PromotionLens: Inspecting Promotion Strategies of Online E-commerce via Visual Analytics

被引：0

作者：

Zhang C. ^{[1
]}

Wang X. ^{[1
]}

Zhao C. ^{[1
]}

Ren Y. ^{[1
]}

Zhang T. ^{[2
]}

Peng Z. ^{[3
]}

Fan X. ^{[4
]}

Ma X. ^{[5
]}

Li Q. ^{[1
]}

机构：

[1] School of Information Science and Technology, ShanghaiTech University

[2] School of Artificial Intelligence, Sun Yat-sen University

[3] School of Entrepreneurship and Management, ShanghaiTech University

来源：

IEEE Transactions on Visualization and Computer Graphics | 2023年 / 29卷 / 01期

关键词：

'what-if' analysis; E-commerce; promotion strategy; time-series prediction; visualization;

D O I：

10.1109/TVCG.2022.3209440

中图分类号：

学科分类号：

摘要：

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. © 2022 IEEE.

引用

页码：767 / 777

页数：10

共 59 条

[1]

Agmeka F., Wathoni R.N., Santoso A.S., The influence of discount framing towards brand reputation and brand image on purchase intention and actual behaviour in e-commerce, Procedia Computer Science, 161, pp. 851-858, (2019)

[2]

Agrawal R., Adhikari R., An Introductory Study on Time Series Modeling and Forecasting, (2013)

[3]

Akter S., Wamba S.F., Big data analytics in e-commerce: A systematic review and agenda for future research, Electronic Markets, 26, 2, pp. 173-194, (2016)

[4]

Armstrong J.S., Collopy F., Error measures for generalizing about forecasting methods: Empirical comparisons, International Journal of Forecasting, 8, 1, pp. 69-80, (1992)

[5]

Armstrong M., Chen Y., Discount pricing, Economic Inquiry, 58, 4, pp. 1614-1627, (2020)

[6]

Barrow D.K., Crone S.F., Kourentzes N., An evaluation of neural network ensembles and model selection for time series prediction, The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1-8, (2010)

[7]

Brainerd J., Becker B., Case study: E-commerce clickstream visualization, IEEE Symposium on Information Visualization, 2001. INFOVIS 2001, pp. 153-156, (2001)

[8]

Cai F., Fan X., Bodenhausen G., There is no such thing as a free lunch: The negative effect of zero price on consumer demand, ACR European Advances, (2018)

[9]

Camastra F., Filippone M., A comparative evaluation of nonlinear dynamics methods for time series prediction, Neural Computing and Applications, 18, 8, pp. 1021-1029, (2009)

[10]

Cankurt S., Subasi A., Tourism demand modelling and forecasting using data mining techniques in multivariate time series: A case study in turkey, Turkish Journal of Electrical Engineering & Computer Sciences, 24, 5, pp. 3388-3404, (2016)

← 1 2 3 4 5 6 →