Any thoughts and how this relates to marketing management

Principles Of Marketing
17th Edition
ISBN:9780134492513
Author:Kotler, Philip, Armstrong, Gary (gary M.)
Publisher:Kotler, Philip, Armstrong, Gary (gary M.)
Chapter1: Marketing: Creating Customer Value And Engagement
Section: Chapter Questions
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Any thoughts and how this relates to marketing management 

During this period, the market was correct in 11 out of 14 times. When
the market missed a forecast, the error was never more than one level of sever-
ity away. This success enables health officials to better contain an influenza out-
break.
Summary
The above examples show that prediction markets can work in a wide
range of industries even in cases where forecasting is challenging. These markets
work because they could effectively aggregate information from a diverse group
of individuals.
Potential Pitfalls
While the prediction market can be a powerful tool, it is not a panacea for
all forecasting situations. There are many common pitfalls.
Prediction markets only aggregate information well if the number of par-
ticipants is large. A small pool of participants can limit the amount of liquidity in
the market and hence the market's ability to aggregate information. This runs
counter to the principle of having a large crowd, as noted above. The success of a
prediction market relies on active participation. If participants are not
sufficiently motivated, they will have a low level of trading activities. Conse-
quently, prices of the markets will not capture information well. This violates the
principle of incentives. In combination, these problems lead to low market liq-
uidity.
A recent application at a computer manufacturer is an illustration of this
liquidity problem. This particular manufacturer experimented using a prediction
market to forecast sales of computing products. This particular prediction market
size had a small pool of participants (about 15). Most participants were execu-
tives from marketing and finance organizations. Since the size of the stakes was
small ($50 per person on average) and these executives were busy, they did not
pay enough attention to the prediction market. As a result, the market had low
liquidity. Since information aggregation stems from trading activities, this low
level of liquidity stopped active participants from executing trades that they
wanted and this further exacerbated the problem. Despite these problems, the
results were somewhat encouraging. The generated forecasts slightly beat the i
company's official forecasts in six out of eight times. However, the improve-
ments were not substantial enough to justify the cost and time spent by the
participants. As a consequence, this prediction market failed to be adopted as
an ongoing forecasting tool.
The principles of crowd and independence imply that people who have
relevant information should be included in the market. Otherwise, the aggregate
information may be coarse and consequently the forecast may not be accurate.
Put differently, the prediction market is a system of "garbage in, garbage out."
In 2003, after the U.S. forces took Bagdad, Saddam Hussein was on the run and
managed to avoid capture for several months. During that time, www.trades-
156
UNIVERSITY OF CALIFORNIA, BERKELEY VOL 50. NOI FALL 2007
ports.com conducted a prediction market on whether Saddam Hussein would be
captured before a certain deadline. The price of the scenario, saying Saddam
would be captured before the deadline, was hovering around 9 cents over the
dollar for a long time and only jumped up to 30 cents two days before his actual
capture. This rapid adjustment is typical when new information is assimilated
into the market. However, even at 30 cents, the market was predicting a strong
chance (70%) that Saddam would not have been captured before the deadline.
Obviously, the market was inaccurate, predicting only a 9% probability of cap-
ture until two days before his capture. This inability of the market to forecast
accurately underscores the importance of the existence of "wisdom" in the
crowd. Therefore, the importance of recruiting individuals who have informa-
tion cannot be overemphasized. When the size of the group is limited by cir-
cumstances to be small, one can use related methods that are developed for
this specific purpose."
Prediction markets can only work well if players do not use other incen-
tives to trade. That is, players trade solely to make money in the market. The
market can fail if players have other incentives. For example, consider a predic-
tion market for product demand. A product manager, who will gain a higher
level of resources if the generated demand forecast is high, may want to mislead
the market by pushing up prices of shares of high demand scenarios. Similarly, a
Republican might want to depress prices of Democratic Party nominee in a polit-
ical market in order to influence the ultimate political outcome (e.g.. affect voter
turnout). That is the reason why it is important to limit the stake of individuals.
in the market. The IEM limits the stake to $500 for the same reason. This size
limit of the stakes also minimizes incentives for the participants to manipulate
ultimate outcome. For example, a sales representative, who has bought shares
of low demand scenarios, would not have deliberately reduced his own sales
effort to make the prediction come true.
Conclusions
Prediction markets are "smart" markets that are capable of accurately.
predicting outcomes of uncertain future events. They have been successfully
used in a wide range of settings and industries. Prediction markets work well if
the underlying scientific principles (incentive, indicator, improvement, indepen-
dence, and crowd) are adhered to. When a prediction market fails to predict
future outcomes, it is often the case that one of the underlying principles does
not hold.
As long as prediction markets are active, they always contain the most
current "wisdom of crowds." Hence, they are better than a one-time survey in
aggregating information from individuals because these same individuals have
strong incentives to learn from each other through the price discovery process.
Thus, the participants become smarter over time. In effect, a well-functioned
market will eventually contain a large group of experts who freely share knowl-
edge through their trading behaviors.
CALIFORNIA MANAGEMENT REVIEW VOL 50. NOI FALL 2007
157
Transcribed Image Text:During this period, the market was correct in 11 out of 14 times. When the market missed a forecast, the error was never more than one level of sever- ity away. This success enables health officials to better contain an influenza out- break. Summary The above examples show that prediction markets can work in a wide range of industries even in cases where forecasting is challenging. These markets work because they could effectively aggregate information from a diverse group of individuals. Potential Pitfalls While the prediction market can be a powerful tool, it is not a panacea for all forecasting situations. There are many common pitfalls. Prediction markets only aggregate information well if the number of par- ticipants is large. A small pool of participants can limit the amount of liquidity in the market and hence the market's ability to aggregate information. This runs counter to the principle of having a large crowd, as noted above. The success of a prediction market relies on active participation. If participants are not sufficiently motivated, they will have a low level of trading activities. Conse- quently, prices of the markets will not capture information well. This violates the principle of incentives. In combination, these problems lead to low market liq- uidity. A recent application at a computer manufacturer is an illustration of this liquidity problem. This particular manufacturer experimented using a prediction market to forecast sales of computing products. This particular prediction market size had a small pool of participants (about 15). Most participants were execu- tives from marketing and finance organizations. Since the size of the stakes was small ($50 per person on average) and these executives were busy, they did not pay enough attention to the prediction market. As a result, the market had low liquidity. Since information aggregation stems from trading activities, this low level of liquidity stopped active participants from executing trades that they wanted and this further exacerbated the problem. Despite these problems, the results were somewhat encouraging. The generated forecasts slightly beat the i company's official forecasts in six out of eight times. However, the improve- ments were not substantial enough to justify the cost and time spent by the participants. As a consequence, this prediction market failed to be adopted as an ongoing forecasting tool. The principles of crowd and independence imply that people who have relevant information should be included in the market. Otherwise, the aggregate information may be coarse and consequently the forecast may not be accurate. Put differently, the prediction market is a system of "garbage in, garbage out." In 2003, after the U.S. forces took Bagdad, Saddam Hussein was on the run and managed to avoid capture for several months. During that time, www.trades- 156 UNIVERSITY OF CALIFORNIA, BERKELEY VOL 50. NOI FALL 2007 ports.com conducted a prediction market on whether Saddam Hussein would be captured before a certain deadline. The price of the scenario, saying Saddam would be captured before the deadline, was hovering around 9 cents over the dollar for a long time and only jumped up to 30 cents two days before his actual capture. This rapid adjustment is typical when new information is assimilated into the market. However, even at 30 cents, the market was predicting a strong chance (70%) that Saddam would not have been captured before the deadline. Obviously, the market was inaccurate, predicting only a 9% probability of cap- ture until two days before his capture. This inability of the market to forecast accurately underscores the importance of the existence of "wisdom" in the crowd. Therefore, the importance of recruiting individuals who have informa- tion cannot be overemphasized. When the size of the group is limited by cir- cumstances to be small, one can use related methods that are developed for this specific purpose." Prediction markets can only work well if players do not use other incen- tives to trade. That is, players trade solely to make money in the market. The market can fail if players have other incentives. For example, consider a predic- tion market for product demand. A product manager, who will gain a higher level of resources if the generated demand forecast is high, may want to mislead the market by pushing up prices of shares of high demand scenarios. Similarly, a Republican might want to depress prices of Democratic Party nominee in a polit- ical market in order to influence the ultimate political outcome (e.g.. affect voter turnout). That is the reason why it is important to limit the stake of individuals. in the market. The IEM limits the stake to $500 for the same reason. This size limit of the stakes also minimizes incentives for the participants to manipulate ultimate outcome. For example, a sales representative, who has bought shares of low demand scenarios, would not have deliberately reduced his own sales effort to make the prediction come true. Conclusions Prediction markets are "smart" markets that are capable of accurately. predicting outcomes of uncertain future events. They have been successfully used in a wide range of settings and industries. Prediction markets work well if the underlying scientific principles (incentive, indicator, improvement, indepen- dence, and crowd) are adhered to. When a prediction market fails to predict future outcomes, it is often the case that one of the underlying principles does not hold. As long as prediction markets are active, they always contain the most current "wisdom of crowds." Hence, they are better than a one-time survey in aggregating information from individuals because these same individuals have strong incentives to learn from each other through the price discovery process. Thus, the participants become smarter over time. In effect, a well-functioned market will eventually contain a large group of experts who freely share knowl- edge through their trading behaviors. CALIFORNIA MANAGEMENT REVIEW VOL 50. NOI FALL 2007 157
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