Move Web Analytics Data Out Of Silo

Web Analytics tools are great for providing a good view of one channel i.e. your website (ok, maybe slightly more than one channel e.g. some email, some social media, some offline). They worked great in silo for first few years of the internet because the only way for customers to interact with your brand online was on your site and websites were not an integral part of the business. Nowadays the story is different, customers interact with your brand in so many way, your website is just one small part of the whole “web” ecosystem and “web” is just one part of the whole “customer” experience and buying cycle ecosystem. Customer’s don’t think and operate in one channel i.e. your website. However, many “web analytics” tools do not even provide you full view of a customer journey and interactions online let alone the offline journey.

To understand today’s customer and performance of your marketing efforts, web analytics data has to move out of it’s silo and needs to be integrated with other data sources.

Many of you might be already be using 3rd party solutions to pull data from few sources into a dash boarding tool. That is a great start but it still does not provide you a complete view of customer journeys. For example, just because you have social media mentions on the same dashboard as your on-site analytics data does not tell you if those mentions are from your customers or somebody, who is neither a customer nor is your target customer, just blabbering in social media. But I will give you credit for thinking outside the Web Analytics tool.

To understand complete customer journey (i.e. 360 degree view of customer) and to conduct analysis that take you from marginal improvements in conversions to something that has a huge impact on the business you need much more detailed data than a web analytics report or a dash boarding tool can provide. First, you need to collect individual data for each customer in various channels then warehouse the data in one place where you join various sources via common key such as customer id, email address, phone number etc. Only then you can create and run complex cross channel queries to understand customer behavior and campaign performance.

Many mature organization are already doing it or are working on it. If you are not then it is about time to start thinking about if you want to stay competitive.

Don’t think that just because you are using Google Analytics you can’t have this level of data because you can. You just have to push yourself and start thinking outside what your web analytics tool can provide.

How Can You Do it

Web Analytics tools already anticipated this needs so they have built a way for you to get the data out easily. You can use either of the two methods listed below to get the required data

  1. APIs – Many tools like Google Analytics provide data via APIs. Use those APIs to pull appropriate data into your datamart/datawarehouse.
  2. Data Feeds – Many tools provide data in a flat file that you can use to populate your datamart.

Here are few things to keep in mind before you start putting this data in your datamart

  1. Make sure your tools are configured properly to collect the data in the right format and
  2. Your data transformation process should be able to understand the difference between various custom variables that you have used in the data collection
  3. Various data sources also need proper identifiers (keys) to match them together.

This is not going to be an easy project but this is a critical step in using your web analytics data to stay competitive.

There are few 3rd companies who are already providing tools and service to help you with it. You should also check out Gary Angel’s Blog. Gary has worked and written extensively on this topic.
If you have any question, I will be happy to chat. Email me.



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  1. Hi Anil, great blog and right on the money as usual. We are told regularly by customers and prospects that the more traditional Web Analytics solutions are not actually designed to provide the detailed data necesary to conduct cross channel analysis of an individual customer in a warehouse. The up-front tagging headache is still there, regardless of the introduction of TMSs as you still need to figure out what you need to know before you start and you then need to craft the tag strategy to do the job, which of course is a never ending cycle. Compound this considerable workload with then trying to decipher a nightly flat-file data feed, which requires extensive custom ETL with a view to populating a physical data model in Teradata, Oracle, IBM etc., and you have a recurring and not insignificant job on your hands. Gary has indeed done a great job of helping Companies to understand the task at hand and I would recommend a specific white paper he wrote earlier this year with Celebrus, titled “the future of digital measurement and personalization” ( which tackles this rather tricky subject. If you are unfamiliar with our Company Anil, Celebrus is specifically designed for the task outlined above. We deliver via partners such as SAS, Teradata and Oracle a tag free, real-time data source which delivers structured data via pre-determined ETL, directly into a warehouse or alternmatively, un-structured data into Big Data environments such as Aster Data/Map Reduce or Hadoop. We are always looking for an opportunity to highlight the workload involved with systems that are not in fact designed for this job and trust you will let me knwo if you have any questions re. our technology.

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