preprocessing
- Retention Rate: 17.3% - Percentage of data retained after cleaning
Interpretation
The significant reduction in data points (143 out of 173 removed) indicates a rigorous data cleaning process. The 17.3% retention rate suggests that the analysis is now based on a more refined and potentially more reliable subset of the original data.
Context
The data preprocessing decisions directly impact the accuracy and reliability of the price elasticity analysis. By removing outliers and irrelevant data points, the analysis can focus on a more representative sample, potentially leading to more accurate insights for optimizing pricing strategies.