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

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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“…Prophet is robust to outliers and shifts in the trend and it can handle various seasons of historical time series data. Relative to other approaches, studies have recommended Prophet due to its open source algorithm, automated nature, accuracy, efficient and speedy time series analysis and forecasting 28–30. The forecasting model is an additive model where nonlinear trends are modelled with yearly, monthly, weekly or daily seasonality including holiday effects.…”

“…The MAPE is an easy-to-interpret scale-independent measure that compares prediction performance between various scaled datasets 28 31 32. A forecast’s MAPE value ≤10% is interpreted as highly accurate, 11%–20% good, 21%–50% reasonable and >50% inaccurate forecasting 33…”

“…Relative to other approaches, studies have recommended Prophet due to its open source algorithm, automated nature, accuracy, efficient and speedy time series analysis and forecasting. [28][29][30] The forecasting model is an additive model where nonlinear trends are modelled with yearly, monthly, weekly or daily seasonality including holiday effects. The Prophet model is described in equation ( 1 represents effects of holidays and events (models often non-periodic shocks; this component was not considered in this study as the data do not have an effect of the holiday term in trend forecasts).…”