Dynamic creative optimization
It is proposed that this article be deleted because of the following concern:
If you can address this concern by improving, copyediting, sourcing, renaming, or merging the page, please edit this page and do so. You may remove this message if you improve the article or otherwise object to deletion for any reason. Although not required, you are encouraged to explain why you object to the deletion, either in your edit summary or on the talk page. If this template is removed, do not replace it. This message has remained in place for seven days, so the article may be deleted without further notice. Find sources: "Dynamic creative optimization" – news · newspapers · books · scholar · JSTOR Nominator: Please consider notifying the author/project: {{subst:proposed deletion notify|Dynamic creative optimization|concern=no apparent reliable sources for this particular bit of advertising jargon.}} ~~~~Timestamp: 20160713042438 04:24, 13 July 2016 (UTC) Administrators: delete |
Dynamic creative optimization (DCO), is a form of programmatic advertising that allows advertisers to optimize the performance of their creative using real-time technology.[1] While the actual optimization approaches may vary, they almost always involve the use of multivariate testing. The DCO process consists of creative development, identification of test variables, definition of the optimization objective, and method of optimization.[2] Creative development is done using creative studio tools like Adobe Photoshop. It may include video, animation, native components, and interactive elements. Test variables represent the parts of the ad creative that are varied in the multivariate testing framework. These commonly include graphical elements, ad copy, colors, and click-through actions. The optimization objective can be initial engagement, a user action (such as click or install), or a post-install metric (such as purchase, registration, or lifetime value). Optimization of this objective is carried out using some form of discrete or combinatorial optimization.
Dynamic versus static
Most campaign creatives are optimized statically, i.e., a few alternate ad creatives are developed and then tested using a split sample to select the best performing creative. This process ignores many factors (such as time-of-day, day-of-week, seasonality, geographical region, and inherent variations in user preference) that may play a significant role in the performance of the creative. By adjusting the optimal solution dynamically, DCO can account for all these factors making the optimization process more accurate and stable.
References
- ^ "Dynamic Creative Optimization: What is it? | ProgrammaticAdvertising.org". Retrieved 2016-07-13.
- ^ "How Dynamic Creative Optimization Works". 2011-01-17. Retrieved 2016-07-13.
- http://marketingland.com/martech-landscape-marketers-know-dynamic-creative-optimization-dco-creative-management-platforms-cmps-182352
- http://www.beet.tv/2016/05/david-moore-2.html
- https://support.google.com/richmedia/answer/2691686?hl=en-GB
- http://connexity.com/articles-white-papers/dynamic-creative-programmatic-world/
- http://www.aarki.com/blog/impact-of-creative-optimization-on-campaign-roi
- http://www.mediapost.com/publications/article/272461/dynamic-creative-in-a-programmatic-world.html