Dynamic optimization models for displaying outdoor advertisement at the right time and place

ABSTRACT

Digital billboards, as a new form of outdoor advertising, has gained popularity in recent years per its revolutionized way to control when and where the specific ads appear. However, this development also demands more complicated optimization for strategic deployments: the advertisers have to not only decide on a set of locations to display their ads, but also when to display them. The existing static optimization approaches become insufficient for this dynamic scenario to match advertisement and intended audience. Therefore, this research proposes three models in a workflow to mine mobile phone data and points of interest (POIs) data and to meet advertising needs in various situations. The three optimization models include a dynamic audience model to maximize the coverage of the target users, a dynamic environment model to maximize the coverage of the target environment, and a dynamic integrated model to maximize the coverage of both target audience and environment. A case study using shopping ads in Wuxue, China tests the three optimalization models. The results show that the proposed models are effective for providing an optimal solution for digital billboard configuration with a greater coverage of the target audience and environment compared to the state-of-the-art static models.

KEYWORDS:

• Digital outdoor advertising
• mobile phone data
• maximal coverage location problem

Acknowledgments

The authors sincerely appreciate all valuable comments and suggestions from the anonymous reviewers that helped us to improve the quality of this article.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data and codes availability statement

The data and codes that support the findings of this study are available with a DOI at https://doi.org/10.6084/m9.figshare.12932963. The related mobile phone data was provided by China Mobile, and cannot be made publicly available due to the protection of participant privacy. Mocked mobile-phone data are shared at the link to show how the codes work.

Supplementary material

Supplemental data for this article can be accessed here.

Notes

1. There is a mismatch between the period of the mobile phone data and the POI data. But as there was no major urban development between 2015 and 2017 in Wuxue, we expect this mismatch will not have a big impact on the results.

2. As the road network might also be a potential factor that affects the viewing distance of billboards, we implemented a further experiment using service areas with road network distances. The results are presented in the Appendix, and again showed the superiority of the proposed dynamic audience model and dynamic environment model, compared to state-of-the-art static models.

Additional information

Funding

This research was supported in part by the National Key R&D Programme [2017YFC1405302] and National Natural Science Foundation of China [41771473, 41231171].