Using the Mathematical Model on Precision Marketing with Online Transaction Data Computing

Wu, Jun; Shi, Li; Xu, Guangshu; Yuan, Yu‐Hsi; Tsai, Sang‐Bing; Hao, Weiyi; Jiang, Jiesong

doi:10.1155/2021/5539300

Cited by 4 publications

(5 citation statements)

References 19 publications

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“…With the rapid development of Internet technology and the continuous penetration of mobile Internet technology, major e-commerce platforms and enterprises have begun to analyze the basic information of customers, from which useful information can be mined [8]. Accurate screening of customer needs, and finally, develop a targeted marketing program, which can generate revenue for the enterprise while reducing advertising costs [9][10].…”

Section: ) E-commerce Platform Precision Marketingmentioning

confidence: 99%

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Research on the Application of KNN Algorithm Incorporating Gaussian Functions in Precision Marketing Classification of E-commerce Platforms

Wang

2023

Applied Mathematics and Nonlinear Sciences

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The technology can fully explore the user’s consumption behavior habits and help the e-commerce platform formulate more precise marketing strategies in a targeted manner. This paper firstly analyzes the optimization of marketing strategy based on the 3R marketing theory, gives the design process of the precise marketing strategy of an e-commerce platform, and analyzes the personalized service based on consumer classification. Secondly, for the shortcomings of the KNN algorithm in the process of accurate classification, the Gaussian function is introduced to weight the optimization of the algorithm, which further realizes the construction of the G-KNN algorithm. Finally, the testing and application analysis of the algorithm model was carried out using the actual user consumption data of the e-commerce platform. The results show that the classification accuracy of the G-KNN algorithm has been maintained at about 95% when the K value exceeds 800, and the F1 composite value of this paper’s algorithm fluctuates around 56% when the K value exceeds 1000. On the e-commerce platform, except for the electrical appliances category classification test, the fit and accuracy of other categories basically match. Using the KNN algorithm incorporating the Gaussian function can effectively realize the accurate classification of user characteristics on the e-commerce platform and provide data support for the e-commerce platform to formulate accurate marketing strategies based on consumer preferences.

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Section: ) E-commerce Platform Precision Marketingmentioning

confidence: 99%

“…( 9) are eliminated to generate a new sequence of Euclidean distances and reference points, and the mean d  and median error d  are recalculated Eqs. ( 7)- (8).…”

Section: Knn Algorithm With Gaussian Function Weightingmentioning

confidence: 99%

Research on the Application of KNN Algorithm Incorporating Gaussian Functions in Precision Marketing Classification of E-commerce Platforms

Wang

2023

Applied Mathematics and Nonlinear Sciences

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“…Wu, J. et al's analytical study is mainly based on the operation data of a real e-commerce company, and in the data preprocessing stage, three classical recommendation algorithms are applied to analyze the consumer's purchasing behaviors and to compare the performances of the three algorithms. The data analysis of e-commerce is aimed at verifying the performance of the designed and proposed precision marketing decision-making system [7]. Zhang, L. believes that the era of big data is an opportunity and a challenge for enterprises, and, at the same time, it also affects the social economy and people's lives.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on the classification and application of precision marketing based on big data e-commerce platforms

Dong,

Li,

Kong

2023

Applied Mathematics and Nonlinear Sciences

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This paper proposes and improves the traditional K-means algorithm, utilizes the HC-Kmeans algorithm to deeply analyze the marketing status quo of the M e-commerce platform, and constructs the overall framework of precision marketing based on big data technology. The RFM model is used to measure customer value and segment customer behavior. Formulate marketing strategies that correspond to the consumption habits and preferences of different categories of users. The data from 40 consecutive days of observation is used to verify the precision marketing effect of the A/B testing method, and the final results show a good improvement in the click rate, order rate, payment rate, and order amount. During the Double Twelve Shopping Festival, compared with the traditional mode, the payment amount of M Company’s product recommendation increased by 64,604 yuan, and the order increased by 301 units, so the implementation of the precision marketing strategy was effective.

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“…Later, through their research, some experts defined the concept of precision marketing. Precision marketing, they explained, indicates that enterprises should fully reflect "right" and "appropriate" in the four aspects of channels, time, target customers, and information to meet the goal of encouraging target customer purchase [21]. Following that, as Internet technology advances, precision marketing is increasingly combined with information technology.…”

Section: Precision Marketingmentioning

confidence: 99%

Enterprise Precision Marketing Effectiveness Model Based on Data Mining Technology

Meng

2022

Mobile Information Systems

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Due to the explosive growth of massive consumption data, the traditional marketing model of enterprises has become stretched. The rapid advancement of data mining technology has given new impetus to the innovation of the marketing strategies of enterprises, promoting the progressive transformation of enterprises from traditional passive marketing to precise and refined marketing. Data mining technology’s basic task is to analyze data, acquire insight into cause and effect, and then predict the future. As a result, the technology is coupled with the enterprises’ marketing activities, and a precise marketing model is built using big data consumption. This will aid in the comprehensive and three-dimensional description of customers, as well as the analysis of potential customers’ attributes, to give a scientific basis for the formulation and implementation of precise data-driven decision-making for enterprises. As a result, our study enhances and optimizes the K-means algorithm in combination with the artificial bee colony algorithm aiming at fixing the issue that the K-means algorithm is sensitive to cluster center initialization and improving the enterprise precision marketing model’s clustering performance. In the precise marketing scenario of the telecom business, the improved K-means clustering model is utilized to realize the analysis and prediction of telecom customers, as well as to carry out precise marketing based on the predicted findings. Finally, the optimized K-means clustering algorithm can objectively and comprehensively reflect the characteristics of telecom customer value segmentation, efficiently mining future clients and preventing blind marketing by enterprises, based on the model's actual verification results. Simultaneously, it provides substantial data support for telecom enterprises’ resource planning, as well as pointing out the next step in increasing market share.

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Using the Mathematical Model on Precision Marketing with Online Transaction Data Computing

Cited by 4 publications

References 19 publications

Research on the Application of KNN Algorithm Incorporating Gaussian Functions in Precision Marketing Classification of E-commerce Platforms

Research on the Application of KNN Algorithm Incorporating Gaussian Functions in Precision Marketing Classification of E-commerce Platforms

Research on the classification and application of precision marketing based on big data e-commerce platforms

Enterprise Precision Marketing Effectiveness Model Based on Data Mining Technology

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