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.
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References
Kahn, K.B.: Solving the problems of new product forecasting. Bus. Horiz. 57(5), 607–615 (2014)
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)
Kahn, K.B.: An exploratory Investigation of new product forecasting practices. J. Prod. Innov. Manag. 19, 133–143 (2002)
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)
Rosenberg, L.: Artificial swarm intelligence vs human experts. In: 2016 International Joint Conference on Neural Networks (IJCNN). IEEE (2016)
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/10.1177/0008125619862256
Rosenberg, L., Willcox, G.: Artificial swarm intelligence. In: Intelligent Systems Conference (IntelliSys), London, UK (2019)
Rosenberg, L., Baltaxe, D., Pescetelli, N.: Crowds vs swarms, a comparison of intelligence. In: IEEE 2016 Swarm/Human Blended Intelligence (SHBI), Cleveland, OH (2016)
Baltaxe, D., Rosenberg, L., Pescetelli, N.: Amplifying prediction accuracy using human swarms. In: Collective Intelligence 2017, New York, NY (2017)
McHale, I.: Sports Analytics Machine (SAM) as reported by BBC. http://blogs.salford.ac.uk/business-school/sports-analytics-machine/
Rosenberg, L., et al.: Artificial swarm intelligence employed to amplify diagnostic accuracy in radiology. In: IEMCON 2018, Vancouver, CA (2018)
Bonabeau, E.: Decisions 2.0: the power of collective intelligence. MIT Sloan Manag. Rev. 50(2), 45 (2009)
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)
Surowiecki, J.: The wisdom of crowds. Anchor (2005)
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)
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)
Seeley, T.D., et al.: Stop signals provide cross inhibition in collective decision-making by honeybee swarms. Science 335(6064), 108–111 (2012)
Seeley, T.D.: Honeybee Democracy. Princeton University Press, Princeton (2010)
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)
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)
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)
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.
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Willcox, G., Rosenberg, L., Schumann, H. (2020). Group Sales Forecasting, Polls vs. Swarms. In: Arai, K., Bhatia, R., Kapoor, S. (eds) Proceedings of the Future Technologies Conference (FTC) 2019. FTC 2019. Advances in Intelligent Systems and Computing, vol 1069. Springer, Cham. https://doi.org/10.1007/978-3-030-32520-6_5
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DOI: https://doi.org/10.1007/978-3-030-32520-6_5
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