As we already got an idea from looking at the techniques above, data mining is a complex procedure but one that has become essential.
Technology has advanced in data storage, and, what a few years ago was seen as unnecessary and a waste of time, today is already showing itself as an opportunity to reach the public interested in your product.
Identifying patterns that are relevant to a company is a valuable strategy for companies in any industry.
A clothing store may know the profile of the customer who is most interested in its products, and the pattern may reveal that customers with lower average annual salaries are more likely to default on loans.
Consequently, this information can help the marketing manager design a more personalized outreach strategy or a more effective loan arrangement for future customers.
In addition to analyzing information about the company, data mining allows for a view of the competition.
Knowing whether the public approves or not, you will already be optimizing the process of taking advantage of this information so that your business campaigns can reach the public more positively in the future.
In short, we can say that the importance of data mining for companies lies in the fulfillment of three main functions:
Risk Forecasting And Prevention
When data analysis seeks to understand why a particular strategy went wrong and prevent it from happening again.
For example, you have an online store, and you realize that the number of visitors who checked out a product but didn’t make a purchase is greater than the number of people who bought it.
Thus, it is possible to rethink the price or create a new offer, in addition to making more bold decisions regarding the stock of that product.
As we saw earlier, groupings made through customer data allow companies to group users differently.
This effectively reaches the audience with specific and more personalized offers, which are more likely to be successful.
Data mining allows you to identify the stimuli to which each audience responds most often.
Companies can define, for example, which day and time of the week the emails sent are accessed in more significant numbers and the time of most excellent traffic on the website.
It is also possible to outline strategies to increase the reach of social media publications and, consequently, gain more audience. Thus, data mining solves problems that harm the company’s performance.
The Role Of IT In Data Mining
As we have seen, data mining involves statistical and machine learning techniques to define customer behavior parameters.
These techniques can and are very commonly used together, depending on the objectives that the company seeks to achieve. But how do we have the necessary understanding to define how these strategies will be integrated?
For example, IT professionals working in data science end up with up-and-coming career prospects with very high salaries.
Although technology can process many things beyond human capacity, as with data mining, it would be a mistake to say that this entire process can happen independently, without any human intervention.
Main Data Mining Techniques
To relate the information obtained to a single purpose, it is necessary to use some general technique to guide the data mining. Below we list some of the main methods used by companies:
Classification is used to define a type of customer, item or object based on the identification of attributes of a class.
Applying to clients, for example, they can be classified by age, social class, region where they live, and type of job, among others.
To develop a new product, such as a smartphone, a classification can be made that considers the operating system, camera resolution, and display extension, among other aspects.
Classification is also widely used to complement the other techniques we will see below.
Association Or Relationship
The association or relation technique is probably the best-known data mining technique. A simple relationship is established between the analyzed items, seeking to identify patterns.
We can think of the case of e-commerce purchases, for example, where websites usually make a purchase suggestion that complements the item already purchased.
This is because if many customers who buy a cell phone also buy a headset, the headset will also be suggested whenever a new customer purchases a cell phone.
The decision tree is a strategy related to most other techniques, especially classification and prediction.
This is because it is used as part of the selection criteria for an overall structure.
First, the strategy starts with a simple question with few answer options. These answers lead to another, more specific question that helps classify or identify patterns.
From this, it is possible, for example, to define forecasts for each scenario according to each response.