Header Bidding products are certainly the future of Ad Technology, except that it is evolving and is being beneficial among publishers, buyers, and advertisers. This evolving technology solution needs to address certain aspects in terms of its audiences.
Header bidding still got a long way to go to reach the levels.
On a promising note,
- Leading header bidding wrapper solutions are subjected to robust A/B testing among digital publishers before implementing them. The hypothetical results of A/B tests guide the publishers to examine which bidding process work better.
- In today’s technology advancement, with the advent of Machine Learning (ML) and Artificial Intelligence (AI), header bidding has improved a step further to hybrid header bidding to access multiple demand partners.
- It has become easy for the publishers to know which auction types are participating in the bid request that gets executed from the Demand Side Platform (DSP) with more standardization and use of OpenRTB protocols that provide details about the auction types.
- With increasing numbers of open and private ad exchangers in the market for publishers and advertisers, the demand for best header bidding models in terms of complete control of inventory, floor prices, adding/preserving demands, and setting targets have also increased.
- Equivalent to A/B tests, Header bidding has applied Machine Learning, Natural Language, and Deep Learning to
- Optimize the process of bid promotion from Ad Networks
- Determine audience ratio out of user data and content
- Forecast the performance of Ad Content
Header bidding may need to label certain aspects of the execution process and address rising concerns among its open bidding competitor technology.
- Integrating the right wrapper solution without favoring a Supply Side Platform (SSP) demand would be mindful.
- Publishers must be guided with technical expertise while selecting, configuring header bidding wrapper, controlling ad units, editing changes to the list of demand partners, and other related header bidding tasks.
- Privacy should be safeguarded with the increasing number of bidding partners who access data from all the users with impressions.
- Uncertainty and inefficiencies in the existing header bidder model at the SSP level make the outcome of the sequential actions doubtful and results in the highest bidder to lose the auction with publishers missing the opportunity of yielding the maximum price for an impression that the buyer is willing to pay.
- Caching mechanisms should be applied after checking the budget lines of a high bid that is applied repeatedly for a length of time at SSP’s discretion. In such cases, the DSPs (Demand Side Platform) should specify cache lifetime for their business needs.
- In Real-Time Bidding (RTB), publishers and multiple advertisers, selling impressions are unaware of the historical highest bidding prices offered by RTB advertisers among Ad Exchangers running second pricing auction models. Proper algorithm models for reserve price failure rate prediction should be worked out after considering user and page interactions and header bidding information.
Conclusively, the expectation of building custom header bidding products and solutions with capabilities to existing Ad Tech platforms has got potential and phenomenal growth for technological teams to plan, design, and build the right future.