The choices are abundant when a company decides to investigate which Web Analytics tool to invest in, from simple reporting tools to advanced multi-layered analytical systems. The tendency to believe that the best systems cost vast amounts of money and offer the maximum number of features leads to bloated and over-budget implementations that ultimately hurt their adaption and utilization. Determining the appropriate feature set and budget are important to choosing the correct Web Analytics tool to fulfill the needs of the business.
The “10/90 Rule” from Kaushik is a guideline to avoid these pitfalls (2010, pg16). This term comes from the ratio of $10 spent on the tool itself for every $90 spent on the “intelligence resources” who add value to it (2010, pg16). This ratio was determined to bring the fullest potential from an investment to solve the four basic problems with Analytical Tools: Analytical Tools will provide data, not solutions; This data will be regurgitated in large quantities that will need to be managed; Qualitative and quantitative data must be accounted for; and finally, the most powerful tools to turn information into insights are the people. According to Kaushik three basic determinations must be before attempting to implement a new tool – do they need Analytics, what are their strengths and weaknesses, and what outcomes …show more content…
The company culture and the decision-making structure may lend itself to simply require reports on existing data; therefore, spending more resources on a robust analytical tool with superfluous features will not be a best fit for the company needs. Providing too many features that will not be utilized will lead to attempts to justify the investment. Employees may feel they have to use the features taking time, effort, and focus from the company’s core competencies, leading to a higher risk of rejection and resentment
The “10/90 Rule” from Kaushik is a guideline to avoid these pitfalls (2010, pg16). This term comes from the ratio of $10 spent on the tool itself for every $90 spent on the “intelligence resources” who add value to it (2010, pg16). This ratio was determined to bring the fullest potential from an investment to solve the four basic problems with Analytical Tools: Analytical Tools will provide data, not solutions; This data will be regurgitated in large quantities that will need to be managed; Qualitative and quantitative data must be accounted for; and finally, the most powerful tools to turn information into insights are the people. According to Kaushik three basic determinations must be before attempting to implement a new tool – do they need Analytics, what are their strengths and weaknesses, and what outcomes …show more content…
The company culture and the decision-making structure may lend itself to simply require reports on existing data; therefore, spending more resources on a robust analytical tool with superfluous features will not be a best fit for the company needs. Providing too many features that will not be utilized will lead to attempts to justify the investment. Employees may feel they have to use the features taking time, effort, and focus from the company’s core competencies, leading to a higher risk of rejection and resentment