This study investigated coffeemaker search interest in the United States using the monthlytime series data from Google Trends. The forecasting model developed can be utilized as a part of the coffeemaker market research since accurately forecasting user interest would enable whoever is intrigued to anticipate future developments and make informed decisions. To analyze the underlying pattern, the data was decomposed with STL into seasonal, trend, and residual components. We observed a consistent annual seasonality with a surge in interest every November and December. This pattern was attributed to the increase in user interest during the end-of-the-year holiday season sales. Anomaly detection using the STL residuals found two anomalies. The anomaly witnessed in December 2020 is best understood as the result of the demand surge during the holiday season compounded by the adoption of online shopping imposed by the COVID-19 lockdown. For the model selection process, ACF and PACF plots were used to make the initial judgments on the parameters of the time series model. The first round of model selection tested potential AR and MA orders. The second round of model selection tested potential seasonal AR and MA orders. SARIMA(0, 1, 2)×(1, 0, 1)12 is the final model, chosen based on AIC and BIC scores. This model was able to capture the annual seasonal pattern and meet the stationary assumption with first-order differencing. The model has a MAPE of 4.3% and a RMSE of 3.841 with the rolling forecast origin prediction on the out-sample set. The residuals were confirmed to be white noise, which indicates the SARIMA model is a good fit for predicting the monthly coffeemaker search interest in the United States.