The product demand model driven by consumer’s information perception and quality perception

Yuan, Guanghui; Han, Jingti; Wang, Yaqiong; Liang, Hejun; Li, GangYuan

doi:10.1016/j.physa.2019.122352

Cited by 9 publications

(7 citation statements)

References 10 publications

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“…Consumer perception refers to the relevant information obtained by consumers from various approaches according to their demand (Yuan et al, 2019), which measures include the number of reviews, positive ratings and merchant reputation of each product. When consumers refer to such information, the time frame is all moments prior to the current time point.…”

Section: The Selection Of Input Variablementioning

confidence: 99%

M-GAN-XGBOOST model for sales prediction and precision marketing strategy making of each product in online stores

Wang

Yang

2021

DTA

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PurposeThe 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/approachBased 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.FindingsThe 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/valueThe 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.

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Section: The Selection Of Input Variablementioning

confidence: 99%

M-GAN-XGBOOST model for sales prediction and precision marketing strategy making of each product in online stores

Wang

Yang

2021

DTA

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“…They expect companies to protect the environment, prevent exploitation, and treat business partners and employees fairly (Chang, Chiang, Liu, & Xie, 2019). Beyond consumer demand, cost saving is one of the key reasons why businesses are adopting sustainability programs (Yuan, Han, Wang, Liang, & Li, 2019;Park, Kim, & Ryu, 2019).…”

Section: Insight Into Sustainabilitymentioning

confidence: 99%

Indonesian Undergraduate Students’ Perspectives of Sustainable Economic Education: A Survey Study

Wahjoedi¹,

Priambodo²,

Wijayanti³

et al. 2020

PED

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This study was designed to uncover perceptions of 230 undergraduate students at a public university in Indonesia, concerning sustainable economic education issues in higher education settings. The results of this study suggested that the respondents held positive beliefs on the issue of sustainable economic education issues. Despite this, they voiced multifaceted perspectives on the refinement of economic teaching practices. This paper ends with suggestions for the stakeholders to improve the quality of classroom teachings which corresponds to the age of industry 4.0.

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“…When experiencing a specific product, a consumer may perceive valuable experience attributes according to her/his own experiences and feelings (Galloa et al , 2019). Valuable experience attributes perceived by consumers may differ for the same product because experiences and feelings vary among people (Yuan et al , 2019). Therefore, for a specific EPSLC, it may be difficult to mine an attribute set reflecting the common needs of all consumers.…”

Section: Introductionmentioning

confidence: 99%

Predicting total sales volume interval of an experiential product with short life cycle before production: similarity comparison in attribute relationship patterns

Tang

Wang

Cui³

et al. 2021

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PurposeBecause of short life cycle and fluctuating greatly in total sales volumes (TSV), it is difficult to accumulate enough sales data and mine an attribute set reflecting the common needs of all consumers for a kind of experiential product with short life cycle (EPSLC). Methods for predicting TSV of long-life-cycle products may not be suitable for EPSLC. Furthermore, point prediction cannot obtain satisfactory prediction results because information available before production is inadequate. Thus, this paper aims at proposing and verifying a novel interval prediction method (IPM).Design/methodology/approachBecause interval prediction may satisfy requirements of preproduction investment decision-making, interval prediction was adopted, and then the prediction difficult was converted into a classification problem. The classification was designed by comparing similarities in attribute relationship patterns between a new EPSLC and existing product groups. The product introduction may be written or obtained before production and thus was designed as primary source information. IPM was verified by using data of crime movies released in China from 2013 to 2017.FindingsThe IPM is valid, which uses product introduction as input, classifies existing products into three groups with different TSV intervals, mines attribute relationship patterns using content and association analyses and compares similarities in attribute relationship patterns – to predict TSV interval of a new EPSLC before production.Originality/valueDifferent from other studies, the IPM uses product introduction to mine attribute relationship patterns and compares similarities in attribute relationship patterns to predict the interval values. It has a strong applicability in data content and structure and may realize rolling prediction.

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The product demand model driven by consumer’s information perception and quality perception

Cited by 9 publications

References 10 publications

M-GAN-XGBOOST model for sales prediction and precision marketing strategy making of each product in online stores

M-GAN-XGBOOST model for sales prediction and precision marketing strategy making of each product in online stores

Indonesian Undergraduate Students’ Perspectives of Sustainable Economic Education: A Survey Study

Predicting total sales volume interval of an experiential product with short life cycle before production: similarity comparison in attribute relationship patterns

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