An E-commerce Searching Advertisement Serving Strategy Based on Data Mining

doi:10.3969/j.issn.1007-7375.2019.01.009

Abstract

Abstract: In order to reduce the cost of advertising investment for businesses, and optimize the shopping experience of users in the e-commerce platform, the massive real data provided by Ali "Tianchi Big Data" platform is applied and a machine learning model is built and trained with the target of advertising conversion rate. By analyzing the model learning results, the influencing factors and effects of search advertising conversion rate are explored. The results show that store logistics services, advertising product sales, consumer preferences, and the degree of intelligence of e-commerce platforms have a greater impact on conversion rates. At the same time, all the influencing factors are classified and analyzed in the four dimensions of users, shops, advertising products and advertising contexts. Based on this, the advertisement serving strategy based on the platform and the merchant perspectives is proposed to provide reference for the e-commerce platform and its resident merchants.

Key words: searching advertisement, conversion rate, data mining, advertisement serving strategy

CLC Number:

F272.3

ZHANG Lei, GUO Feng, HOU Xiaochao. An E-commerce Searching Advertisement Serving Strategy Based on Data Mining[J]. Industrial Engineering Journal , 2019, 22(1): 69-78.

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