Boosting Retailer Revenue by Generated Optimized Combined Multiple Digital Marketing Campaigns

TL;DR

This paper presents a neural network-based approach to generate and optimize combined digital marketing campaigns, significantly boosting online retailer revenue through personalized and ranked campaign selection.

Contribution

The work introduces DMCNet for personalized campaign generation and a sub-modular optimization method for ranking, enabling effective combined campaign recommendations for online retailers.

Findings

Online GMV increased by approximately 6% with the proposed method.

The neural network effectively generates personalized campaign pools.

The combined campaigns outperform individual campaigns in revenue contribution.

Abstract

Campaign is a frequently employed instrument in lifting up the GMV (Gross Merchandise Volume) of retailer in traditional marketing. As its counterpart in online context, digital-marketing-campaign (DMC) has being trending in recent years with the rapid development of the e-commerce. However, how to empower massive sellers on the online retailing platform the capacity of applying combined multiple digital marketing campaigns to boost their shops' revenue, is still a novel topic. In this work, a comprehensive solution of generating optimized combined multiple DMCs is presented. Firstly, a potential personalized DMC pool is generated for every retailer by a newly proposed neural network model, i.e. the DMCNet (Digital-Marketing-Campaign Net). Secondly, based on the sub-modular optimization theory and the DMC pool by DMCNet, the generated combined multiple DMCs are ranked with respect to their revenue generation strength then the top three ranked campaigns are returned to the sellers' back-end management system, so that retailers can set combined multiple DMCs for their online shops just in one-shot. Real online A/B-test shows that with the integrated solution, sellers of the online retailing platform increase their shops' GMVs with approximately 6$\%$.