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Web Analytical Essay

738 Words3 Pages

What is the point of investing in the best web analytics tools money can buy, if you do not have the resources to analyze the data and make sense of the data? The tools will be useless. The goal of the 10/90 rule is to bring the maximum value of your company’s implementations of web analytic tools. The 10/90 rule is for every $10 you spend on web analytics technology, you should spend $90 on people to analyze the data. For example, if a company spends $75,000 on any analytical tool, that company should be spending $675,000 on a team of analysts to analyze and gain valuable understanding of the data. Analysts responsibilities include reporting, analyzing acquisition strategies/optimization, understanding on-site customer experience, staying …show more content…

The first one being websites are vastly complex and even though the tools capture all the data, they do not tell you what the data actually means. The next one is that web analytics tools just spews out data. Without people to interpret the important data, the analytical tools are worthless. Also, since companies deal with both qualitative and quantitative, results multivariate testing and competitive intelligence data, the data might not relate to anything else which is problematic. Lastly, one of the best ways to convert data into insights is to keep up with the “tribal knowledge” in the company. Tribal knowledge is unwritten information that is not usually known by other employees in the company. This information may be key to performance quality but it could be irrelevant to the company’s performance. The only way to solve these four issues is by investing in people. Companies having so much data but do not have the resources to interpret this data. Even with free tools such as Google analytics, you still need to invest in people because you will need to pay a consultant fee to ensure the tool is implemented correctly. As a result, the 10/90 rule will be applied in getting the right employees to make sense of the big

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