Purpose

The rapid development of e-commerce has brought not only great convenience to people but a great challenge to online stores. Phenomenon such as out of stock and slow sales has been common in recent years. These issues can be managed only when the occurrence of the sales volume is predicted in advance, and sufficient warnings can be executed in time. Thus, keeping in mind the importance of the sales prediction system, the purpose of this paper is to propose an effective sales prediction model and make digital marketing strategies with the machine learning model.

Design/methodology/approach

Based on the consumer purchasing behavior decision theory, we discuss the factors affecting product sales, including external factors, consumer perception, consumer potential purchase behavior and consumer traffic. Then we propose a sales prediction model, M-GNA-XGBOOST, using the time-series prediction that ensures the effective prediction of sales about each product in a short time on online stores based on the sales data in the previous term or month or year. The proposed M-GNA-XGBOOST model serves as an adaptive prediction model, for which the instant factors and the sales data of the previous period are the input, and the optimal computation is based on the proposed methodology. The adaptive prediction using the proposed model is developed based on the LSTM (Long Short-Term Memory), GAN (Generative Adversarial Networks) and XGBOOST (eXtreme Gradient Boosting). The model inherits the advantages among the algorithms with better accuracy and forecasts the sales of each product in the store with instant data characteristics for the first time.

Findings

The analysis using Jingdong dataset proves the effectiveness of the proposed prediction method. The effectiveness of the proposed method is enhanced and the accuracy that instant data as input is found to be better compared with the model that lagged data as input. The root means squared error and mean absolute error of the proposed model are found to be around 11.9 and 8.23. According to the sales prediction of each product, the resource can be arranged in advance, and the marketing strategy of product positioning, product display optimization, inventory management and product promotion is designed for online stores.

Originality/value

The paper proposes and implements a new model, M-GNA-XGBOOST, to predict sales of each product for online stores. Our work provides reference and enlightenment for the establishment of accurate sales-based digital marketing strategies for online stores.

中文翻译：

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M-GAN-XGBOOST模型用于在线商店中每个产品的销售预测和精准营销策略制定

目的

电子商务的快速发展不仅给人们带来了极大的便利，也给网上商店带来了巨大的挑战。近年来，缺货、滞销等现象屡见不鲜。只有提前预测到销售量的发生，并及时执行足够的警告，才能对这些问题进行管理。因此，牢记销售预测系统的重要性，本文的目的是提出一个有效的销售预测模型，并利用机器学习模型制定数字营销策略。

设计/方法/方法

基于消费者购买行为决策理论，我们讨论了影响产品销售的因素，包括外部因素、消费者感知、消费者潜在购买行为和消费者流量。然后我们提出了一个销售预测模型，M-GNA-XGBOOST，使用时间序列预测，确保根据上一学期或上一月或上一年的销售数据，确保在线商店在短时间内对每种产品的销售情况进行有效预测. 提出的 M-GNA-XGBOOST 模型作为自适应预测模型，其输入是即时因素和上一期的销售数据，并基于提出的方法进行优化计算。使用所提出模型的自适应预测是基于 LSTM（长短期记忆）开发的，GAN（生成对抗网络）和 XGBOOST（极限梯度提升）。该模型继承了算法之间的优势，具有更好的准确性，首次以即时数据的特征预测了店内每款产品的销售情况。

发现

使用京东数据集的分析证明了所提出的预测方法的有效性。与滞后数据作为输入的模型相比，该方法的有效性得到了增强，并且发现即时数据作为输入的准确性更好。发现所提出模型的均方根误差和平均绝对误差约为 11.9 和 8.23。根据每个产品的销售预测，提前安排资源，为网店设计产品定位、产品展示优化、库存管理和产品推广的营销策略。

原创性/价值

该论文提出并实施了一种新模型 M-GNA-XGBOOST，以预测在线商店每种产品的销售额。我们的工作为网店建立精准的基于销售的数字营销策略提供了参考和启示。