Offers profitable forecasts

In this context, data mining can Offers profitable  help you identify consumer habits and develop new product lines, create a more attractive product mix… In short, take your business to the next level.

Do you know the 5 new consumer habits in the face of coronavirus ?

Not all companies have large budgets for collecting, storing and processing data. While data mining requires a qualified professional to analyze the information obtained and transform it into useful knowledge, the process is cheaper and more convenient than in the past.

How many times have you relied on your intuition to make an important decision at work? While feelings can be a good complement to rationality, it is important to look at the data to follow a path that is appropriate to reality.

Encourages informed decision making

 

Here’s an example. Imagine your support agents are seeing an increase in ticket volume, but you don’t know why. Your intuition tells you that customers didn’t like the newly released product and must be complaining about it.

However, after applying data list to data mining techniques, you find out that they are actually not happy with the first response time of the agents. Therefore, they open support requests on various communication channels with your company. Do you see how intuition can deceive you?

Combining the above elements, it is easy to see that one of the great advantages of data mining is its ability to promote continuous process improvement.

Promotes continuous improvement of processes Offers profitable

 

After all, this process reveals to you which problems you frequently face, which needs have not been met, and which estimate time and create detailed project schedule opportunities for change are before your eyes.

Data can change the way you see the world. In this Ted Talk, journalist David McCandless explains how to turn numbers into something beautiful and interesting for any audience.

What are the challenges america email of data mining?
Data mining also presents challenges for companies interested in implementing it. Redundant data, human error, security issues and lack of specialized professionals are the main disadvantages.

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