Calculating View-Through attribution: Challenges

After analyzing the Google Analytics "Content Report" for my blog, it became evident that the "Post-Impression" attribution post is the most popular post so far. So, I thought I would expand a bit more on that same topic, some of the common challenges that could be faced during the test set up and also interpreting the results.

If this test is being done for a very large advertiser, delivering in the range of billions of impressions in a month, then it is virtually impossible to create a test and control group as the reach of the campaign would be so high. It is highly likely that all consumers will be exposed to the advertising.

1. If all the ads are not third party served, it is impossible to create a test and control, as if there is a x% of Ads which are site served, creating a control group will be hard across the entire advertising network
   
2. Even with all the ads being served via third party ad servers, consumers could get exposed to the Ads on different computers, example work vs. home
   
3. After the results if the lift is negative i.e. it shows that showing the ads did not increase the conversion rate vs. control group – this indicates that the "Brand" has very high brand awareness
   
4. Interpreting the results could be another tough nut to crack; DoubleClick in one of their studies has shown that for Continental Airlines that there is a 67% post-impression attribution. Lately, it has been seen that the post-impression attribution has been pretty low due to increase in online advertising.
   
   1. One more variable could be seasonality, the post-impression attribution has been seen to change based on the different times of the year. The consumer mindset changes based on the seasonality for example it could be very high during the back to school season for school products like laptops, ipods etc