Social forecasting

from Wikipedia, the free encyclopedia

Social forecasting is a crowdsourcing approach, the aim of which is to aggregate the distributed knowledge of employees and experts and to convert it into quantifiable business figures, which are then available to company management or management. It is a business management method to use the collective knowledge of a group to make statements about the outcome of future events. Social forecasting is a combination of crowdsourcing, gamification elements and a virtual forecast exchange. Companies use social forecasting to receive forecasts, analyzes and other key figures from their employees for future-oriented questions.

introduction

Social forecasting uses the principle of swarm intelligence and uses the crowdsourcing concept for forecasts. A group is asked about the occurrence of future events or the effect of decisions. Historically, social forecasting is the further development of forecast markets. This approach was first used in the USA in the 1980s at the Iowa Election Markets (IEM) - you could bet online with a symbolic bet of a few cents on the outcome of election results. The results of the IEM were more precise than the forecasts of the market researchers. Social forecasting as a Web 2.0 tool is a term coined by the German entrepreneur Aleksandar Ivanov. In India, the term is used as a term for predicting social development.

method

Social forecasting is similar in its meaning and approach to an online-based crowdsourcing survey. With social forecasting, however, specific questions are asked about the outcome of future events, e.g. B. the sales potential of new products or the future market shares in an existing market. The distinction to classic crowdsourcing lies in the special selection of participants: Social forecasting does not require any representative participants (“representative crowd”) from the network, but “knowing participants” (“wise crowd”). Furthermore, in contrast to conventional crowdsourcing, social forecasting primarily uses employees for the survey. As a result, their consumer perspective as well as their company-specific knowledge as well as their professional market and technical expertise are taken into account and integrated in a knowledge pool. This commitment also takes into account that no business-relevant knowledge or sensitive data is made public.

Another important element of social forecasting is the setting of appropriate incentives and the use of gamification elements. Because with surveys only participation is rewarded, with social forecasting, however, the accuracy of the answer. In this way, the participants should be encouraged to make forecasts that are as true and accurate as possible. For this purpose, specific questions about the development of future events are asked via a virtual forecast exchange. If the respective participant makes a very good forecast, his profit will be correspondingly higher. In addition, a ranking of the best participants can be published in order to create greater motivation to give good assessments.

A final major difference to other methods is the way the results are updated. Often assumptions and framework conditions change so often that a survey has to be repeated. Social forecasting works differently because the incentive system ensures that when new information becomes known, the participants themselves have an incentive to update their opinions immediately in order to increase their chances of winning.

Benefits for companies

Social forecasting is used by companies to support business decisions. With social forecasting, the relevant knowledge is not converted into wikis and blogs, i.e. in text form, but into numbers and thus directly tangible. Management can use the crowdsourcing approach to use forecasts, analyzes and other information from employees.

Areas of application

Social forecasting can be used in a variety of areas:

  • Market forecast , e.g. B. Forecast of sales figures, market shares, growth rates for any product
  • Competitive Intelligence , e.g. B. Risk of new competitors entering the market in segment X
  • Project management , e.g. B. truthful early warning indicators and precise estimates of actual completion dates, milestones, etc. Ä.
  • Product innovation , e.g. B. Life-time sales potential, actual development costs and duration for new product ideas; Assessment of the potential of new product ideas
  • R&D management, e.g. B. quantify technology trends, assess development risks in good time;
  • Risk management , e.g. B. Estimation of credit default risks; Forecast of the mean credit risk and credit volume; more efficient equity planning for Basel II reserves
  • Economic forecasts , e.g. B. unemployment rate , economic growth
  • Sales planning : number of new customers; Number of products sold

Practical examples

Market forecast

Market forecasts, for example the forecast of sales figures, market shares, growth rates for any product. The consumer goods manufacturer Henkel uses social decision support and was able to increase its forecast accuracy by 22 percent.

Competitive Intelligence

Competitive intelligence, for example, assess the risk of new competitors entering the market in segment X. Zeppelin Rental uses Social Decision Support to bundle the knowledge of employees from over 100 locations for strategic questions.

Product innovation

Product innovation, for example for forecasting flop rates, actual development costs and duration for new product ideas. With Social Decision Support, Tchibo used the knowledge of its branch employees to assess new products.

R&D management

R&D management, for example the quantification of technology trends, in order to assess development risks in good time. Deutsche Telekom pools the knowledge of 240,000 employees in order to better assess the potential of new technologies.

Economic forecasts

Economic forecasts, for example unemployment rate, economic growth. The seed manufacturer Syngenta uses Social Decision Support to adapt its production to expected global demand at an early stage.

Similar concepts

Forerunners of social forecasting are so-called forecast markets . This is a virtual marketplace on which future events with an unknown outcome are traded, for example the question of the sale of product A in a certain period of time. On this trading platform, participants submit their own individual prognosis by betting a number of points on a specific outcome of the question, similar to a stock exchange. These forecasts are often more accurate than the opinions of experts.

literature

  • Franziska Beckmann: Forecast exchanges as an instrument of knowledge management in companies , 2010.
  • Bughin and Chui: The rise of the networked enterprise: Web 2.0 finds its payday ; McKinsey Quarterly, December, 2010
  • Chresbrough: Open Innovation: The New Imperative for Creating and Profiting from Technology. Harvard Business School Press, Boston, 2003.
  • Reneé Dye: The promise of prediction markets: A roundtable , in: The McKinsey Quartely, Issue 2, 2008.
  • Ericsson, AK and Jacqui, S .: Toward a General Theory of Expertise: Prospects and Limits ; Cambridge University Press, Cambridge, 1991
  • Ericsson et al: The Cambridge Handbook of Expertise and Expert Performance , 2006.
  • Larissa Hammon: Crowdsourcing - An Analysis of the Driving Forces within the Crowd , Verlag Dr. Kovac, Hamburg 2013.
  • Larissa Hammon, Hajo Hippner: Crowdsourcing . In: Wirtschaftsinformatik, Vol. 54 (2012), No. 3, pp. 165–168.
  • Larissa Hammon, Stefan Hampel, Hajo Hippner: Crowdsourcing . In: WISU, No. 5, 2010, pp. 698-704.
  • Holger Herkle: Knowledge in Organizations, Considerations on the Concept of Systematic Knowledge Management , 2007.
  • Prabhakar Krishnamurthy: Social Forecasting - Relevance in strategic planning for corporate sector , in: Microeconomics: Intertemporal Choice and growth eJournal , Vol. 2 No. 86, 2010 http://www.academia.edu/251671/SOCIAL_FORECASTING_-_RELEVANCE_IN_STRATEGIC_PLANNING_FOR_CORPORATE_SECTOR .
  • Prabhakar Krishnamurthy: Methodological Premises of Social Forecasting in the Context of Business organizations , 2nd National Conference on Management Science and Practice Indian Institute of Technology, Madras, March 9–11, 2007 http://www.academia.edu/251667/Methodological_Premises_of_Social_Forecasting_in_the_Context_of_Business_organizations .
  • Prabhakar Krishnamurthy: Social Forecasting - Tool for Corporate Planning and Application to Information Technology Industry , in: econometrics: applied econometrics and modeling eJournal , Vol. 4 No. 99, 2011 http://www.academia.edu/468038/Social_Forecasting_-Tool_for_Corporate_Planning_and_Application_to_Information_Technology_Industry .
  • Franz Lehner: Knowledge Management, Basics, Methods and Technical Support , 2008.
  • Ralf Ike: Performance Management, Synergy Potential of Knowledge Management and Business Intelligence as part of a holistic approach to strategic corporate management , 2008.
  • Rietz et al: Accuracy and Forecast Standard Error of Prediction Markets , University of Iowa, Working Paper.
  • Brain Twiss: Social Forecasting for Company Planning , 1982.

Web links

Individual evidence

  1. ^ Franziska Beckmann: Forecast exchanges as an instrument of knowledge management in companies , 2010, p. 27
  2. ^ Stanley W. Angrist, Iowa Market Takes Stock of Presidential Candidates (Reprinted with Permission of The Wall Street Journal), The University of Iowa, Henry B. Tippie College of Business, 2012
  3. ↑ a b Aleksandar Ivanov: Social Forecasting , in: Web2.0 and Social Media in Corporate Practice (Ed .: Andrea Back et al.), 2012
  4. ^ Social Forecasting - Relevance in strategic planning for the corporate sector . Prabhakar Krishnamurthy. March 3, 2013. Accessed in 2010.
  5. ↑ Jörg Hackhausen: Forecast exchanges predict the future , Handelsblatt, 2006
  6. ↑ Science shows: the wisdom of the many works . crowdworx.com. August 4, 2012. Retrieved February 13, 2013.
  7. ↑ We now have to go completely new methodical ways . iwkoeln.de. 03.03.2013, 13:29. Archived from the original on February 2, 2014. Info: The archive link was inserted automatically and has not yet been checked. Please check the original and archive link according to the instructions and then remove this notice. Retrieved March 11, 2009. @1@ 2Template: Webachiv / IABot / www.iwkoeln.de
  8. ↑ A way to management 2.0: forecast exchanges in companies . itespresso.de. August 4, 2012. Retrieved February 13, 2013.
  9. ↑ Marketplace for specific questions . cfoworld.de. August 4, 2012. Retrieved February 13, 2013.
  10. ↑ Converting knowledge into numbers: social forecasting in companies . Claudia Thurner-Scheuerer. August 4, 2012. Retrieved February 13, 2013.
  11. ↑ Modern corporate management Social Decision Support - the way to Management 2.0 . perspective-mittelstand.de. August 4, 2012. Retrieved February 13, 2013.
  12. ↑ Sales planning in retail: high-speed with low-cost . Aleksandar Ivanov. August 4, 2008. Retrieved February 13, 2013.
  13. ↑ Employee engagement re-invented: . Aleksandar Ivanov. August 4, 2008. Retrieved February 13, 2013.
  14. ↑ Sales Planning in Retail: High speed at low cost . Aleksandar Ivanov. August 4, 2008. Retrieved February 13, 2013.
  15. ^ Strategy and Risk Management: Fast employee feedback for the CEO . Aleksandar Ivanov. August 4, 2008. Retrieved February 13, 2013.
  16. ^ Franziska Beckmann: Forecast exchanges as an instrument of knowledge management in companies , 2010, p. 45ff.
  17. ↑ Market forecasts: Strategic insights - in real time! . Aleksandar Ivanov. August 4, 2008. Retrieved February 13, 2013.
  18. ↑ CrowdWorx: Platform bundles employee knowledge . crowdworx.com. 03.03.2013, 13:29. Archived from the original on March 5, 2013. Info: The archive link was inserted automatically and has not yet been checked. Please check the original and archive link according to the instructions and then remove this notice. Retrieved March 6, 2013. @1@ 2Template: Webachiv / IABot / business.chip.de