Performing web analytics on Snowplow data using Tableau - a video demo

24 October 2012  •  Yali Sassoon

People who see Snowplow for the first time often ask us to "show Snowplow in action". It is one thing to tell someone that having access to their customer- and event-level data will open up whole new analysis possibilities, but it is another thing to demonstrate those possibilities.

Demonstrating Snowplow is tricky because currently, Snowplow only gives you access to data: we have no snazzy front-end UI to show off. The good news is that there are a lot of smart people developing fast, powerful and easy-to-use reporting tools. And because Snowplow gives you access to underlying customer- and event-level data, it is easy to analyse Snowplow data in nearly all of these tools. One such tool is Tableau - we like Tableau as it is fast and intuitive, making it easy for us to perform train-of-thought analyses on Snowplow data. (We will explain more on how to connect Tableau to Snowplow data in a future blog post.)

In the following series of videos, we start to show how Snowplow lets you use Tableau for exploring your web analytics data. In the first video, we introduce Tableau and talk through the Tableau worksheet created with Snowplow data for an online retailer:

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In the second video, we show how to perform an analysis of the drivers of growth of traffic on a website. The video serves to highlight how effective Tableau is at performing train-of-thought analysis:

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In the third video, we show how to perform an analysis comparing the relative performance of different products in an online retailer's catalogue. This is an example of catalogue analytics, a very important branch of analytics - where we analyse how different products on a retailer's site perform relative to one another, or how different media items (e.g. articles / videos) on a media site perform. Surprisingly, catalogue analytics is not supported by traditional web analytics packages like Google Analytics:

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In the fourth video, we analyse improvements in conversion rates over time for the retailer. This is a core measure to track in order to understand how improvements to the website and marketing strategy drive increased conversion rates. Again, this is something not supported by Google Analytics out of the box. We show how easy it is with Snowplow and Tableau to identify trends in conversion rates over time:

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In the fifth video, we show how to visualise patterns of individual user visits over time. This is an interesting starting point to begin to unpick the patterns that make up successful user engagement:

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In the sixth video, we show how to visualise the range of product pages visited by each user. This can help us to understand how successful the retailer is at driving users interested in one product to consider buying other products (cross-selling), and onwards to developing recommendation algorithms (users who liked this product also liked this product):

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In the final video in the series, we perform an example cohort analysis, with a view to understanding how 'sticky' the online retailer site is, and how its stickiness has improved over time. In this example, we use stickiness to refer to how good the website is at driving repeat visits:

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