Advertisement

Group Sales Forecasting, Polls vs. Swarms

  • Gregg Willcox
  • Louis RosenbergEmail author
  • Hans Schumann
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1069)

Abstract

Sales forecasts are critical to businesses of all sizes, enabling teams to project revenue, prioritize marketing, plan distribution, and scale inventory levels. Research shows, however, that sales forecasts of new products are highly inaccurate due to scarcity of historical data and weakness in subjective judgements required to compensate for lack of data. The present study explores sales forecasting performed by human groups and compares the accuracy of group forecasts generated by traditional polling to those made using real-time Artificial Swarm Intelligence (ASI), a technique which has been shown to amplify the forecasting accuracy of human teams in a wide range of fields. In collaboration with a major fashion retailer and a major fashion publisher, three groups of fashion-conscious millennial women (each with 15 participants) were asked to predict the relative sales volumes of eight clothing products (sweaters) during the 2018 holiday season, first by ranking each sweater’s sales in an online poll, and then using an online software platform called Swarm to form an ASI system. The Swarm-based forecasts were significantly more accurate than polling such that the top four sweaters ranked using Swarm sold 23.7% more units than the top four sweaters as ranked by survey, (p = 0.0497). These results suggest that ASI swarms of small groups can be used to forecast sales with significantly higher accuracy than a traditional polling.

Keywords

Swarm intelligence Artificial intelligence Collective intelligence Sales forecasting Product forecasting Customer research Market research Customer intelligence Marketing Business insights 

Notes

Acknowledgments

Thanks to Bustle Digital Group for supporting this project by sourcing participants and coordinating with the retail partner. Also, thanks to Unanimous AI for the use of the swarm.ai platform for this ongoing work.

References

  1. 1.
    Kahn, K.B.: Solving the problems of new product forecasting. Bus. Horiz. 57(5), 607–615 (2014)CrossRefGoogle Scholar
  2. 2.
    Lynn, G.S., Schnaars, S.P., Skov, R.B.: A survey of new product forecasting practices in industrial high technology and how technology businesses. Ind. Mark. Manag. 28(6), 565–571 (1999)CrossRefGoogle Scholar
  3. 3.
    Kahn, K.B.: An exploratory Investigation of new product forecasting practices. J. Prod. Innov. Manag. 19, 133–143 (2002)CrossRefGoogle Scholar
  4. 4.
    Rosenberg, L.: Human Swarms, a real-time method for collective intelligence. In: Proceedings of the European Conference on Artificial Life 2015, pp. 658–659 (2015)Google Scholar
  5. 5.
    Rosenberg, L.: Artificial swarm intelligence vs human experts. In: 2016 International Joint Conference on Neural Networks (IJCNN). IEEE (2016)Google Scholar
  6. 6.
    Metcalf, L., Askay, D.A., Rosenberg, L.B.: Keeping humans in the loop: pooling knowledge through artificial swarm intelligence to improve business decision making. Calif. Manag. Rev. (2019).  https://doi.org/10.1177/0008125619862256CrossRefGoogle Scholar
  7. 7.
    Rosenberg, L., Willcox, G.: Artificial swarm intelligence. In: Intelligent Systems Conference (IntelliSys), London, UK (2019)Google Scholar
  8. 8.
    Rosenberg, L., Baltaxe, D., Pescetelli, N.: Crowds vs swarms, a comparison of intelligence. In: IEEE 2016 Swarm/Human Blended Intelligence (SHBI), Cleveland, OH (2016)Google Scholar
  9. 9.
    Baltaxe, D., Rosenberg, L., Pescetelli, N.: Amplifying prediction accuracy using human swarms. In: Collective Intelligence 2017, New York, NY (2017)Google Scholar
  10. 10.
    McHale, I.: Sports Analytics Machine (SAM) as reported by BBC. http://blogs.salford.ac.uk/business-school/sports-analytics-machine/
  11. 11.
    Rosenberg, L., et al.: Artificial swarm intelligence employed to amplify diagnostic accuracy in radiology. In: IEMCON 2018, Vancouver, CA (2018)Google Scholar
  12. 12.
    Bonabeau, E.: Decisions 2.0: the power of collective intelligence. MIT Sloan Manag. Rev. 50(2), 45 (2009)Google Scholar
  13. 13.
    Woolley, A.W., Chabris, C.F., Pentland, A., Hashmi, N., Malone, T.W.: Evidence for a collective intelligence factor in the performance of human groups. Science 330(6004), 686–688 (2010)CrossRefGoogle Scholar
  14. 14.
    Surowiecki, J.: The wisdom of crowds. Anchor (2005)Google Scholar
  15. 15.
    Seeley, T.D., Buhrman, S.C.: Nest-site selection in honey bees: how well do swarms implement the ‘best-of-N’ decision rule? Behav. Ecol. Sociobiol. 49, 416–427 (2001)CrossRefGoogle Scholar
  16. 16.
    Marshall, J., Bogacz, R., Dornhaus, A., Planqué, R., Kovacs, T., Franks, N.: On optimal decision-making in brains and social insect colonies. Soc. Interface (2009) Google Scholar
  17. 17.
    Seeley, T.D., et al.: Stop signals provide cross inhibition in collective decision-making by honeybee swarms. Science 335(6064), 108–111 (2012)CrossRefGoogle Scholar
  18. 18.
    Seeley, T.D.: Honeybee Democracy. Princeton University Press, Princeton (2010)Google Scholar
  19. 19.
    Seeley, T.D., Visscher, P.K.: Choosing a home: How the scouts in a honey bee swarm perceive the completion of their group decision making. Behav. Ecol. Sociobiol. 54(5), 511–520 (2003)CrossRefGoogle Scholar
  20. 20.
    Rosenberg, L., Willcox, G.: Artificial swarms find social optima. In: 2018 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA 2018), Boston, MA (2018)Google Scholar
  21. 21.
    Willcox, G., Rosenberg, L., Donovan, R., Schumann, H.: Dense neural network used to amplify the forecasting accuracy of real-time human swarms. In: IEEE International Conference on Computational Intelligence and Communication Networks (CICN), (2019)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Unanimous AISan Luis ObispoUSA

Personalised recommendations