Supply Path Optimization in Video Advertising Landscape
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).
Outline/Structure of the Talk
- Introduction to Supply Path Optimization (1 min)
- Problem Statement (2 min)
- Discussion on different approaches - Clustering, DEA, Classification Modelling and Scoring Methodology
Which approach worked? (1 min)
Why did a particular approach work better than the other? (1 min)
- Detail explanation of the approaches worked
Approach 1: Data Envelopment Analysis (4 min)
Approach 2: Scoring Methodology based on Classification Modelling (5 min)
Comparison of the results of all the different approaches tried. (1 min)
Detailed discussion on the results of the finalized approaches (2 min)
- Challenges Faced - Data size issues, Code language, etc (2 min)
- Questions (1 min)
1. How ranking/sorting can be done on Unsupervised Learning?
2. How one can apply these Unsupervised learning techniques in Digital Marketing domain?
Data Scientists, Operations Researchers, Product Managers, Digital Advertising Enthusiasts , Students, Marketers, Digital Advertisers, Business Owners
Prerequisites for Attendees
Basic Knowledge of Classification Modelling and Statistics
schedule Submitted 1 year ago
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