One of the most obvious ways is to compare organic search traffic to the dates new algorithms go live. Checkout the Google Algorithm Change History page. There is even a chrome plugin called Chartelligence that overlays the algorithm change dates over your traffic data.

Also checkout webmaster tools for messages for unnatural link warnings from Googs themselves.

Hope that helps!

I have a client who is currently ranking close to the top of the Google Places results for a handful of important local keywords.

They have a competitor who is pretty consistantly ranking in number #1 and #2 organic positions, above the Places results.

Does anyone know the average click through rates for results similar to these? Do the #1 and #2 organic positions still outperform the lower placed Google Places (with side map) results when the search intent is very local?

I just wonder if they are possibly in a better position as-is if people are more likely to use the map or places listings on the SERPs page, rather than click through to another site.

Thanks

Perhaps this will get your on the right track, from http://flintanalytics.com/how-to-determine-if-you-should-advertise-higher-priced-goods-to-mac-users/

Average Order Value Statistical Significance

To do this you need to download the transaction data for both variants, because you need to be able to determine the variance in order value. You can download this data by going to conversions > ecommerce > transactions in Google Analytics. Once there, Google only lets you export 500 rows of transactions at one time, so to keep it simple I ensured my date ranges were under 500 transactions.

Once you get the order values for each set of customers in Excel you need to perform a t-test so that you can compare the means of the two groups. This t-test will give you the p-value that you will use to determine if the difference in average order values between the groups is significant.  Yout formula in excel should look like the following:

=t.test(array of group 1, array of group 2,two-tailed distribution, two-sample equal variance homoscedastic)

If possible add unique content to your category pages, and use unique post extracts. Then no need to block.

Don't block "read more" as these link to full posts.