4 Ways Data Analytics Can Help Your Business - Itekinsight

Statistical analysis is the evaluation of raw statistical data to extract useful information that can help a company make better decisions. Itekinsight provides the best Business Analyst Data Analyst Training and Job Placement in US. In a sense, it is a procedure that allows you to connect the dots between seemingly disparate sets of data. Like its cousin, big data, it has recently become an absolute buzzword, especially in the advertising world.

Data analysis and customer behavior

Small agencies may agree that their small size, which allows them to build relationships with clients, is very up close and personal and cannot be replicated in larger firms, which can be an aggressive differentiator. However, we are beginning to see that large groups are able to reflect some of these traits in their relationships with clients, using record analysis strategies to create an artificial sense of intimacy and personalization. In fact, most real evaluations focus on buyer behavior. A better example of good use of data was when Gilt adjusted the frequency of e-mails to its members primarily according to their age and membership class.

Understand where to draw the line

Just because you can better target customers through information analysis doesn't mean you should always do so. Certain moral, logical, or reputational issues may also cause you to reconsider your actions based on the documents you find. For example, the American store Gilt Groupe may have overreacted to its statistical evaluation system by sending members a "We have your size" email.  A better example of good use of data was when Gilt adjusted the frequency of e-mails to its members primarily according to their age and membership class.

Customer complaints - a gold mine of useful information.

You've probably heard that customer feedback provides useful data that is worth its weight in gold. Data analytics offers a way to explore customer sentiment by methodically sorting and analyzing the content and factors that lead to customer feedback, whether positive or negative. A better example of good use of data was when Gilt adjusted the frequency of e-mails to its members primarily according to their age and membership class. The goal is to discover the root causes of your customers' recurring problems and find solutions to solve them. However, one of the problems is the format of the record, which, by definition, is not organized as numbers in neat rows and columns.

Waste in - waste out

Most of the resources invested in data analysis are often recognized by the purity of the statistics themselves. You may have heard of the "waste in, waste out" principle, which refers to the correlation between the quality of the raw data and the beauty of the resulting analytical insights. In other words, good facilities and high-quality analysts will struggle to produce anything meaningful if the fabric they are working with is not collected in a methodical and consistent manner. First of all, the data must be clean. A better example of good use of data was when Gilt adjusted the frequency of e-mails to its members primarily according to their age and membership class.