Predicting Customer Behavior Using Prophet Algorithm In A Real Time Series Dataset

  • Ledion Liço MS in Data Science, University of Rochester, New York
  • Indrit Enesi Department of Electronics and Telecommunication, Polytechnic University of Tirana, Albania
  • Harshita Jaiswal MS in Data Science, University of Rochester, New York
Keywords: CRM, Time Series Analysis, Prophet Algorithm, Holiday Data, Accuracy In Prediction, Cross-Validation

Abstract

Customer Relationship Management is important in analyzing business performance. Predicting customer buying behavior enables the business to better address their customers and enhance service level and overall profit. This paper focuses on proposing a model that predicts future period sales in a real retail department store with low prediction error rate, and it also discovers the main sales trends over time. A model based on the Prophet algorithm is implemented and modified according to different parameters in order to lower the prediction error. The modifications consisted of the insertion of a new seasonality pattern, changes in the Fourier order of the existing and the new seasonality pattern, inclusion of the holiday data, and parameterizing its impact. The performance of the standard and modified model is evaluated in terms of the MAE (mean absolute error) and MAPE (mean absolute percentage error). The standard and the modified model were tested on a real dataset consisting of the sales between 2011-2019 in a department store of a shopping center in Albania. Implementation results show that the MAE in sales prediction for the modified model is reduced, while the MAPE in sales prediction for the modified model was measured for prediction periods. The implementation results indicate a comparable or evenbetter performance than the standard model. different

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Published
2021-07-31
How to Cite
Liço, L., Enesi, I., & Jaiswal, H. (2021). Predicting Customer Behavior Using Prophet Algorithm In A Real Time Series Dataset. European Scientific Journal, ESJ, 17(25), 10. https://doi.org/10.19044/esj.2021.v17n25p10
Section
ESJ Natural/Life/Medical Sciences