This content relates to : MARKETING INNOVATION
HIGHLIGHTS
Prerelease consumer buzz data enhances the accuracy of a new product’s sales forecast.
Prerelease consumer buzz dynamics (e.g., the trajectory of prerelease buzz evolution) has a higher forecast accuracy than aggregated data (e.g., average or total prerelease buzz volume).
Prerelease consumer buzz dynamics enables accurate forecast of a new product’s sales well in advance of its launch; it also influences the stock returns of the firm introducing the new product.
Guiyang Xiong
Syracuse University
It is important for firms to predict consumer demand of new product introductions as early as possible in order to wisely allocate resources and make necessary adjustments to product design and promotional strategy. This is because it is extremely difficult to make up for lost sales later to recoup the significant costs already incurred in the development, production, inventory, and marketing of the product. However, accurate sales forecast before product launch has been a challenging task for firms, because a truly new product does not have prior sales history and traditional consumer surveys that collect consumer opinions may not represent the entire potential consumer base.
This study proposes a new method to enhance the accuracy of new product sales forecast using information from prerelease consumer buzz. Consumers often generate buzz (e.g., blog or forum postings) about a new product before it is launched in the market. Online prerelease buzz data can be conveniently tracked and recorded, and we show that, if used smartly, such data can help accurately predict new product sales, even well in advance of the product launch date.
Instead of looking at the total volume of prerelease buzz, we investigate how consumer buzz evolves over time in the entire period before product launch. Using a state-of-the-art statistical method called functional data analysis, we extract the pattern of the evolution path over time. We find that prerelease buzz evolution pattern significantly enhances the forecast accuracy of new product sales, compared to simple aggregated measures such as average or total volumes. Moreover, this method allows accurate “early forecast”, e.g., one month or two months prior to the product release date.
In addition to the consumer market, we find evidence that stock market investors also incorporate information from prerelease buzz dynamics when evaluating the value of the firm introducing the new product. We also identify some factors that influence prerelease buzz evolution trajectories, such as product attributes, advertising, and interfirm collaborations.
Author:
Guiyang Xiong
Associate Professor, Whitman School of Management, Syracuse University
https://whitman.syr.edu/directory/showInfo.aspx?nid=gxiong
To learn more, read:
Xiong, Guiyang and Sundar Bharadwaj (2014), “Prerelease Buzz Evolution Patterns and New Product Performance,” Marketing Science, 33 (3), 401-421