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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:
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:
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 BigDropInc.com. In this article he is sharing some tips to avoid bad web design mistakes that can spoil the user-experience.