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Utilization of Big Data from Web Analytics to Improve Web Design Decision-making

All businesses that own/control websites, blogs and web and/or mobile applications know the importance of having web analytics tracking code. Nonetheless, the implementation of said systems is more complex than most imagine it to be, leading to major and minor flaws in the instrumentation of the sites and applications as a result.

The explosion of big data and its ubiquity in every industry and field has provided highly sophisticated and specialized tools that businesses can use to track users and their web interactions. In addition, they come with simplified data collection and interpretation, which knowledge can be applicable in different areas including:

  • Filling up spaces in other research data available. Frequently, web application and site user research can hardly be all-encompassing due to resource and time constraints. Therefore, users can utilize the data provided by web analytics to complement that collected from simulations and creation of personas and scenarios to acquire more information about users.
  • Giving an appropriate feedback mechanism within which to weigh design and redesign changes, both real-time and over extended periods, to enable appropriate tweaking to produce the desired effect.

Implementing and using web analytics data occurs through two major steps, discussed below:

Step #1: Investigation and Data Quality adjustment

The main consideration in using web analytics data is its accuracy, since inaccurate data will lead to incorrect inferences. Before you can build your design on the underlying data, it is necessary to conduct thorough review and sanitization. This stage is quite technical, therefore it is recommended that you involve your IT and technical experts to ensure that all relevant issues have been addressed.

Here are some common red signals you might want to look out for:

  1. Traffic over- or under-counting – this can result from incorrectly implementing web analytics tools’ scripts, wrong handling of inbound traffic or incorrect handling HTTP redirects.
  2. Search data in-site – sometimes in-site searches’ query parameters are not recorded because of incompatibility in the query syntaxes of the web analytics tool and the search engine.

A few other things to look into for include conversion rates that are in the extremes, user behavioral information and goat funnels, which can help detect any irregularities in the configuration of the web analytics tool in use.

Step 2: Use the big data to get a position of strength

Ask appropriate questions to formulate hypotheses that the data available can then confirm or disprove. You can create audience segments in order to perform analysis that is more detailed.

Mature web analytics tools not only offer statistical data, they are able to interpret consumer behavior, user characteristics and offer answers to web designers’ and site owners questions relating to the performance of the website. Use web analytics data to perform comprehensive site analysis to identify anomalies and aspects requiring adjustments and improvements.

Use the factual attributes from the data to produce a reconstruction of what happens within various user segments. You can create filters that separate various groups within your target audience in order to better dissect the extent and quality of their interactions with your online profiles. 

Author Bio: Jack Dawson is a well-known web professional who works in In this article he is sharing some tips to avoid bad web design mistakes that can spoil the user-experience.

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Tags: Analytics, Big, Data, Design, Web


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Comment by Bala R Subramanian on August 27, 2015 at 12:47pm

What happens to insights gained through Data Science? Are they utilized wherever they can improve results? Is there a loss of value? How could that loss be quantified?  

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