Computer Science > Human-Computer Interaction
arXiv:2307.12213 (cs)
[Submitted on 23 Jul 2023 (v1), last revised 2 Aug 2023 (this version, v2)]
Title:LiveRetro: Visual Analytics for Strategic Retrospect in Livestream E-Commerce
View a PDF of the paper titled LiveRetro: Visual Analytics for Strategic Retrospect in Livestream E-Commerce, by Yuchen Wu and 7 other authors
View PDFAbstract:Livestream e-commerce integrates live streaming and online shopping, allowing viewers to make purchases while watching. However, effective marketing strategies remain a challenge due to limited empirical research and subjective biases from the absence of quantitative data. Current tools fail to capture the interdependence between live performances and feedback. This study identified computational features, formulated design requirements, and developed LiveRetro, an interactive visual analytics system. It enables comprehensive retrospective analysis of livestream e-commerce for streamers, viewers, and merchandise. LiveRetro employs enhanced visualization and time-series forecasting models to align performance features and feedback, identifying influences at channel, merchandise, feature, and segment levels. Through case studies and expert interviews, the system provides deep insights into the relationship between live performance and streaming statistics, enabling efficient strategic analysis from multiple perspectives.
| Comments: | Accepted by IEEE VIS 2023 |
| Subjects: | Human-Computer Interaction (cs.HC) |
| Cite as: | arXiv:2307.12213 [cs.HC] |
| (orarXiv:2307.12213v2 [cs.HC] for this version) | |
| https://doi.org/10.48550/arXiv.2307.12213 arXiv-issued DOI via DataCite |
Submission history
From: Quan Li [view email][v1] Sun, 23 Jul 2023 03:10:05 UTC (15,192 KB)
[v2] Wed, 2 Aug 2023 15:22:47 UTC (19,108 KB)
Full-text links:
Access Paper:
- View PDF
- TeX Source
- Other Formats
View a PDF of the paper titled LiveRetro: Visual Analytics for Strategic Retrospect in Livestream E-Commerce, by Yuchen Wu and 7 other authors
References & Citations
Bookmark
Bibliographic and Citation Tools
Bibliographic Explorer(What is the Explorer?)
Connected Papers(What is Connected Papers?)
Litmaps(What is Litmaps?)
scite Smart Citations(What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv(What is alphaXiv?)
CatalyzeX Code Finder for Papers(What is CatalyzeX?)
DagsHub(What is DagsHub?)
Gotit.pub(What is GotitPub?)
Hugging Face(What is Huggingface?)
Papers with Code(What is Papers with Code?)
ScienceCast(What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower(What are Influence Flowers?)
CORE Recommender(What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community?Learn more about arXivLabs.