Will Critchlow - Using and abusing data

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Slide 30

grep www.distilled.net-access_log -e 'Googlebot' | grep -e '404‘ grep www.distilled.net-access_log -e ' 404 ' | grep -e 'store'

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Using and abusing data Setting us apart from other marketers Will Critchlow, 2011

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This presentation is about skills and things to try… Understanding data is all that separates us from the savages

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Some skills I firmly believe technical skills make you a better SEO

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Gathering data: there are loads of places it could come from, but you should be adept at accessing it. Check out http://www.infochimps.com/

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There are loads of great APIs: http://www.seomoz.org/blog/api-and-dataset-cheatsheet-building-quick-dirty-tools

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Sometimes you need to go get your own: with varying levels of difficulty. Check out http://www.mozenda.com/, http://scraperwiki.com/

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Rapid prototyping FTW: I’m talking *really* Heath Robinson

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On statistics The modern world runs on math. Learn just a little bit

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We are good at spotting (and extrapolating) trends Go, go, go

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But randomness often looks like a trend Uh oh

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And sometimes, we are just wrong PANIC!!1

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A 1% mis-diagnosis rate can result in more errors than correct diagnoses

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Pie charts are often hard to be read and can easily be made misleading http://dis.tl/m6sZLO

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Watch out for charts that don’t go to zero on the y-axis http://dis.tl/iH1qgs

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The same data presented in a less misleading way

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Action: read “How to lie with statistics” http://www.amazon.com/How-Lie-Statistics-Darrell-Huff/dp/0393310728 Old, but great

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Don’t blindly mine data for patterns (you’ll definitely find something). Have a hypothesis Test on different data to that used to form the hypothesis Don’t forget that a 95% confidence interval is wrong 1 in 20 times Common statistical misconceptions / mistakes

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Visual =Powerful

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Geckoboard: build a culture of data - http://www.geckoboard.com

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Visible data and clear targets work: we had a modest internal target to hit 2,000 followers of @distilled by our #linklove conference Target set Target ended Source: twittercounter.com

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One of our clients built a tool to visualize website changes over time: http://www.reevoo.com / https://github.com/georgebrock/timelapse

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Public = powerful

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Coming soon: public post analytics on SEOmoz

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Back to getting the data

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=importxml("http://www.google.com/search?gl=us&q="&A2, "//h3[@class='r']/a/@href") We’ve written about the power of Gdocs for rapid prototyping a bunch of times – see for example: http://dis.tl/kZsv9y and http://dis.tl/jqk05J

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Another xpath resource: http://developer.yahoo.com/yql/console/

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Command line

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Linux: native command lines make a lot of this easier. The latest Ubuntu and VirtualBox are a piece of cake to install and get using

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VirtualBox: take snapshots really frequently

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Unix principle: lots of small tools, piped together – great for ad-hoc or to create specifications grep * -r -e ‘UA-XXXXXX’ | sed -e ‘s/UA-\([0-9]*\)/UA-XXXXXX/g’

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Inspiration: via @tomcritchlow - http://www.neilkodner.com/

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Real programming

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apt-get install python-virtualenv virtualenv --no-site-packages <path> source <path>/bin/activate pip install gdata http://code.google.com/p/gdata-python-client/ http://code.google.com/apis/analytics/docs/gdata/gdataExplorer.html

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https://gist.github.com/967503 (~/Downloads/googleanalytics.py)

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Training: I really like the peepcode videos for learning technical stuff. Recommended: command line, git, vim. http://peepcode.com/

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STOP

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If you start catching the bug, go learn a framework. I like django (https://docs.djangoproject.com/en/1.3/intro/tutorial01/)

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Stackoverflow is your friend – everything you can’t get answers to here is a copy and paste error guaranteed(*)

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Time series

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You come across data that looks like this all the time Day Date Visits 1 Monday 05/04/2010 877 2 Tuesday 06/04/2010 1087 3 Wednesday 07/04/2010 1018 4 Thursday 08/04/2010 1039 5 Friday 09/04/2010 917 6 Saturday 10/04/2010 670 7 Sunday 11/04/2010 746 8 Monday 12/04/2010 1165 9 Tuesday 13/04/2010 1192 10 Wednesday 14/04/2010 1053 11 Thursday 15/04/2010 1022 12 Friday 16/04/2010 947

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Your first action: eyeball the data

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Your action: Fire up R - http://www.r-project.org/ > pg = read.csv("<path_to_file.csv>") > tspg=ts(pg[,3],start=1,freq=7) > plot(stl(tspg,s.window="periodic"))

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Your action: decompose time series into its constituent parts

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The “trend” is useful: this removes seasonality and outliers

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The “remainder” is the outliers: might correspond to activity

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Videos are now available of our #linklove conference in London and New Orleans: www.distilled.net/store

Summary: As online marketers, understanding data is one of the things that sets us apart. In order to use data as effectively as possible, we need to understand what tools are available and how we can use those tools. In this webinar, Will is going to share his tips for dealing with data quickly and efficiently.

Tags: data

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