[1]李 璐,何利力.基于多指标的 BP 神经网络产品进退模型设计[J].计算机技术与发展,2021,31(12):39-44.[doi:10. 3969 / j. issn. 1673-629X. 2021. 12. 007]
　LI Lu,HE Li-li.Design of BP Neural Network Product Launch and Withdrawal Model Based on Multidimensional Indicators[J].,2021,31(12):39-44.[doi:10. 3969 / j. issn. 1673-629X. 2021. 12. 007]
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基于多指标的 BP 神经网络产品进退模型设计()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年12期
页码:
39-44
栏目:
大数据分析与挖掘
出版日期:
2021-12-10

文章信息/Info

Title:
Design of BP Neural Network Product Launch and Withdrawal Model Based on Multidimensional Indicators
文章编号:
1673-629X(2021)12-0039-06
作者:
李 璐; 何利力
浙江理工大学 信息学院,浙江 杭州 310018
Author(s):
LI Lu; HE Li-li
School of Information Science and Technology,Zhejiang Sci-Tech University,Hangzhou 310018,China
关键词:
产品进退; 多维指标; BP 神经网络; AHP 层次分析法; 大数据
Keywords:
product’s launch and withdrawal; multidimensional indicators; BP neural network; AHP analytic hierarchy process; big data
分类号:
TP391.1
DOI:
10. 3969 / j. issn. 1673-629X. 2021. 12. 007
摘要:
在大数据背景下,企业管理思维和营销思维发生转变,企业为促进自身发展,重视产品进退与市场营销结合带来的影响,若能准确地把握产品在市场上的投放或退出,有助于提高企业整体营销思想,能够为企业带来良好的经济效益,但产品进退与市场营销的结合蕴含着潜在的市场风险,因此在企业产品管理中还需讲究一定的科学策略。 基于此背景,该文主要针对如何运用产品进退将企业管理与市场营销相结合,根据影响产品进退的多维指标:上柜率、再购率、同价类销量比、商业存销比、订足率、销售年限、是否进入衰退期,提出在大数据环境下多维指标结合改进 BP 神经网络与 AHP 分析算法,建立市场产品进退预测模型,判断市场是否接收产品的投放或退出,并与 BP 神经网络模型、SVM 模型进行对比。实验表明,结合多维指标的改进 BP 神经网络预测模型的精度明显优于其他两类,为大数据下产品进退预测提供了方法。
Abstract:
Under the background of big data,the corporation thinking of management and marketing has changed. In order to promote its own development,enterprises attach importance to the impact of the combination of product’s launch and withdrawal and marketing. If the enterprise can accurately grasp the product launch or withdrawal in the market,it will help improve the overall marketing thinking of the enterprise and bring good economic benefits to the enterprise. However,the combination of product’s launch and withdrawal and marketing contains potential market risks, so certain scientific strategies need to be paid attention to enterprise product management.Based on this background,we mainly focus on how to use product’s launch and withdrawal to combine corporate management with marketing. According? ? to the multi-dimensional indicators that affect product’s launch and withdrawal:product listing rate,repurchase rate,sales ratio of the same? price category,commercial stock-to-sales ratio,subscription rate,sales period and whether entering a recession,we propose to combine multi-dimensional indicators to improve BP neural network and AHP analysis algorithm in the big data environment,establish market product advance and retreat prediction model, and judge whether the market accepts product launch or withdrawal.Compared with the BP neural network model and the SVM model,it is showed that the accuracy of the improved BP neural network prediction model combined with multi-dimensional indicators is significantly better than the other two types,which provides a method for predicting product advance and retreat under the big data background.
更新日期/Last Update: 2021-12-10