USING BAIDU INDEX TO NOWCAST MOBILE PHONE SALES IN CHINA

Most of the official data are released with a lag period, which increases the difficulties for decision-makers assessing the situation. To solve the problem of data lag, we used real-time Baidu Index to nowcast the Chinese consumer behavior of buying the best-selling smartphone, Huawei Mate 7. We introduced keywords like “Mate 7” and “Huawei” in Baidu Index search queries to examine whether the introduction of real-time data can improve the efficiency of benchmark model. Overall, our finding is that the introduction of Baidu Index, both in-sample and out-of-sample, can improve the prediction accuracy of the model significantly. The extended model provided a 55.2% outperformance relative to benchmark one. This can not only make up for official data release lag, but also help firms gain near-real-time insight into the consumer demand trends and reduce inventory costs. The findings suggest that firms can improve marketing performance by use of search engine promotion campaign.

[1]  John H. Gerdes,et al.  Using web-based search data to predict macroeconomic statistics , 2005, CACM.

[2]  Meltem Gulenay Chadwick,et al.  Nowcasting the Unemployment Rate in Turkey: Let's Ask Google , 2015 .

[3]  Guillaume Thierry,et al.  Non-selective lexical access in bilinguals is spontaneous and independent of input monitoring: Evidence from eye tracking , 2013, Cognition.

[4]  H. Varian,et al.  Predicting the Present with Google Trends , 2012 .

[5]  Jeremy Ginsberg,et al.  Detecting influenza epidemics using search engine query data , 2009, Nature.

[6]  Frédéric Karamé,et al.  Can Google Data Help Predict French Youth Unemployment , 2012 .

[7]  Guohua Zou,et al.  Least squares model averaging by Mallows criterion , 2010 .

[8]  N. Askitas,et al.  Google Econometrics and Unemployment Forecasting , 2009, SSRN Electronic Journal.

[9]  Prasan Kumar Sahoo,et al.  Analyzing Healthcare Big Data With Prediction for Future Health Condition , 2016, IEEE Access.

[10]  Xiankai Huang,et al.  The Baidu Index: Uses in predicting tourism flows –A case study of the Forbidden City , 2017 .

[11]  Xin Yang,et al.  Forecasting Chinese tourist volume with search engine data , 2015 .

[12]  Ladislav Kristoufek,et al.  Nowcasting Unemployment Rates with Google Searches: Evidence from the Visegrad Group Countries , 2014, PloS one.

[13]  B. Hansen.  Least Squares Model Averaging , 2007 .

[14]  Steven F. Lehrer,et al.  Box Office Buzz: Does Social Media Data Steal the Show from Model Uncertainty When Forecasting for Hollywood? , 2016, Review of Economics and Statistics.

[15]  Tian Xie,et al.  Prediction model averaging estimator , 2015 .

[16]  Greg Tkacz,et al.  Predicting Recessions in Real-Time: Mining Google Trends and Electronic Payments Data for Clues , 2013 .

[17]  Torsten Schmidt,et al.  A monthly consumption indicator for Germany based on Internet search query data , 2010 .

[18]  H. Varian,et al.  Predicting the Present with Google Trends , 2009 .

[19]  Juri Marcucci,et al.  'Google It!' Forecasting the US Unemployment Rate with A Google Job Search Index , 2010 .

[20]  Yue Chen,et al.  Data mining from web search queries: A comparison of google trends and baidu index , 2015, J. Assoc. Inf. Sci. Technol..