ProBargainHunter.com

This is April 2008 revision of the Popular Deal Sites list. You can find the last month revision of the list here.

The change in rating shows the difference compared to March results and includes all deal sites from my database that meet Alexa Rank < 50,000 requirement. As you can see, the ratings have changed dramatically after Alexa reworked their measuring algorithm. To keep the list at the same length I had to tighten the requirement (it was 100,000 before).

It looks like all sites benefited about the same from this latest change each gaining 40-60% in the ranking. This is similar to what coupon sites went through last month. These changes in rank are meaningless though because they for the most part reflect who of these deal sites benefited the most (and the least) from the algorithm changes

#	Web Site	Alexa Rank		Page Rank		Online Since
1	slickdeals.net (info)	496	(54.54%)	6		10-Nov-1999
2	fatwallet.com (info)	937	(53.22%)	6		29-Nov-1999
3	anandtech.com	2901	(39.84%)	6		24-Aug-1997
4	deals2buy.com (info)	2968	(57.65%)	4		15-Nov-2002
5	dealnews.com (info)	3043	(57.11%)	6		20-Jan-1999
6	techbargains.com (info)	3285	(61.51%)	6	(1)	03-May-1999
7	bensbargains.net	3691	(60.88%)	5		21-Mar-2000
8	dealsea.com	4095	(56.61%)	5		27-Jun-2001
9	dealcatcher.com	6922	(59.05%)	6	(1)	28-Jul-1999
10	dealspl.us (info)	9418	(49.03%)	6	(1)	15-May-2006
11	edealinfo.com	10709	(54.99%)	5	(1)	12-Dec-2000
12 (4)	spoofee.com	11346	(58.31%)	4		31-Dec-2000
13 (1)	cheapstingybargains.com	11855	(55.58%)	5		20-Jan-2005
14 (2)	xpbargains.com	12218	(53.2%)	5		29-Nov-2001
15 (2)	dealigg.com (info)	13182	(49.52%)	4		04-Sep-2006
16 (1)	resellerratings.com (info)	13662	(49.73%)	6		18-Jun-1998
17 (1)	dealsofamerica.com	14441	(55.04%)	4		06-Sep-2003
18 (1)	dealtaker.com (info)	15566	(48.72%)	5		08-Feb-2004
19	dealofday.com	22210	(46.08%)	7		30-Aug-1999
20	gottadeal.com	23350	(50.81%)	5		27-Jan-2003
21	bargainist.com (info)	34328	(46.18%)	5	(-1)	21-Nov-2006
22	gotapex.com	36331	(50.82%)	5		29-Apr-1999
23	dealslist.com	39802	(48.26%)	4		18-Jul-2000
24	judysbook.com	40644	(48.19%)	6		11-Aug-2003
25	bradsdeals.com	46848	(44.44%)	5		29-Jun-2005

I spent some time last night collecting stats and putting them together. The result is a huge table with a lot of numbers which by itself is pretty useless unless you want to use it as a reference.

My goal however was not to impress you with the amount of information but to find if Alexa and Technorati can be valuable tools in estimating a blog (or any website for that matter) popularity.

My assumption was that if blogs belong to the same category and the visitors demographics is similar then Alexa and Technorati ranks can be very precise when compared in a group relative to each other.

How the data was analyzed

The sample set included 62 Personal Finance blogs and you can browse the complete table with raw stats here. I fed this data into a statistical analysis tool and ran several correlation tests between the data sets.

Correlation in statistics is a number ranging from -1.0 to +1.0. The closer it is to +1 or -1, the more closely the two variables are related. A value close to 0 means little relationship. If you want to find more about correlation, here is a good article with some real life examples.

In addition I threw in another statistical coefficient, significance level. This is a number from 0 to 1 which shows how likely a result is due to a pure chance. For better readability, I will show (1 - significance level) so values close to 1 will mean the correlation is more significant (is likely to be true).

Sample size shows how many blogs are included in a particular calculation. Since some blogs lack certain stats, the calculations will exclude these blogs from the original set. Bigger sample set means better calculation precision.

Results: high correlation

Here are the most interesting findings. The correlation of 0.9 or more indicates that the values are highly related to each other. Please note however that correlation highlights overall statistical trend and doesn’t exclude exceptions. A good example is Not Made of Money which lags by both Alexa and Technorati but still scores impressive daily visitors counts.

Below is a scatter plot matrix which shows how the dots distribute around X/Y coordinates where Y is 1/Technorati and X is daily visitors.

Results: some correlation

Here are some results that show less correlation but are still very interesting to see. P/V is the number of pageviews divided by number of visits. It indicates how long the same visitor stays at the website before they leave or how often they return back during the day.

Blogs with most devoted readers

Here is another interesting list. This is a list of blogs sorted by the pageviews to visitors ratio. The high P/V can indicate two things:

• The blog has good design and convenient navigation which encourages new visitors to stay longer and browse around;
• The blog has devoted readers, the kind who come back several times a day to comment or check on updates.

In either case I encourage you to check out these blogs. Whatever the reason for high P/V may be, they must be doing something right.

Note, not all blogs from the original list are included since some bloggers do not publish their SiteMeter stats.

Conclusion

The correlation numbers above have exceeded my expectations. Alexa and Technorati turn out to be very good indicators of a blog/website popularity if used against a group of blogs/websites from the same category. Technorati seems to be better than Alexa which goes along well with this other study done at SEOmoz.

The last list can also be of some interest, mostly for bloggers. It looks like these blogs are doing a great job with their content and/or design and encourage readers to stick around for longer. Check them out and let me know in the comments what you think about it.