An In-ad contents-based viewability prediction framework using Artificial Intelligence for Web Ads

In the current competitive corporate world, organizations rely on their products' advertisements for surpassing competitors in reaching out to a larger pool of customers. This forces companies to focus on advertisement quality. This work presents a content-based advertisement viewability prediction framework using Artificial Intelligence (AI) methods. The primary focus here is on the web-advertisements available on various online shopping websites. Most of the past work in this domain emphasizes on the scroll depth and dwell time of an ad. However, the features that directly influence the viewability of an ad have been overlooked in the past. Unlike other approaches, this work considers multiple in-ad features that directly influence its viewability. Some of these include color, urgency, language, offers, discount, type, and prominent gender. This work presents an AI-based framework for identifying the features attributing towards increased viewability of ads. Feature selection techniques are executed on the dataset to extract important attributes. Afterward, clustering is applied to confirm the number of class labels assigned to the instances. To validate the clustering results, three validation indices are used here, namely Davies Bouldin Index, Dunn Index, and Silhouette Coefficient. Five classifiers, i.e., Support Vector Machine, k- Nearest Neighbors, Artificial Neural Network, Random Forest, and Gradient Regression Boosting Trees are trained using multiple features and viewability of an ad is predicted. The obtained results confirm that various in-content ad features, i.e., gender, type, discount, layout, and crowdedness play a vital role in predicting an ad's viewability.