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Finding the right consumer: Optimizing for Conversion in Display Advertising Campaigns

Finding the right consumer: Optimizing for Conversion in Display Advertising Campaigns. Offense: Harini Sridharan, Abhinav Kachchwaha. Who is the target audience for the paper? The model has to be implemented by advertisers? Publishers? This is not clear in the paper.

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Finding the right consumer: Optimizing for Conversion in Display Advertising Campaigns

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  1. Finding the right consumer: Optimizing for Conversion inDisplay Advertising Campaigns Offense: Harini Sridharan, Abhinav Kachchwaha

  2. Who is the target audience for the paper? The model has to be implemented by advertisers? Publishers? This is not clear in the paper. • For the purpose of creation of user profiles, user activity preceding the conversion is tabulated. How are different users distinguished here, based on cookies? Payment info?

  3. Conversion rates for a particular campaign can be dependent on a lot of external factors • holiday season • offers, • Popularity • reviews and ratings on the landing page of the website. Are any of these variables even part of the data gathered?

  4. The paper specifies that activity of the user preceding the conversion containing page clicks , ad views etc. Are these recorded only pertaining to the converted ad? Or all ads the user was interested in general? • In the case of ad views, and clicks how is the case of a fraudulent click detected?

  5. Dataset comprised of online activity of users over 4 weeks, and since conversion rates and user behavior is being measured here, there can be a bias created due to various external factors for example if the data was collected around Christmas, the conversion rates will be higher, ads on popular websites like amazon may have higher conversion rates? • On page 475, there is a mention of certain threshold T. How is this value assigned? How is the selected value justified?

  6. Conversions can mean different things to advertisers. What, in your dataset, defines a conversion? A purchase? Account sign-up? The paper is ambiguous in this regard. • The paper talks about a javascript that records conversions from the ad’s landing page. How are you going to handle recording different types of conversion for users, as conversions can mean different things to different advertisers.

  7. According to the paper, this model requires atleast a day’s history before it can predict for conversions, this while the predictions for behavioral targeting are going towards real-time. • How is feature selection performed on the campaign features data? How are you accounting for missing attributes as this might lead to poor model fit?

  8. For the purpose of classification, you consider a binary system of positive and negatives where positive refers to a conversion and negative refers to all campaigns that were not converted. Does the intermediate data/user behavior account for nothing in your prediction model? How can you omit data that led to the conversion especially when they are correlated?

  9. Conversion process in quite complex , many books have been published explaining the steps in purchasing cycle. Conversion depends on all these steps and considering it as binary process is not a fair assumption. • Review • Analysis • Comparison (quality, price)

  10. Method limitations SVN and Regression • runs very slow • Computationally expensive • sensitive to irrelevant features Naïve bayes • Assumes independence of features  

  11. Regression can be misleading • Starting points are the most important parts of regression , if you arestarting point differs from where it should be it can lead to wrong results. • Missing positive and negative correlation • User representation section does not include explanation for new users. • Campaign representation considered several variables can range from 0 to millions. • Threshold value of convertor for campaign is varies with campaign.? If I am having 1000 campaign we should have 1000 threshold value?

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