PromotionLens: Inspecting Promotion Strategies of Online E-commerce via Visual Analytics
August 01, 2022 Β· Declared Dead Β· π IEEE Transactions on Visualization and Computer Graphics
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Authors
Chenyang Zhang, Xiyuan Wang, Chuyi Zhao, Yijing Ren, Tianyu Zhang, Zhenhui Peng, Xiaomeng Fan, Quan Li
arXiv ID
2208.01404
Category
cs.HC: Human-Computer Interaction
Citations
17
Venue
IEEE Transactions on Visualization and Computer Graphics
Last Checked
4 months ago
Abstract
Promotions are commonly used by e-commerce merchants to boost sales. The efficacy of different promotion strategies can help sellers adapt their offering to customer demand in order to survive and thrive. Current approaches to designing promotion strategies are either based on econometrics, which may not scale to large amounts of sales data, or are spontaneous and provide little explanation of sales volume. Moreover, accurately measuring the effects of promotion designs and making bootstrappable adjustments accordingly remains a challenge due to the incompleteness and complexity of the information describing promotion strategies and their market environments. We present PromotionLens, a visual analytics system for exploring, comparing, and modeling the impact of various promotion strategies. Our approach combines representative multivariant time-series forecasting models and well-designed visualizations to demonstrate and explain the impact of sales and promotional factors, and to support "what-if" analysis of promotions. Two case studies, expert feedback, and a qualitative user study demonstrate the efficacy of PromotionLens.
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