O. Schaer, N. Kourentzes, 2015, 27th European Conference on operational Research, Glasgow.
Public available search engine (SE) data such as from Google Trend can offer insights on the search interest for a specific product. Nowadays, customers often use search engines to inform themselves about a product prior to the purchase. This permits new modelling approaches that can improve forecasting accuracy by using it as a leading indicator. Several studies already focused on the correlation of SE data and actual sales but rather focused on brand-level or initial phase of a new product only. This study focuses on describing the relationship between search engine and sales data considering the entire product life span using weekly video games sales data. At first, we evaluate the suitability of search volume data as a leading indicator and focus especially on the detection of causal dependency, overcoming a common limitation in many studies using SE data. In particular, exploring the direction of causality is interesting, as during the mature phase of a product, search traffic can also be caused by users of already sold entities and therefore may no longer be causal of new sales. According to those findings we propose appropriate forecasting models that incorporate SE data for short and long-term forecasting and evaluate their accuracy using real sales data from the gaming industry.