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Evaluating the use of internet search volumes for time series modeling of sales in the video game industry

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Abstract

Internet search volumes have been successfully adopted for time series analysis of different phenomena. This empirical paper evaluates the feasibility of search volumes in modeling of weekly video game sales. Building on the theoretical concepts of product life cycle, diffusion, and electronic word-of-mouth advertisement, the empirical analysis concentrates on the hypothesized Granger causality between sales and search volumes. By using a bivariate vector autoregression model with a dataset of nearly a hundred video games, only a few games exhibit such causality to either direction. When correlations are present, these rather occur instantaneously; the current weekly amount of sales tends to mirror the current weekly amount of searches. According to the results, search volumes contribute only a limited additional statistical power for forecasting, however. Besides this statistical limitation, the presented evaluation reveals a number of other limitations for use in practical marketing and advertisement foresight. Internet search volumes continue to provide a valuable empirical instrument, but the value should not be exaggerated for time series modeling of video game sales.

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Notes

  1. In this paper, the concept of precedence is understood in the sense of Granger’s (1988) classical exposition. While the term Granger causality is often used interchangeably, causality itself is a much more problematic concept, of course – as are the related concepts such as exogeneity (here see, e.g., Lütkepohl 2005). This terminological disclaimer should be kept in mind throughout the paper.

  2. Although cumulative sales and search volumes are used for a few illustrations, formal estimation is carried out with the non-manipulated weekly S t and G t time series.

  3. A few details are also worth remarking. First, Google defines some characters (such as the plus sign) invalid; these were omitted from a few names. Second, release information (see “Deterministic variables”) was also queried with the same strategy, and, hence, special editions and other non-standard release types are counted as separate games insofar these carry different names. Third, the platform-specific releases of a few outliers (notably, Super Mario Bros.) are classified by VGChartz Ltd. (2016) as separate games, although these are collapsed into one composite for most other sampled games.

  4. To clarify, a simple leading indicator model (Choi and Varian 2012), \(\mathrm {S}_{t} = \mu + {\sum }_{i=1}^{p} \alpha _{i} \mathrm {S}_{t-i} + \beta _{1} \mathrm {G}_{t} + \beta _{2} d_{t} + \varepsilon _{t}\) could be used as a simple benchmark model for the VAR models, among other algebraic variations of the equation. Another option might be to investigate the effect of deterministic trend functions in the VAR specifications. Such models should be further benchmarked against the common (Chintagunta et al. 2009; Ruohonen et al. 2015a) non-linear diffusion models. Finally, it should be remarked that predicting a game’s later life cycle may not be a realistic scenario in practice. Therefore, further empirical experiments are required for forecasting time periods that are closer to video game release dates.

  5. It can be further remarked that some studies have tried to balance these issues (including the indexing problem) by scaling the observed search volumes with a generic high-frequency search term (Curme et al. 2014). As said, however, it arguably remains unclear how useful such scaling is without knowing technical details about the proprietary Google search engine.

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Correspondence to Jukka Ruohonen.

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Ruohonen, J., Hyrynsalmi, S. Evaluating the use of internet search volumes for time series modeling of sales in the video game industry. Electron Markets 27, 351–370 (2017). https://doi.org/10.1007/s12525-016-0244-z

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