Predicting new product success with prediction markets in online communities

By: Kurt Matzler, Christopher Grabher, Jürgen Huber, and Johann Füller

Source: http://eds.b.ebscohost.com/eds/pdfviewer/pdfviewer?sid=fc8cd7f5-3da1-460d-802f-8d40d58bd464%40sessionmgr111&vid=3&hid=103

Summary:

The introduction of this article discusses the history of predicting new product success and compares the pros and cons of the traditionally-used methods to the use of prediction markets. It first describes how there are many methods that are extremely error prone and how it is believed that prediction markets could be a very viable alternative to those methods. This is due to the many cons of the traditional ways as well at the many pros of prediction markets. Some of the many issues with the current methods are that experts are difficult to identify, there are declining survey response rates, consumer resentment is growing, and cost associated with traditional market research are increasing. The pros to using prediction markets include the fact that they can be effective with small and non-representative pools of participants, can efficiently aggregate asymmetrically dispersed information, and have other benefits such as speed, adaptive interactivity, and task engagement.

According to this article, decades of research and studies have proven that the accuracy of prediction markets tends to be much higher than that of other research tools such as polls, questionnaires, and censuses. In addition to high accuracy, this method has a very wide range of applications and has been used to predicts everything from election outcomes in 1988, outcomes of sports competitions, statistical weather forecasts, and many different business events. This article also states that under the right circumstances, groups are often very intelligent (a.k.a. the concept of "wisdom of the crowd") which would support the statement that prediction markets have a high degree of accuracy.

This article also describes an experiment in which prediction markets were tested for accuracy when trying to predict the sale of new skis. In order to do this, 62 participants  used PIM Sports (which was set up like a Facebook application) to predict the sales of skis before the main 2010/2011 skiing season, and then the results were compared to the actual sales. Each person was given $40,000 ($10,000/market, four markets created in total) in virtual money to use in order buy and sell individual assets. The four markets in this scenario were race skis, technology products, the powder segment, and women's skis. In total, the application was open for 12 days. After the 12 days were over, this data was compared to the actual sales and ended up showing that prediction markets are mostly accurate. In this instance, race skis, technology products, and the powder segment had pretty high levels of accuracy while the accuracy for women's skis was fairly low in comparison. The article explained that this was probably due the the fact that liquidity (trade volume) is a key factor in the accuracy of prediction markets, and in this case there was much less liquidity in the women's skis market. Overall, I found this article to be a very interesting read.

Critique:

The main issue that I found with this study was that the PIM Sports application had a total 1,345 users, yet the experiment was only based on the 62 active users. While there was technically a great amount of diversity in total (people from over 50 countries visited the sight), that diversity could have been lacking in the sample of people that were actually used. Basically, even though the results matched those of many other studies, it is hard to say that they were accurate because there was a small sample of people who were used for the results, and because diversity (which is very important for this method) could have been lacking because of the low active participation. Also, like any other experiment there is always room for error so that could always have a possible impact on the accuracy of the results.