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Ad360 automatically integrates the results from its AI Research Lab

Ad360 has its own internal AI Research Lab focused on Marketing AI. The Research Lab team is composed of PhDs in Mathematics, Data Science & AI.
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Artificial Intelligence
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Machine Learning
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Data Science
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Data Mining
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Statistics
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Automation

Research Papers published in scientific journals & conferences by our Research Lab

Most recent
Latest scientific publication

A New Optimization Layer for Real-Time Bidding Advertising Campaigns

Journal: Intelligent Data Analysis international journal (IDA).

Abstract: introduce an algorithm which maximizes the chosen kpi by optimally configuring the advertising channels and putting them in competition with each other. To do so, we propose an algorithm for advertisers to add an optimization layer on top of advertising channels.

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An Enhanced Ad Event-Prediction Method Based on Feature Engineering

Conference: 8th international conference on Soft computing, Artificial Intelligence and applications (SAI 2019).

Abstract: introduce an enhanced method for ad event prediction (i.e. clicks, conversions) by proposing a new efficient feature engineering approach. To do so, we propose two novel adjusted statistical measures for feature selection and provide an enhanced framework for ad event-prediction by analyzing the huge amount of historical data.

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Ensemble learning using frequent itemset mining for anomaly detection

Conference: 8th international conference on Artificial Intelligence, soft computing and Applications (AIAA 2018).

Abstract: propose a hybrid supervised learning of anomaly detection using frequent itemset mining and random forest with an ensemble probabilistic voting method. The main contribution is to boost base (weak) learners to strong learners by ensemble learning, which can make very accurate classifiers.

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Fast and Accurate Temporal Data Classification using Nearest Weighted Centroid

Conference: 8th international conference on Soft computing, Artificial Intelligence and applications (SAI 2019).

Abstract: introduce an enhanced method for ad event prediction (i.e. clicks, conversions) by proposing a new efficient feature engineering approach. To do so, we propose two novel adjusted statistical measures for feature selection and provide an enhanced framework for ad event-prediction by analyzing the huge amount of historical data.

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