Optimizing ROI of Digital Advertising using Bid Landscaping
The world economy is a $80 Trillion economy driven by $500 Billion spent on advertising. Out of this, Digital Advertising forms the largest chunk at more than $300 Billion for the year 2019. With the backdrop of COVID-19 hitting the world as several economists doubt recession, business continuity and protection of livelihoods is of paramount importance to the businesses. Programmatic Advertising offers unparalleled targetability, flexibility, and measurability that could help businesses control their advertising costs. Real Time Bidding enabled Programmatic Advertising has allowed advertisers to competitively evaluate the value that each potential ad-space delivers and place a real time bid to win the ad-space and give businesses a unique opportunity of effective advertising.
Effective advertising consists of two aspects - knowing where to bid, and knowing what to bid. These two pieces of information together enable efficient management of a digital ad-campaign. For the purpose of this talk, I will consider "where" a solved problem. However "what" is still an actively researched problem. Bid-Landscape is a model, mapping the distribution of bids vs wins that allows us to calculate an optimal bid for an auction in Real Time Bidding scenario. Typically, Gaussian and Log-normal distributions are used to approximate the distribution of bids and wins. Such assumptions are seldom true in practice and do not produce a generalized model. To complicate things further, this modeling has to be done on left censored data. Left censored refers to an arrangement where only the auction winner knows the winning price, where as the other participants only know that they have lost the auction.
To overcome an assumed distribution, our team used a deep neural network to learn the bid landscape. We have also leveraged Domain Embeddings, a novel approach for this task. This embedding is learnt using a character CNN model. Character CNN allows us to extrapolate the learning to unseen domain names. We use Feed Forward layers to model the bid-landscape. The dataset used is left censored and contains approximately 14 Billion rows.
In this talk I will be discussing various approaches that we tried and what worked. Bid Landscape is a dynamic distribution that changes over time. Hence I also discuss the validity of a model across multiple time periods.
I will conclude the talk with a small discussion on the business impact of this model and results obtained on a few live digital ad-campaigns.
Outline/Structure of the Talk
- Very Brief introduction to digital advertising (1 min)
- What is RTB (1 min)
- Problem Statement (2 min)
- Final Result (2 min)
- Bridge the Gap between 2 & 3
- Dataset (1 min)
- Target Variables (2 min)
- Challanges (2 min)
- Approaches tried ( 2 min)
- Network Architecture (1 min)
- Difference from existing approaches (2 min)
- Cost, Training time etc (1 min)
- Discussion of impact on live campaigns (2 min)
- Q&A (1 min)
- Practical use of Machine learning in Digital Advertising
- Use of Character CNN
- Use of Deep Learning for modeling distributions
- Intro to Real Time bidding
Data Scientists, Students, Marketers, Digital Advertisers, Business Owners, Machine Learning Practitioners, Data Science Product Managers
Prerequisites for Attendees
This talk will cover bid landscaping in digital advertising. Participants with a business interest in the talk only need to know briefly about digital advertising and Real Time Bidding.
Participants attending the talk from a technical point of view should have exposure to:
- Machine Learning - Basics
- Deep Learning - Basics
- Probability and Distributions
schedule Submitted 2 years ago
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In the programmatic era, with a lot of players in the market, it is quite complex for a buyer to reach the destination, namely advertising slot from the source, namely publisher. Auction Duplication, internal deals between DSP & SSP, and fraudulent activities are making the existing complex route even more complex day by day. Due to the aforementioned reasons, it is fairly evident that a single impression is being sold through multiple routes by multiple sellers at multiple prices. The new dilemma that has emerged recently is: Which route/path should the buyer choose and what should be the fair price to pay?
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