What Is Data Mining and How Does It Influence Decision Making?

>>What Is Data Mining and How Does It Influence Decision Making?

What Is Data Mining and How Does It Influence Decision Making?

Data mining is used to extract relevant data from a larger set of raw data. The purpose is to look for patterns, correlations and anomalies that can predict future outcomes. By understanding specific groups of data, businesses can increase revenue, improve customer relationships, reduce risk and much more.

Let’s talk more about the importance of data mining and how it can influence the decisions you make for your business or organization.

Why Data Mining is Important

Big data has been a buzzword for years. It refers to a large volume of data, both structured and unstructured. As wonderful as it is to have large amounts of data, you must know how to make sense of it. Otherwise, this information is not valuable.

To much surprise, 90 percent of data is considered unstructured, meaning that it’s locked away in various data stores. By pulling out the information you need, you can cut through the noise and access the data you need to make smart, strategic decisions that benefit your business or organization. And, you don’t have to be confused or distracted by data that has no bearing on your future successes.

Approaches to Data Mining

There are numerous approaches to data mining. Some are aimed at finding similarities within groups of data. These similarities can then be used to determine the reasons behind a product’s success or failure. Other approaches are used to classify events in the future, such as predicting a person’s likelihood of repaying their loan.

Here are a few examples of data mining approaches:  

  • Cluster detection. Cluster detection looks for patterns within large data sets. In other words, it takes information and categorizes it based on similarities. For example, a higher institution might gather student test scores and organize them based on demographics.
  • Anomaly detection. Anomaly detection looks for anomalies in data, or things that don’t fit in. Anomaly detection is often used to track weather patterns or issues with forensic computing.
  • Affinity grouping. With affinity grouping, you can group together people with common interests or similar goals. For instance, people who buy Product A usually buy Product B, too.
  • Regression. Regression is a technique used to predict future outcomes. It’s highly valuable in the marketing industry as marketers can predict future engagement and customer retention.

Benefits of Data Mining

Data mining helps businesses and organizations address some of their biggest challenges. It can be used in all industries, including retail, communications, education, banking, insurance and manufacturing. The key benefits of data mining include:

  • Better understand your data
  • Make smarter, more informed decisions
  • Offer more competitive products
  • Optimize marketing campaigns
  • Enhance customer relationships
  • Predict future sales
  • Reduce risk

Using Data Mining to Influence Decisions

In order to reduce risk, more businesses and organizations rely on data before making decisions. While it can be tempting to go off assumptions and gut feelings, this is rarely effective. In fact, it can cause businesses to put their resources into the wrong areas and lose in the long run.

To ensure that your business is always making data-driven decisions, collecting the right data and analyzing it appropriately is vital. Looking specifically at the sales and marketing industry, here are some of the ways that data mining can influence the decisions made.

    • Customer demographics. Use data mining to make sense of your audience. Who are they? What interests do they have? When are they most likely to be online? What types of emotions drive them to buy?
    • Customer journey. What is the typical buyer journey like? Data mining can help you better understand this path and what influences customers to convert.
  • Inventory. The more you learn about your customers’ shopping habits, the better you can plan for inventory. This increases efficiency and sales margins. What months need more inventory? What months can you scale back?
  • Marketing campaigns. What types of campaigns do your customers respond best to? What promotions and prices do they consider competitive? Extracting the right information allows you to create more strategic, optimized campaigns.

Conclusion

As exciting as big data might sound, much of it is information that we can’t use. This is why data mining is so important – it allows businesses, organizations, marketers and others to focus on relevant information. Because this information is easier to understand, anomalies, patterns and correlations can be identified, predicting future outcomes. By learning how and when to use data mining, you can improve customer relationships, increase sales and reduce risks.

By | 2018-07-05T23:30:59+00:00 July 5th, 2018|Categories: Featured|Tags: , , |0 Comments

About the Author:

Tayllor Gomez-Spillane is the Senior SEO Strategist at SEMGeeks and a contributor to the AMA NJ blog.

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