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schedule Submitted 1 year ago
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Ujwala Musku - Supply Path Optimization in Video Advertising LandscapeUjwala MuskuData Scientist IIMiQ Digital
schedule 1 year agoSold Out!
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?
In this talk, we will discuss a framework that solves the problem of choosing the best path at the right price in programmatic Video Advertising. Initially, we will give an overview of all the different approaches tried i.e., Clustering, Classification Modelling, DEA, and Scoring based on Classification modeling. Out of these, DEA and Scoring Methodology had better results, and hence a detailed comparison of results and why a particular approach worked better will be illustrated. The final framework explains the two best-worked techniques: 1. Data Envelopment Analysis and 2.Scoring based on Classification Modeling. DEA is a non-parametric method used to rank the Unsupervised dataset of various supply paths by estimating the relative efficiencies. These efficiencies are calculated by comparing all the possible production frontiers of decision-making units (here supply paths). As a statistical and machine learning hybrid, the Scoring method calculates the score against each supply path, helping us decide whether a path is worth bidding.
The results of these models are compared with each other to choose the best one based on campaign KPI i.e., CPM (Cost per 1000 impressions) and CPCV (Cost per completed view of the video ad). A 4 - 8% improvement in CPM is observed in multiple test video ad campaigns, however, there is a dip in the number of impressions delivered. This is tackled by including impressions as an input in both the techniques. These clear improvements in CPM indicate that the technique results in better ROI compared to the heuristic approach. This approach can be used in various sectors like Banks (determining Credit Score) and Retail Industries(supply path optimization in Operations).
Aditya Jain - Optimizing ROI of Digital Advertising using Bid LandscapingAditya JainData Scientist IIMIQ Digital
schedule 1 year agoSold Out!
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.