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Analysis and Forecasting of Sales Funnels

Author

Listed:
  • Egor Griva

    (Department of Data Processing Automation, Tomsk State University of Control Systems and Radioelectronics, 634050 Tomsk, Russia)

  • Irina Butorina

    (Department of Data Processing Automation, Tomsk State University of Control Systems and Radioelectronics, 634050 Tomsk, Russia)

  • Anatoly Sidorov

    (Department of Data Processing Automation, Tomsk State University of Control Systems and Radioelectronics, 634050 Tomsk, Russia)

  • Pavel Senchenko

    (Department of Data Processing Automation, Tomsk State University of Control Systems and Radioelectronics, 634050 Tomsk, Russia)

Abstract

This article discusses the use of analysis and forecasting methods for sales funnels to help further decision-making. A number of objective and subjective factors preventing the wide use of various sales funnel forecasting methods are described. It has been substantiated that due to a large number of external and internal factors, perfect forecasting results cannot be obtained. It has been proved that the most accurate and suitable methods for the forecasting of sales funnels are the methods included in the group of time series forecasting methods. Recommendations have been developed to improve some of the methods that significantly increase the accuracy of the forecasted data. Using the data received from different organizations, it was possible to empirically verify the accuracy of the forecast values. The obtained results of analysis and forecasting were used for testing the methods of searching the optimal scenarios of achieving the forecast indicators.

Suggested Citation

  • Egor Griva & Irina Butorina & Anatoly Sidorov & Pavel Senchenko, 2022. "Analysis and Forecasting of Sales Funnels," Mathematics, MDPI, vol. 11(1), pages 1-22, December.
  • Handle: RePEc:gam:jmathe:v:11:y:2022:i:1:p:105-:d:1015572
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    References listed on IDEAS

    as
    1. Philip Hans Franses & Michael McAleer & Rianne Legerstee, 2009. "Expert opinion versus expertise in forecasting," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 63(3), pages 334-346, August.
    2. Hueng, C. James & McDonald, James B., 2005. "Forecasting asymmetries in aggregate stock market returns: Evidence from conditional skewness," Journal of Empirical Finance, Elsevier, vol. 12(5), pages 666-685, December.
    3. Vilma Todri & Anindya Ghose & Param Vir Singh, 2020. "Trade-Offs in Online Advertising: Advertising Effectiveness and Annoyance Dynamics Across the Purchase Funnel," Information Systems Research, INFORMS, vol. 31(1), pages 102-125, March.
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