Data Mining Techniques for Customer Relationship Management

Relationship management has not been left out of the disruption taking place across industries. The adoption of advanced technology such as data warehousing and data mining technologies has become a big part of the disruption. Firms and corporate organizations are taking advantage of these technologies to develop a competitive edge in the marketplace. They achieve this by gaining expertise in extracting hidden predictive information from a large dataset through an advance statistical & mathematical algorithm.

Data mining can aid with the identification of valuable customer insights, predict future behaviours of customers, and help make data-driven decisions. Data mining has improved over the years and can be described as matured enough to deliver unprecedented benefits to organizations. Companies can achieve automated, future-oriented analyses, which can help them gain competitive advantage. A truly automated Customer Relationship Management is now possible to achieve because, with Data Mining, business questions that were time-consuming in the past are now very much achievable.

There are different techniques and approaches to data mining. However, an organization should utilize the method and strategy that will help in achieving its overall objective of establishing a productive and successful customer relationship management.

An Overview of Data Mining
Data mining is defined as a sophisticated data search capability algorithm to discover patterns in data. It extracts information that is buried in the data warehouse; complementing other analysis techniques like a spreadsheet analysis, statistics, and primary data access. In other words, data mining is a way to put meaning in data.

Relationships and patterns that are hidden in data, are discovered by means of data mining and this is called knowledge discovery. Data mining does not find patterns and knowledge that can be automatically trusted without verification. It helps business analyst to generate hypotheses, but it does not really validate the hypothesis.

Applications of Data Mining
Data mining tool takes data and then constructs a reality which in turn forms a model (describing the patterns and relationship). Data mining activities fall into three categories: See diagram below:

1. Discovery: which is the process of querying the database and finding hidden patterns without a predetermined idea or hypothesis about what the trends are.

2. Predictive Modeling: Is simply the process of taking the model discovered from the database and using them to make a future prediction.

3. Forensic Analysis: is the process of applying the patterns that have been extracted and using it to find unusual data elements.

In the retail business, the use of point-of-sale (POS) systems, retailers can keep detailed records of all shopping transactions. This will help them understand their customer segments.

The followings are some retail applications, they include:

Performing basket analysis — this application reveals which of the item’s customers tend to purchase regularly. This knowledge can improve stocking, strategies on how to stock your store, and promotions.

Sales forecasting — Here, questions like if a customer purchases an item today when are they likely to buy another similar item or the complementary? the third point is

Database marketing — Retailers can use this to develop or categorize their customers based on their patronage. This information will be vital in preparing a cost-effective promotion.

Lastly, in the banking industry, banks can utilize the knowledge they get with data mining for card marketing, where card issuers and acquirers can improve profitability, targeted product development, and customer pricing.

Card issuers can also take advantage of data mining technology to price their various products in other to maximize profit and customer retention. Banks can use data mining for fraud detection, by running analysis of past transactions, they can fish out fraudulent transactions by pattern identification.

Telecommunication companies face escalating competition, which is forcing them to engage in special market pricing programs aimed at retaining customers and attracting new ones. Data mining Knowledge discovery in the telecommunication industry comes, in the area of call, detail record analysis, and customer loyalty.

In conclusion, data mining represents the link from the data stored over many years through various interactions with customers via online portals and websites, and the knowledge necessary to be successful in relationship marketing concepts. Through the full implementation of a CRM program, which must include data mining, organizations can foster improved loyalty, increase the value of their customers, and attract the right customers.

Charles Kogolo, Assistant Consultant, Technology. pcl.