Estimating the market potential pre-launch with …...Estimating the market potential pre-launch with search traffic Oliver Schaer Nikolaos Kourentzes Robert Fildes 38th International

Estimating the market potential pre-launch with search traffic

Oliver SchaerNikolaos Kourentzes

Robert Fildes

38th International Symposium on Forecasting18th of June 2018

Motivation for generating pre-launch forecasts

Increased competition lead to shorter life-cycles and faster introduction of new products

Forecast needed to adjust marketing planning and operations

Difficult to estimate the market potential (Goodwin et al. 2014)

• Expert judgment bias when forecasting new products (Belvedere & Goodwin 2017; Tyebjee 1987)

• Consumer preferences change during pre-launch phase (Meeran et al. 2017)

Obtaining pre-launch market potential estimates

By expert judgement

• Survey (Lee et al. 2014, Kim et al. 2013, Bass et al. 2001)

By analogy

• Parameters from previous or similar products, i.e. product attributes (Kim et al. 2014, Goodwin et al. 2013, Lee et al. 2003,

Lillien et al. 2000)

By market research

• Conjoint (Orbach & Fruchter 2011, Lee et al. 2006)

• Pre-orders (Moe & Fader 2002)

Obtaining pre-launch market potential estimates

By expert judgement

• Survey (Lee et al. 2014, Kim et al. 2013, Bass et al. 2001)

By analogy

• Parameters from previous or similar products, i.e. product attributes (Kim et al. 2014, Goodwin et al. 2013, Lee et al. 2003,

Lillien et al. 2000)

By market research

• Conjoint (Orbach & Fruchter 2011, Lee et al. 2006)

• Pre-orders (Moe & Fader 2002)

Last year’s ISF

Pre-release buzz (PRB)

PRB is the aggregate anticipation of consumers towards a new product (Houston et al. 2018)

• includes (i) communication, (ii) search, and(iii) participation in experiential activities

Why PRB supposed to work?

• Less information compared to post-launch forecasting available, i.e. univariate information

• PRB has smaller potential to have endogeneity than post-release phase

• Several studies which include pre-release buzz report increase in forecast accuracy.

Pre-release buzz forecasting literature

Investigating pre-launch buzz for sequential products

Most products nowadays are not entirely new but released as sequential.

• What is the relation of pre-launch buzz to the market parameter for sequential product releases?

• Pre-release buzz also able to improve long-term forecasts and what lead time can we achieve?

Using direct analogy of previous releases requires very few training sample.

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Sales for Assassin's Creed

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Google Trends

Proposed methodology0

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j=1 j=2 j=3 j4

Sales

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Search traffic

Predicting the market potential withpre-release buzz (PRB)

In addition we define the same models also in an additive way

Empirical evaluation on 56 franchises (257 games)

Google Trends with game title as keyword:

Set of parametric diffusion models

Smoothed adoption shape of j-1 as non-parametric model

Average Relative Absolute Error on the cumulative

adoption with mj-1 (Davydenko & Fildes 2013)

w = 4 to 12 weeks Google Trends window l = 1 to 12 weeks lead time

PRBj

Overall performance

Performance of GT percentage across lead times

Forecasting accuracy gains available 10 weeks before release

The story so far for pre-release buzz

Managerial implications for pre-launch buzz:• Automated pre-launch forecasting process improving

accuracy by up to 15% compared to established analogy based methods

• Search traffic information providing up to 10 weeks lead time

• Good accuracy with simplest model that is available for the 2nd product generation

Further research

• Can we improve the accuracy by including trend direction

• Compare to other marketing variables such as ad spending

• Can this information also be used to allocate success of products by just looking into the pre-launch buzz?

Thank you!

[email protected]@oliverschaer

github.com/mamut86

References

Asur, S., Huberman, B., Aug 2010. Predicting the future with social media. In: Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference on. Vol. 1. pp. 492-499.

Belvedere, V., Goodwin, P., 2017. The inuence of product involvement and emotion on short-term product demand forecasting. International Journal of Forecasting 33 (3), 652-661.

Craig, C. S., Greene, W. H., Versaci, A., 2015. E-word of mouth: Early predictor of audience engagement. Journal of Advertising Research 55 (1), 62-72.

Davydenko, A. and Fildes, R., 2013. Measuring forecasting accuracy: The case of judgmental adjustments to SKU-level demand forecasts. International Journal of Forecasting, 29, 510-522.

Dhar, V., Chang, E. A., 2009. Does chatter matter? The impact of user-generated content on music sales. Journal of Interactive Marketing 23 (4), 300-307.

Ding, C., Cheng, H. K., Duan, Y., Jin, Y., 2017. The power of the like button: The impact of social media on box office. Decision Support Systems 94, 77-84.

Divakaran, P. K. P., Palmer, A., Sndergaard, H. A., Matkovskyy, R., 2017. Prelaunch prediction of market performance for short lifecycle products using online community data. Journal of Interactive Marketing 38, 12-28.

Foutz, N.Z. & Jank, W. 2010. Research Note – Prerelease Demand Forecasting for Motion Pictures Using Functional Shape Analysis of Virtual Stock Markets. Marketing Science, 29(2), 568-579.

References

Gelper, S., Peres, R., Eliashberg, J., 2015. Pre-release word-of-mouth dynamics: The role of spikes. Erasmus University Rotterdam working paper, 1-40, Working paper 14th Sept.

Goodwin, P., Meeran, S., Dyussekeneva, K., 2014. The challenges of pre-launch forecasting of adoption time series for new durable products. International Journal of Forecasting 30 (4), 1082-1097.

Hann, I.-H., Oh, J., James, G., 2011. Forecasting the sales of music albums: A functional data analysis of demand and supply side p2p data. Working Paper

Houston, M. B., Kupfer, A.-K., Hennig-Thurau, T. and Spann, M., 2018. Pre-release consumer buzz. Journal of the Academy of Marketing Science, 1-23 (Pre-print).

Kim, H., Hanssens, D. M., 2017. Advertising and word-of-mouth effects on pre-launch consumer interest and initial sales of experience products. Journal of Interactive Marketing 37, 57-74.

Kim, T., Hong, J., Kang, P., 2015. Box office forecasting using machine learning algorithms based on SNS data. International Journal of Forecasting 31 (2), 364-390.

Kim, T., Hong, J., Koo, H., 2013. Forecasting diffusion of innovative technology at prelaunch: A survey based method. Industrial Management & Data Systems 113 (6), 800-816.

Kulkarni, G., Kannan, P., Moe, W., 2012. Using online search data to forecast new product sales. Decision Support Systems 52 (3), 604-611.

Lee, H., Kim, S. G., woo Park, H., Kang, P., 2014. Pre-launch new product demand forecasting using the bass model: A statistical and machine learning-based approach. Technological Forecasting and Social Change 86, 49-64.

References

Lee, H., Kim, S. G., Woo Park, H., Kang, P., 2014. Pre-launch new product demand forecasting using the bass model: A statistical and machine learning-based approach. Technological Forecasting and Social Change 86, 49-64.

Lee, J., Boatwright, P., Kamakura, W. A., 2003. A bayesian model for prelaunch sales forecasting of recorded music. Management Science 49 (2), 179-196.

Lee, J., Cho, Y., Lee, J.-D., Lee, C.-Y., 2006. Forecasting future demand for large screen television sets using conjoint analysis with diffusion model. Technological Forecasting and Social Change 73 (4), 362-376.

Lilien, G. Y., Rangaswamy, A., Van den Bulte, C., 2000. Diffusion models: Managerial applications and software. In: Mahajan, V., Muller, E., Wind, Y. (Eds.), New-Product Diffusion Models. Kluwer, Massachusetts, pp. 295-311.

Liu, Y., 2006. Word of mouth for movies: Its dynamics and impact on box office revenue. Journal of Marketing 70 (3), 74-89.

Meeran, S., Jahanbin, S., Goodwin, P., Neto, J. Q. F., 2017. When do changes in consumer preferences make forecasts from choice-based conjoint models unreliable? European Journal of Operational Research 258 (2), 512-524.

Meeran, S., Jahanbin, S., Goodwin, P., Neto, J. Q. F., 2017. When do changes in consumer preferences make forecasts from choice-based conjoint models unreliable? European Journal of Operational Research 258 (2), 512-524.

Moe, W. W., Fader, P. S., 2002. Fast-track: Article using advance purchase orders to forecast new product sales. Marketing Science 21 (3), 347-364.

References

Mülbacher, H., Füller, J., Huber, L., 2011. Online forum discussion-based forecasting of new product market performance. Marketing ZFP 33 (3), 221-234.

Orbach, Y., Fruchter, G. E., 2011. Forecasting sales and product evolution: The case of the hybrid/electric car. Technological Forecasting and Social Change 78 (7), 1210-1226.

Tian, C. H., Wang, Y., Mo, W. T., Huang, F. C., Dong, S., Huang, 2014. Prerelease sales forecasting: A model-driven context feature extraction approach. IBM Journal of Research and Development 58 (5/6), 8:1-8:13.

Tyebjee, T. T., 1987. Behavioral biases in new product forecasting. International Journal of Forecasting 3 (3), 393-404.

Wang, F., Zhang, Y., Li, X., Zhu, H., 2010. Why do moviegoers go to the theater? The role of prerelease media publicity and online word of mouth in driving movie going behavior. Journal of Interactive Advertising 11 (1), 50-62.

Xiong, G. and Bharadwaj, S., 2014. Prerelease Buzz Evolution Patterns and New Product Performance. Marketing Science, 33 (3), 401-421.