Data Science Seminar
Time: 2pm to 3 pm
Venue: NKN Hall
Measuring Digital Advertising Effectiveness using Deep Learning
Kantar Analytics, Chennai.
Advances in artificial intelligence and deep learning are revolutionizing the way companies are leveraging data analytics to power solutions that can deliver more value to clients. In this talk, we will discuss one such solution developed by the Data Science & Innovation team of Kantar Analytics that leverages the power of Recurrent Neural Networks (RNNs) to measure the effectiveness of digital advertising. Advertisers typically serve multiple ads to a given user in the hope of incrementally building brand awareness until their combined effect eventually leads the user to purchase the product being advertised. Our deep learning models use this data to derive insights on which ads work best for different audiences, how soon their impact saturates, how best to space them apart in time etc. These insights can be used to quantify the impact of advertising campaigns and to optimize advertising budgets to maximize ROI. We will discuss our modeling methodology, network architectur e, validation criteria and how we draw insights from the model using simulations. We will conclude by briefly introducing a few other AI/ML solutions we have built using cutting-edge data science techniques to answer real-world business problems.
About the speaker: Dinesh is a Partner at the Data Science & Innovation team at Kantar Analytics where he has been leading several R&D initiatives on developing state-of-the-art solutions powered by advanced data science techniques. He has extensive experience in using deep learning and machine learning algorithms across a variety of problems such as computer vision, time-series modeling, text analysis and ROI modeling. He has directed studies for multiple clients across different geographies and industries, addressing issues such as segmentation and targeting, customer satisfaction and loyalty, and brand strategy. Dinesh received his Ph.D. from the University of Michigan, Ann Arbor.