EXAMINING CUSTOMER CENTRIC DATA MINING
OBJECTIVE OF THIS STUDY
At the end of this lesson, readers should be able to:
- Define Data Mining
- State its benefits
- Discuss its components.
Customer-centric Data Mining is a collection of techniques and methods that enable the business to engage and retain their share of the market. The goal of data mining is to provide the ability to convert high-volume data into high volume information
Data Mining helps in discovering enterprise knowledge from historical data and combines historic enterprise knowledge with current conditions and goals in order to reside uncertainty about enterprise outcome.
COMPONENTS OF DATA MINING
- Discovery: In this, the organisation examines their data and try to discover a pattern of the transaction. For example desolate as a telecommunication organization can discover that a large number of their youths segments likely to use their airtimes to browse the internet, which may inform the way they have come up with easy cliq platform.
- Exploration: Under exploration, the organisation tries to work on the pattern that they have discovered. This will now use this pattern to come up with a solution to a problem that customers in the organisation have been facing while using products and services from the organisation. This can also be used in segmenting and profiling the organisation’s customers models which are solutions to some of the problems discovered can be a solution to a single problem or array of problems confronting the organisation’s customers.
Data Mining you should provides a formal and well-organized way of discovering a pattern in data. Let’s say for example you have discovered raw gold in a play you now want to dig up. That is exactly what data mining does. It tries to find out useful information about the customers. Which can help the organisation to minor their pattern of interaction and come up with products and services that will satisfy their needs? Data Mining applications serve information and correlated customers data in a comprehensive manner. Data mining tools you should know can identify the relationship that exists between historical data.
In examining customer centric data mining, you should know that Data Mining applications have tools and techniques that will allow organisations to get the best out of their customer information. They will not just store the information but they will be able to make an informed decision through active use of data.
Data Mining help organisation to maximize their return on Investment (ROI). Through it the organisation will be able to know the potential of their customer base. They will be able to know which calibre of their customers are likely to make a repeated purchase and what they need to do in order to sustain their profitable customers. Data Mining has the potential of extracting profiles of customers and identify business opportunities that can be developed through them.
In examining customer centric data mining, you should know that Data Mining help in answering two basic questions. These are questions that every CCE has to ask themselves. These questions include:
- Which of my high-profit customers are most likely to leave? The organisation has to be proactive in order to retain them.
- Which of my low-profit customers are least likely to leave? Raise their price and make them more profitable.
Ever Data Mining application user must be able to maximize the amount of useful information that they are able to extract from their passive customer information. They should also be able to minimize the loss of productivity that results from the information retrieval effort, prohibitive access, communications difficulties, or retrieval of meaningless, incorrect or corrupted data among others.
In order to satisfy the curiosity of Analysts while examining customer centric data mining, you should know that there is a need for the information to be applicable to different groups of people, Data Mining must consist of different associations of data, it must be able to works with multiple channels and networks, and it must be able to analyse, interpret and display information in a variety of meaningful ways.
Combining discovery-driven analysis with the more pervasive assumption – driven methods will ultimately produce the most comprehensive analysis for the greatest business benefit.
One thing you should know is that while examining customer centric Data Mining, there is a high tendency to incur an unnecessary cost if the application is faulty. Once the organisation is able to get rid of unnecessary data, they will be able to put cost under control.
In examining customer centric data mining, you should always focus on business forecasting. One other thing is that business forecasting is very essential if an organisation wants to get the best form Data Mining. They must be able to use the available information about customers at their disposal to predict what sales and responses is going to be like in any given year and continue to crosscheck their data in order to validate their predictions. The forecasting we are talking about here goes beyond seeing what cash flow is going to be like but it involves the organisation predicting what make is going to like, judging from their available historical data.
About all, the main essence of Data Mining is the ability of the organisation to estimate the lifetime value of their customers, which will allow the organisation to know how profitable the organisation is going to be in the future.
SUMMARY ON EXAMINING CUSTOMER CENTRIC DATA MINING
At the end of this lesson, we have discovered that:
- Data Mining is the collection of methods and techniques that allows the organisation to retain their share of the market.
- We have two components of Data Mining which are discovery and exploration.
- Data Mining allow organisation to get the best out of heir customer information
- It also help them to maximize heir ROI
- Data Mining helps organisation’s to know how profitable they will be in the future.
- The main purpose of Data Mining is the ability of the organisation to maximize the lifetime value of their customers.
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