Every data reaches the reviewers in raw form. The analysts need to extract meaningful information from raw data and save it in a safe yet easily extractable manner. This process of extracting usable data from a bigger, raw data source is called data mining.
This process has become crucial for market research experts and other customer-centric marketers to develop better utilities that can contribute to customer experience. Since this process’s overall impact is to gain insight or knowledge, the users also call it Knowledge Discovery in Data or KDD.
Top data mining techniques
Without extracting information in a knowledge-oriented way, all data floating around proves to be nothing more than junk. To extract the best application from the data mining process, the following implementation techniques are available to consider:
- Classification technique: Creating folders according to their use or no use and saving data in those folders is an example of a classification technique of data mining. While applying this technique, data miners use algorithms to classify and sort data according to utility parameters. Making different folders in the inbox to send spammers’ emails in a separate folder is a well-known use of the data mining classification technique.
- Regression analysis: This data mining technique bases various predictions and forecasting procedures. It involves the identification and analysis of the relationship among variables. If one value changes, the dependent’s values increases or decreases accordingly.
- Anomaly Detection: In this data mining technique, the value different from others is picked based on the deviation from normal behavior. Sometimes, the anomaly may require immediate attention. It is quite useful in fraud detection, domain intrusions, etc.
- Clustering analysis: Various data features are clustered together in a group where the associations among them are the closest. The cluster will be formed only of those objects, people, or features having similar characteristics. It is quite helpful in customer profiling.
Popular types of data mining
Businesses use data mining to extract the meaning from haphazardly-lying, ambiguously big data. They can use this information for product improvement, implementation of marketing techniques, better customer experience, and enhancement of applications or websites’ features. You can find businesses making use of various data mining types depending upon the source of data, such as:
- Pictorial data mining: It allows businesses to employ graphs, pie-charts, social media posts’ images, etc. These make it easy to test the responses and enhance pictorial presentations in a better way.
- Text mining: Researchers can categorize, dissect and sort text messages, comments, stories on social media handles and find their role in brand building.
- Social media mining: All social media data comprising posts, pictures, comments, metadata, videos, etc., is mined to find the patterns in user behavior. It can be employed to understand how social media can help build brands and develop other online consumables.
- Web mining: Web forms perhaps the biggest data pool, and mining its data is not easy. Web mining is most useful in users’ web behavior analysis.
Data mining is an advanced research tool giving marketers the best solution for having meaningful insights from the piles of data. It is helpful in all niches and allows users to derive the best solutions for customer profiling, product catalog making, fault and fraud detection, etc.