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This product automatizes some of the most important tasks that media agencies and more generally marketers have to perform during the setup and the management of an advertising campaign. Furthermore, Adcube, exploiting predictive models, supports marketers on making decisions that significantly impact on their business. These tools, through a user-friendly interface, suggest the optimal investment strategies and actions to be taken on advertising platforms to maximize the performance.
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During the definition of a contract with the end client, the marketers must define the campaign goal in terms of volumes, ROI, traffic, etc. The definition of objectives determines the amount of budget invested for the entire campaign and marketer are often conditioned by the achievement of these objectives.
For this reason, It is crucial to predict in advance the potential of a campaign in order to both define achievable goals with the client and above all to establish what is the amount of budget that should be invested in advertising.
SpendOpt is a tool based on Machine Learning techniques that predicts the performance of a set of advertising campaigns in terms of volumes and ROI depending on the overall budget invested. SpendOpt answers the hard question "How much should I invest?" that the clients of a media agency ask themselves. The output of the algorithm are the Volumes / Budget and ROI / budget curves that suggest to the end customer how much to invest to achieve their goals.
One of the more challenging tasks that marketers need to perform when managing an advertising campaign is the budget optimization. Typically, the marketer has to manage, for each customer, a monthly budget that has to be partitioned over a set (typically tens or hundreds) of advertising campaigns that target different classes of users and belonging to different advertising channels.
BudOpt is an Artificial Intelligence system that optimizes the budget of a set of advertising campaigns. Given a set of campaigns, an overall budget, every day, BudOpt optimally distributes the budget over the advertising campaigns with the aim of maximizing revenue while respecting the business constraints defined by the customer.
BudOpt uses “online machine learning” techniques suitable for settings in which data are acquired sequentially and used to improve future strategies. In order to improve the long-term allocation strategy, BudOpt appropriately balances revenue optimization strategies with exploration strategies aimed at acquiring useful information to improve its predictions. Furthermore, BudOpt's strategy aims not only to maximize revenue in the long term, but also to obtain stable daily performance as often requested by the end customer.
During the setup and the management of an advertising campaign, marketers have to identify the most promising users to target with their advertisements. Primarily, marketers have to identify those users who are most likely to generate a conversion after clicking. Secondly, they have to define portions of users large enough to guarantee the complete expenditure of the monthly budget. Since the number of features that characterizes users can be very large (eg. sex, geographical area, time, device, interests, age, etc.), searching for the optimal target can be very hard for a human operator.
TargOpt is a Machine Learning tool that optimizes the target of an advertising campaign. This algorithm receives as input the historical data of existing campaigns and returns a new set of campaigns directed to different classes of users and automatically create them in the advertising platforms.