Using AI for improving performance and design of ad creatives
The ad creative is the image of the ad that the user sees on their device. It includes information such as the image dimensions, its placement on the screen or within the app, the native resolution of the creative and the mobile screen it is displayed on and similar parameters. Why exactly a particular ad is visually appealing and another not is hard to determine. Using data from the performance of hundreds of thousands of creatives that Inmobi displays, we model the performance of an ad creative based on the objects in a creative and their relative positions and sizes. We use state of the art convolutional neural nets to detect objects, text, and logos in creatives and their positions. Our model is able to provide actionable insights to creative designers on how to modify the creative to improve its performance. In general, our approach would be useful for understanding how the composition of an image relates to any engagement metric chosen by a researcher or business.
- Description of a creative
- Measuring performance of a creative
- Object detection using CNNs
- Various object detection models
- Insights from data exploration
- Interpreting the model
- Take home messages
Farhat Habib is a Director in Data Sciences at Inmobi currently working on improving creatives and anti-fraud. Farhat has a PhD and MS in Physics from The Ohio State University and has been doing data science before it was cool. Prior to Inmobi he worked on solving logistics challenges at Locus.sh. Before that he was at Inmobi working on improving ad targeting on mobile devices and prior to that he was at Indian Institute of Science Education and Research, Pune leading research on genomic sequence analysis and computational biology and bioinformatics. Farhat enjoys working on a wide range of domains where solutions can be found by the application of machine learning and data science.