The third party online metrics controversy - part 2
Posted by Florian Pihs at 5/5/2008 7:16 PM and is filed under Interactive Strategy,Analytics Strategy,Campaign metrics 
Reader Tai Te, posted an interesting comment on the "The third party online metrics controversy" post, that I used to argue that 3rd party metrics are unnecessary to plan online marketing campaigns and and to evaluate campaigns success. This post generated a lot of interest due to Kaiser Kuo's mention his the Digitalwatch blog.
Tai Te writes:
I highlight this comment because it echos common concerns about using web analytics metrics for campaigns planning and evaluation.
I can agree with this criticism for "one off" campaigns. It becomes mute though when you regularly or continuously run campaigns (which is the case for most online advertisers) since you can build on experience. It also leads to best practices that accommodate media testing and continuous media optimization.
Testing and ongoing optimizations are critical for other reasons too. While reliable 3rd party metrics will be able to tell you the number of impressions and clicks you can expect for your campaign given a dollar number and media, these metrics will not allow you to predict the results of the campaign.
Real campaign results (outcomes) depend on actions users take on your landing page (e.g. buy, register, play video, learn more etc.) I would argue that its these outcomes you should optimize your media buying for.
Tai Te writes:
"That's all useful to see if your ad WAS effective but it doesn't help
much when it comes to guessing if your ad WILL BE effective."
I highlight this comment because it echos common concerns about using web analytics metrics for campaigns planning and evaluation.
I can agree with this criticism for "one off" campaigns. It becomes mute though when you regularly or continuously run campaigns (which is the case for most online advertisers) since you can build on experience. It also leads to best practices that accommodate media testing and continuous media optimization.
Testing and ongoing optimizations are critical for other reasons too. While reliable 3rd party metrics will be able to tell you the number of impressions and clicks you can expect for your campaign given a dollar number and media, these metrics will not allow you to predict the results of the campaign.
Real campaign results (outcomes) depend on actions users take on your landing page (e.g. buy, register, play video, learn more etc.) I would argue that its these outcomes you should optimize your media buying for.
Florian
As you mentioned in your previous post, there is a lack of reliable 3rd party data for web traffic in China. How do you do competitive intelligence analysis? It seems to me that it's a very important part of web analytics. Without knowing your competitor’s standing, how do you define your ranking in the industry and measure your success? If no accurate data is available, if there is any general industry benchmarks out there?
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Good point Angela. For competitive intelligence we DO use 3rd party data. In this context, 3rd party metrics are the only available tool. There is one key difference though that is important to bear in mind. Publishers (sites that carry advertising) do have a clear interest to manipulate 3rd party PV and UV data, while most advertisers have not. Therefore we find competitive data from the same databases (e.g. IResearch User Tracker) much more reliable.
Regardless, benchmarks remain a pain point for online marketers in China.
- While we are able to get PV & UV of competitive sites, we can get it only by site (not by campaign or landing page).
- There is no benchmarks for CTR, Bounce Rate and conversion rates, I am aware of.
- There is no CPM, CPC or CPV (Cost per Visit) benchmarks either.
In the end we have to rely on our own internal benchmarks, which makes things notably more difficult.Reply to this
Thank you for answering my question! Florian. I can imagine how hard it is with very limited data sources. I believe that's more of an infrastructure issue in China, not only in web analytics world but applies to other areas as well.
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