Research on the B2C Online Marketing Effect Based on the LS-SVM Algorithm and Multimodel Fusion

Huiru Liao and Sang-Bing Tsai

Mathematical Problems in Engineering, 2021, vol. 2021, 1-11

Abstract:

The comprehensive B2C online marketing is analyzed, and the current situation and shortage of comprehensive B2C online marketing strategies are summarized. Then, based on the relevant theories of consumer behavior and online marketing, the model of influencing factors in the purchasing decision-making process of online consumers is preliminarily constructed, the online purchasing behavior of consumers is studied by means of questionnaire survey, and the model is revised and improved through data collection and verification. Finally, based on the model, the online marketing strategy is discussed from the aspects of comprehensive B2C online marketing construction, product positioning, price strategy, channel construction, website design, and so on. It has important guiding significance to comprehensive B2C online marketing practice. Aiming at the B2C online marketing problem of multimodel fusion with multiobservation samples, a new multimodel fusion B2C online marketing algorithm based on LS-SVM is proposed, which is suitable for multiobservation samples. In each B2C online marketing of multimodel fusion, the mode of B2C online marketing to be multimodel fusion is represented by the multiobservation sample set. Firstly, the label of the multiobservation sample set is assumed, and this assumption condition is taken as the constraint condition of the optimization problem in LS-SVM. Thus, the B2C online marketing error of multimodel fusion is obtained. The category of multiobservation samples was determined by comparing the B2C online marketing errors of multimodel fusion under two assumptions. The B2C network marketing prediction method, stacking integrated learning method based on multimodel fusion, is adopted to build a multimachine learning algorithm embedded into the stacking integrated learning B2C network marketing prediction model. Through verification, it shows that the lower the correlation degree, the better the model prediction effect. Compared with the traditional single-model prediction, the B2C network marketing prediction method based on multimodel fusion stacking integrated learning method has higher prediction accuracy. The model prediction effect is better.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:8186849

DOI: 10.1155/2021/8186849

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