Price Elasticity - How Sensitive are Sales to Changes in Prices скачать в хорошем качестве

Price Elasticity - How Sensitive are Sales to Changes in Prices

Price elasticity measures how sensitive sales volume is to changes in price and helps explain how demand responds when a product becomes more or less expensive. In this example, dynamic daily sample data was created for a full year, including price, competitor price, promotions, weekends, seasonality, quantity sold, and revenue. The synthetic data was designed so that quantity sold would decrease when price rises, while also being influenced by promotions, competitor pricing, and timing effects. A log-log regression model was then estimated using price, promotion, competitor price, and weekend indicators to explain sales volume. The estimated price elasticity was about -1.492, meaning that a 1% increase in price is associated with roughly a 1.49% decrease in quantity sold. This result suggests demand is elastic, since customers appear fairly sensitive to price changes. The regression also showed that promotions increase sales, higher competitor prices tend to boost demand for the product, and weekends slightly improve sales as well. Several Plotly charts were used to visualize the problem, including a scatterplot of price versus quantity sold, a demand curve with a fitted line, and a chart comparing actual sales with model-predicted sales over time. The analysis was then extended with interactive sliders for a company’s own price, competitor price, and unit cost so revenue and profit curves could be explored dynamically. Overall, the project shows how synthetic data, regression modeling, and interactive visualization can be combined to estimate elasticity, interpret pricing sensitivity, and identify revenue-maximizing or profit-maximizing prices.