A prediction approach for precise marketing based on ARIMA-ARCH Model: A case of China Mobile

The autoregressive integrated moving average (ARIMA) model presents improved performance in forecasting short-term trends because it considers the dependence of time series and the interference of stochastic volatility. Thus, in this study, we establish ARIMA(0, 2, 1) based on the historical data of large-scale online marketing promotions to realize precise marketing of China Mobile's Ling Xi Voice app in the communication market. We eliminate the auto-regression effect of residual series by establishing the ARIMA model combined with the autoregressive conditional heteroskedasticity (ARCH) model denoted as ARIMA(0, 2, 1) − ARCH(1), the ARIMA model combined with the generalized ARCH (GARCH) model denoted as ARIMA(0, 2, 1) − GARCH(1, 1), and the ARIMA model combined with the threshold GARCH model denoted as ARIMA(0, 2, 1) − TGARCH(2, 1). The performance of the aforementioned models is then compared for validation. Considering the characteristics of the communication markets and the attractive statistical properties of ARIMA, we apply ARIMA(0, 2, 1) to forecast the cumulative number of Ling Xi Voice app users for precise marketing that offers reliable agreement for China Mobile to further advertise and study the market demand. Our analysis contributes toward the development of the current knowledge on forecasting the number of app users in the communication market and provides a new idea to increase the market share for communication operators.