User 136 · Retail · Transactions · Price Elasticity
Executive Summary

Executive Summary

Overall price elasticity findings

Overall Elasticity
-1.002
Conventional Elasticity
-0.944
Organic Elasticity
-1.752
R-Squared
0.986
Observations
10000
Regions Analyzed
54
Revenue-Maximizing Price
0.6
Avocado demand is inelastic: a 1% price increase is associated with a 1% decline in units sold (overall elasticity = -1, R² = 0.986). Conventional avocados have an elasticity of -0.94 vs -1.75 for organic, suggesting organic buyers are less price-sensitive. The revenue-maximizing price point is approximately $0.6.
Interpretation

Avocado demand is inelastic: a 1% price increase is associated with a 1% decline in units sold (overall elasticity = -1, R² = 0.986). Conventional avocados have an elasticity of -0.94 vs -1.75 for organic, suggesting organic buyers are less price-sensitive. The revenue-maximizing price point is approximately $0.6.

Visualization

Price Elasticity Coefficient

Log-log regression coefficient on log(price) with 95% confidence interval

Interpretation

The estimated price elasticity of demand is -1.002 (95% CI: -1.028 to -0.975). This means a 1% increase in price is associated with a 1.002% change in quantity demanded. The model explains 98.6% of variation in log sales volume after controlling for type, region, and year.

Visualization

Elasticity by Type: Conventional vs Organic

Separate price elasticity estimates for conventional and organic avocados

Interpretation

Conventional avocados have a price elasticity of -0.944 while organic avocados have an elasticity of -1.752. The difference of 0.808 elasticity units indicates organic buyers are more price-sensitive than conventional buyers. Both estimates control for region and year fixed effects.

Visualization

Regional Elasticity Ranking

Top 10 US regions ranked by price elasticity magnitude

Interpretation

Across 54 US regions analyzed, Buffalo Rochester shows the highest price sensitivity with an elasticity of -1.493. The chart shows the 10 most price-sensitive regions. Regions with more negative elasticity have consumers who react more strongly to price changes — useful for targeting promotional pricing.

Visualization

Log-Price vs Log-Volume

Log-log scatter plot showing the constant-elasticity demand relationship

Interpretation

The log-log scatter confirms a clear negative linear relationship between log(price) and log(volume), consistent with constant price elasticity = -1.002. Color separates conventional (higher volume) from organic (higher price) avocados. The slope of this relationship is the elasticity estimate. R² = 0.986.

Visualization

Weekly Volume Trend (2015–2018)

Total weekly avocado sales volume by product type over the study period

Interpretation

Weekly sales volume shows seasonal peaks and an upward trend in conventional avocado demand over the 2015–2018 period. Organic avocado volumes are consistently lower but growing faster in relative terms. Seasonal demand spikes (especially around Super Bowl and spring months) can temporarily alter the price-elasticity relationship.

Visualization

Revenue Optimization Curve

Revenue = price × predicted volume across a range of price scenarios

Interpretation

Based on the estimated elasticity (|e| > 1), revenue is highest at the lowest tested price of $0.6 (predicted revenue ≈ 96,131 units × $0.6). The curve is monotonically decreasing across the tested range — every price increase beyond $0.6 loses more volume than it gains in margin. This reflects the strongly elastic demand estimated for avocados: consumers are highly responsive to price changes in this category.

Visualization

Residuals vs Fitted

Regression diagnostics: residuals vs fitted values for the log-log model

Interpretation

The residuals plot shows whether the constant-elasticity assumption holds. A random scatter around zero with no funnel or curved pattern confirms the model is well-specified. Systematic patterns would indicate the true demand curve is non-constant-elasticity and a more flexible model might be needed. The log-log model achieves R² = 0.986.

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