Seasonal sales dynamics: an econometric analysis of coffee shop performance in New York City

This study analyzes the sales trends of three coffee shops in New York City using data collected between January and July 2023. The research employs a combination of time series analysis, statistical testing, clustering, and descriptive statistics to uncover significant patterns in sales performance. Through time series analysis, the study identifies trends and provides forecasts for future sales, offering valuable insights for operational decision-making. Key findings include the identification of several high-performing products, such as Earl Grey Regular, Dark Chocolate Large, and Morning Sunrise Chai Regular, each averaging over 25 units sold daily. These products, along with others like Latte, Peppermint Regular, and Colombian Medium Roast Regular, demonstrate strong market appeal and highlight consumer preferences. Additionally, the analysis shows that demand peaks between 7:00 AM and 10:00 AM, likely driven by morning commuters, making this window critical for staffing and inventory readiness. The time series plot reveals a clear upward trajectory in total sales over the study period, marked by periodic fluctuations that may be influenced by seasonality or marketing efforts. Using time series forecasting, the study projects sales for the next 30 days, predicting total sales to exceed 2,000 units, with a confidence interval to account for variability. This forecast plays a crucial role in guiding inventory management, staffing, and strategic planning, enabling the coffee shops to anticipate customer demand and streamline their operations effectively.