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AnswerMiner
December 11, 20195 min read

What is data mining? [with examples]

You can hear more and more about data mining. There’s a huge noise about it on both technical and business sides. But what is it exactly? How to use it to your advantage? And how to make sure you do it the right way? In this article, you are going to learn about the data mining process. Also, you will read about actual examples of how data mining is used in practice.

What is data mining?

The purpose of data mining is to find useful information in a dataset and to make smarter business decisions. Data mining helps you find correlations and patterns in your data. On a deeper level, you need technical expertise to mine your data. Don’t worry, even if you just start out and don’t have that, you can start getting useful and actionable information from your data using self-service software tools, like AnswerMiner. With modern data mining technologies and tools, it’s much easier nowadays to understand data and to get insights.

Why is data mining important?

Data mining is essential to make smarter decisions. Decisions that, for example, increase sales or reduces churn or improves a KPI that is important for you. Fundamentally, it helps you understand your business and/or your customers better. It’s a process to get more information about things that are important for you. If you can get more information out of your existing data, you will make more knowledgeable decisions.

What is data mining used for?

Data mining is applicable in every organization where there’s a big or even small amount of data available. Let’s look at some actual examples of how data mining is used in practice.

To prevent churn

Mobile service providers use data mining to reduce churn. The way it works is that the company gathers a lot of different types of data about the customer like billing information, customer interactions, website visits, and other metrics. Then, based on data, the company learns when people tend to churn, and just before that point, it reaches out to customers and gives them offers and incentives that are hard to refuse. In this example, data mining “predicts” what will happen, and thus you can make smart decisions in your business.

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Prevent employee resignation

Data mining also can be used in HR departments to prevent employee resignation. If you try AnswerMiner and have a look at the employee satisfaction sample data, you can learn interesting information about your employees. With mining data, you can learn why and when people leave the company. Analyzing the sample data, it shows that people quit if they have low satisfaction level, work more, and didn’t get promoted within the last five years. There are probably some people you want to leave because they are under-performing. But it’s a problem when high-achieving people also go. You want to keep them.

Upsells and cross-sells

In the e-commerce world, you can find another customer-centric data mining example. In most popular e-commerce sites, you can always see a section where it says, “You might also like these…” or “These are products you may like as well…” or on Amazon, “People who viewed that product, also liked this.” This is called product recommendation. This type of usage of data mining is really practical. It has a high chance of increasing sales because the company can customize the user experience by offering relevant up-sells and cross-sells.

Customer preferences

You might have heard the story of how Target exposed a woman’s pregnancy. In short, Target was tracking what its customers had been buying. Then, based on this data, sent out coupons and offers in the mail. Andrew Pole, a business intelligence director, identified 25 products that, when purchased together, indicate a woman is probably pregnant. Then, obviously, Target would send coupons and offers that are relevant for a pregnant woman. The gist of this story is that you can track your customer’s historical buying decisions, then based on this, you can predict what they will buy.

How does data mining work?

Now that we covered some examples of what data mining is let’s see how you can implement a data mining system on your own. If you just start out, it’s essential to focus on simple and quickly achievable goals so you can see the benefits of data mining early on. In the long-term, though, data mining is a complex process, both technical people and decision-makers need to be involved, and it takes a lot of time the implement.

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Understand business goals

First, you should establish what your goals are with data mining. What part of the business you want to focus on and what KPIs you want to improve. Your plan should include what’s your current stage and what would be the desired outcome.

Understand what data you have

Next, you should be clear about what data you can access and use. What data sources you have access to and what pipelines need to. Identify places where you can collect data. In this phase, you probably need to create pipelines and integrations so you can move all of your data into one place.

Prepare data

Data preparation is a time-consuming activity in the data mining process. That’s why it’s important to find the correct technologies and tools you will use. Choosing the right tool can save you tons of time and effort. The goal of this stage is to make sure all of your data in the database is relevant, standardized, normalized, cleaned, and appropriately formatted.

Model

First, include all the columns that you might need and create a relationship diagram. Use mathematical models to determine data patterns. Two of the most used algorithms are classification and regression. You can learn more about them and other algorithms here.

data_mining_algorithms

Evaluation and reports

Finally, you need to evaluate that the results are aligned with your initially defined business goals and that the information is usable that you get from the data mining process. If you decide that the results are satisfying and it really gives insight for better decision-making, then you should set up an automatic way to create reports, so you can get these insights on a recurring basis. Also, you might learn something new that makes you change the objectives of your data mining efforts. Getting more information about your business is an iterative process.

Data visualization made easy

Data visualization is a big part of data mining. Both in data exploration and reporting. If you can create relevant and straightforward data visualizations, that means you can easily understand your data and quickly make smarter decisions. The problem is, without the proper tool, it’s a headache to create visualizations. It’s either time-consuming or technically challenging.

While analyzing data, you can feel lost at the beginning, which is fine. In this article, we wanted to collect the main aspects of data mining and giving you help to starting. So just jump in and start exploring your data. Day by day, you will be better and see the big picture.

Connect your data and start the exploration

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