Google Analytics and AdWords optimisation with GNU R

9th – 11th March we were at Booster Conference in Bergen to present AdWords optimisation using controlled experiments with GNU R.

In the session we:

  • showed how standard analytics reports can bring misleading results
  • showed that controlled experiments can help to identify the true ROI
  • reviewed a study (PDF) by a team of economists from eBay Research
  • provided examples with real life data
  • and showed how to implement these methods using GNU R and the Google Analytics API – Big Data without Big Code

In the presentation we focused on brand keyword bidding. In this case, paid clicks potentially substitute organic traffic.  The advertisement effect we observe is the total AdWords traffic  which includes not only the truly gained traffic but also the clicks of individuals that would have visited the website even in absence of SEM. The latter is what economists call the missing counterfactual. Missing as we do not observe it. Without this data it is impossible to calculate the true ROI.

The way to find the missing counterfactual is through controlled random experiments. The complete AdWords optimisation toolbox necessary to run controlled experiments can be found in this slideshare presentation.

We are excited to share our methods. Feel free to share your opinion.

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Digital Performance and Ancillary Revenues

Airline ancillary revenues are at an all-time high, up by 21% last year to almost $40bn according to the latest CarTrawler Yearbook of Ancillary Revenue press release. With some analysts hoping for US$130bn in additional ancillary by 2020 clearly hopes for sustained airline profitability rest with ancillary sales as fare competition will remain fierce. However, when only 25% of bookings through metamediaries result in ancillary purchase other channels need to be used to reach the expectations. In this post, we look at how airlines digital performance can help ancillary sales.


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