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to better understand why people buy or don't buy what you're selling.
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how visitors use the site and why they stay or leave. The key concept to
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analytics is *learning* about your users so you can improve your web
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application to better suit their needs.
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It's easy to get overwhelmed at both the number of analytics services and
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the numerous types of data points collected. Focus on just a handful of
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metrics when you're just starting to run a Python web application. As your
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application scales and you understand more about your users then add
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additional analytics services to gain further insight into their behavior
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with heatmaps and user actions funnels. This sarcastic but accurate post
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on the
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metrics when you're just starting out. As your application scales and you
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understand more about your users then add additional analytics services
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to gain further insight into their behavior with advanced visualizations such
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as heatmaps and action funnels. The
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[seven stages of startup analytics grief](http://spenczar.com/posts/2013/Sep/07/seven-stages-analytics-grief/)
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provides context for how to begin and grow over time.
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post is an amusing read and provides context for how to begin and then grow
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tracked metrics over time.
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## User funnels
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If your application is
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selling a product or service you can ultimately build a
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[user funnel](http://moz.com/blog/building-your-marketing-funnel-with-google-analytics) (often called "sales funnel" prior to a user becoming a customer)
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to better understand why people buy or don't buy what you're selling. With
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a funnel you can visualize drop-off points where visitors leave your
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application before taking some action, such as purchasing your service.
to better understand why people buy or don't buy what you're selling.</p>
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how visitors use the site and why they stay or leave. The key concept to
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analytics is <em>learning</em> about your users so you can improve your web
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application to better suit their needs. </p>
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<p>It's easy to get overwhelmed at both the number of analytics services and
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the numerous types of data points collected. Focus on just a handful of
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metrics when you're just starting to run a Python web application. As your
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application scales and you understand more about your users then add
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additional analytics services to gain further insight into their behavior
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with heatmaps and user actions funnels. This sarcastic but accurate post
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on the
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metrics when you're just starting out. As your application scales and you
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understand more about your users then add additional analytics services
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to gain further insight into their behavior with advanced visualizations such
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as heatmaps and action funnels. The
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<ahref="http://spenczar.com/posts/2013/Sep/07/seven-stages-analytics-grief/">seven stages of startup analytics grief</a>
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provides context for how to begin and grow over time.</p>
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post is an amusing read and provides context for how to begin and then grow
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tracked metrics over time.</p>
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<h2>User funnels</h2>
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<p>If your application is
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selling a product or service you can ultimately build a
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<ahref="http://moz.com/blog/building-your-marketing-funnel-with-google-analytics">user funnel</a> (often called "sales funnel" prior to a user becoming a customer)
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to better understand why people buy or don't buy what you're selling. With
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a funnel you can visualize drop-off points where visitors leave your
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application before taking some action, such as purchasing your service.</p>
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<h2>Web Analytics Third Party Services</h2>
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<p><ahref="http://www.google.com/analytics/">Google Analytics</a> is a widely used
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