IMPROVING MARKETING FORECASTING THROUGH COLLECTIVE MARKET INTELLIGENCE
AuthorLang, Mark Frederick
Committee memberDi Benedetto, C. Anthony
Stanton, John L.
New Product Development
Permanent link to this recordhttp://hdl.handle.net/20.500.12613/1688
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AbstractNew product development and management are critical to the long-term success of the firm. New product development is also an area where the firm needs to improve performance. Two important new product decisions are selecting new concepts and estimating their future market potential and demand. Forecasting is a critical activity in supporting these two decisions. Unfortunately, forecasting is an activity where firms often struggle to be proficient. Recent advances in forecasting methods offer opportunities for improvement. One of the techniques is prediction markets, an emerging methodology that taps collective intelligence. Despite widely reported application and promise of prediction markets, they have yet to be adopted in marketing practice or examined in marketing academia. This dissertation addresses two research questions: do prediction markets produce better marketing forecasts than methods traditionally employed by firms, and if they do, how do they do it? To answer these research questions, two field studies are completed. The first is an empirical test of prediction markets compared to traditional forecasting methods implemented within a Fortune 100 firm. The second, based on a post survey, is an analysis of how market knowledge factors in combination with prediction markets design factors produce superior results. Study I finds that prediction markets do provide superior results in 67% of the forecasts and reduce error levels and ranges. Study II finds that out of several design factors, prediction market forecast accuracy is driven most by new information acquisition and knowledge heterogeneity. These findings contribute to MSI 2012-2014 Research Priorities and calls in the marketing literature to develop, better, real-time, intelligent decision support tools to help solve problems of the big data era and support improved demand forecasting.
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