The Croatian AI startup airt and the technology company SeekandHit have presented a joint Capt’n.ai solution for optimizing digital advertising campaigns on platforms Google and Facebook. They have joined forces to simplify this process and elevate it to a higher level. SeekandHit is responsible for the development of the application and its integration with advertising platforms, while airt is working on creating AI engine that enables budget prediction and redistribution. Capt’n.ai allows for campaign optimization across networks on a daily basis as it automates the process of daily entry into campaigns, analyzing their results, and optimizing budgets.
Advertising on Facebook and Google networks, to be effective, consumes significant amount of time. For advertising goals to be successful, regular and continuous optimization of campaigns and their budgets is necessary. This involves daily analysis of results, adjusting parameters, and reallocating part of the budget to networks that are more effective in achieving those goals. And of course, all of this requires quick and timely reactions, which poses a significant challenge for those managing a large number of campaigns.
The current principle of setting up a campaign is that the initial budget allocation by channels is made based on some previous experience, and then optimization is done or, better said, should be done, through regular and manual parameter adjustments. —
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For example, a campaign may have 20 percent of the budget allocated to search ads on Google, 40 percent to display ads on the Google network and 40 percent to the Facebook network. Once the budget is divided among individual channels, each of those channels allows for an (increasing) level of automation to best and most efficiently spend the allocated budget within its environment. On platforms such as Google and Facebook, advertisers can determine the total budget that needs to be invested over a specific time period and specify what it is intended for – website visits or purchasing the advertised product – whereby the system finds the optimal strategy to maximize the desired outcome.
However, none of these networks provide a mechanism for automatic optimization of budget allocation between competing channels for clear business reasons. In other words, such budget optimization across different platforms is ultimately done manually, which not only takes a lot of time but is also subject to human errors.
