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So here's what you'd do if you wanted to have actual metrics.

1. Analyze which groups of users you've got, and how large they are

2. For each recorded metric, categorize it into the appropriate group

3. Weight each set of metrics by the actual sizes of each user group from step 1

If some users groups have no, or too little data, pay users in these groups to do surveys.

Now you've got actual metrics telling you how many people will be affected.

Going beyond that, measure how long each visit was, and how long each session was. If users don't agree, again, pay them to take surveys or to watch them browse.

Now you can weight the metrics by how many individual sessions will be disrupted, and how much time you've cost users with your decision.

And this is just the very basics of collecting proper metrics, as done by proper polling institutes. You could contract Nielsen (US) or GfK (DE) to do this for you, they've got decades of experience collecting such metrics for all kinds of industries.

But "Number of page loads of opted in users" is a metric that only tells you how many ad views will be disrupted by a change.



In this instance, why not compare against page loads of documents that have the same substantive content but in a different format?

Also, I'm not sure where ads came into this discussion; none of the documents at issue here have ads in them.


Re: ads, because that's the only use case where unique views is actually relevant.


We're dealing in raw page load counts here, not unique views.




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