DATA MINING AND CUSTOMER RELATIONSHIPS


By
Praveen Ranjan Srivastava
Lecturer (Computer Science)
Banasthali Vidyapith (Deemed University)
Banasthali-304002 (Rajasthan)
Phone : 01438-228787
E-mail :
spraveen@banasthali.ac.in / pra_ranjan@rediffmail.com
 


Introduction:

The way in which companies interact with their customers has changed dramatically over the past few years. A customer's continuing business is no longer guaranteed. As a result, companies have found that they need to understand their customers better, and to quickly respond to their wants and needs. In addition, the time frame in which these responses need to be made has been shrinking. It is no longer possible to wait until the signs of customer dissatisfaction are obvious before action must be taken. To succeed, companies must be proactive and anticipate what a customer desires. In this situation key question is arises how to catch new customers.

There are some factors are working together to increase the complexity of customer relationships:

  • Day by day arising marketing costs.
  • New product offering.
  • Compressed marketing cycle times.
  • Competitors.

Successful companies need to react to each and every one of these demands in a timely fashion. The market will not wait for your response, and customers that you have today could vanish tomorrow. Interacting with your customers is also not as simple as it has been in the past. Customers and prospective customers want to interact on their terms, meaning that you need to look at multiple criteria when evaluating how to proceed. You will need to automate:

  • The Right Offer
  • To the Right Person
  • At the Right Time
  • Through the Right Channel

The purpose of Data Mining.

Data mining helps marketing professionals improve their understanding of customer behavior. In turn, this better understanding allows them to target marketing campaigns more accurately and to align campaigns more closely with the needs, wants and attitudes of customers and prospects.

If the necessary information exists in a database, the data mining process can model virtually any customer activity. The key is to find patterns relevant to current business problems.

Typical questions that data mining addresses include like:

  • Which customers are most likely to drop their cellular phone service?
  • What is the probability that a customer will purchase at least $100 worth of merchandise from a particular mail-order catalog?
  • Which prospects are most likely to respond to a particular offer?

Data Mining, what is it?

Simply put, data mining is a continuous, iterative process that is the very core of business intelligence. It involves the use of data mining software, sound methodology and human creativity to achieve new insight through the exploration of data to uncover patterns, relationships, anomalies and dependencies. We have achieved our reputation as the data mining industry's leading innovator by developing powerful, user friendly and affordable data mining technology, and by delivering comprehensive knowledge transfer to customers to enable them to take advantage of the business benefits data mining technology makes possible. For almost a decade we has taken the leadership role in broadening user understanding and acceptance of this technology as a highly value decision support system for a wide range of business applications in many different industries.

 
Business Process in Data Mining
 
Data mining is part of a much larger series of steps that takes place between a company and its customers. The way in which data mining impacts a business depends on the business process, not the data mining process. Take product marketing as an example. A marketing manager's job is to understand their market. With this understanding comes the ability to interact with customers in this market, using a number of channels. This involves a number of areas, including direct marketing, print advertising, telemarketing, and radio/television advertising, among others. Data mining, on the other hand, extracts information from a database that the user did not know existed. Relationships between variables and customer behaviors that are non-intuitive are the jewels that data mining hopes to find. And because the user does not know beforehand what the data mining process has discovered, it is a much bigger leap to take the output of the system and translate it into a solution to a business problem.
 
  
Data Mining and Customer Relationship Management
 
Customer relationship management (CRM) is a process that manages the interactions between a company and its customers. The primary users of CRM software applications are database marketers who are looking to automate the process of interacting with customers.

To be successful, database marketers must first identify market segments containing customers or prospects with high-profit potential. They then build and execute campaigns that favorably impact the behavior of these individuals. The first task, identifying market segments, requires significant data about prospective customers and their buying behaviors. In theory, the more data the better. In practice, however, massive data stores often impede marketers, who struggle to sift through the minutiae to find the nuggets of valuable information. After mining the data, marketers must feed the results into campaign management software that, as the name implies, manages the campaign directed at the defined market segments. This separation of the data mining and campaign management software introduces considerable inefficiency and opens the door for human errors. Tightly integrating the two disciplines presents an opportunity for companies to gain competitive advantage.
 
How Data Mining Helps Database Marketing
 
Data mining helps marketing users to target marketing campaigns more accurately; and also to align campaigns more closely with the needs, wants, and attitudes of customers and prospects. If the necessary information exists in a database, the data mining process can model virtually any customer activity. The key is to find patterns relevant to current business problems. Typical questions that data mining addresses include the following:

- which customers are most likely to drop their cell phone service?

- What is the probability that a customer will purchase at least $100 worth of merchandise from a particular mail-order catalog?

- which prospects are most likely to respond to a particular offer? Answers to these questions can help retain customers and increase campaign response rates, which, in turn, increase buying, cross selling, etc.

Business Intelligence Benefits 

Data mining technology delivers two key business intelligence benefits:

1.  Descriptive Function

2.  Predictive Function
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    1. It enables enterprises, regardless of industry or size, in the context of defined business objectives, to automatically explore, visualize and understand their data, and to identify patterns, relationships and dependencies that impact on business outcomes (such as revenue growth, profit improvement, cost containment, and risk management) - a descriptive function.

    2. It enables relationships uncovered and identified through the data mining process to be expressed as business rules, or predictive models. These outputs can be communicated in traditional reporting formats (presentations, briefs, electronic information sharing) to guide business planning and strategy. Also these outputs, expressed as programming code, can be deployed or "hard wired" into business operating systems to generate predictions of future outcomes, based on newly generated data, with higher accuracy and certainty - a predictive function.

For example, in the "CRM" arena a business can evaluate and develop a set of business intelligence rules about all aspects of its customer interactions. A simple example is modeling the likelihood of response to a specific solicitation of a new product or service. Based on these business rules, the business can target its marketing campaigns for maximum response to generate a desired level of response, revenue or profitability. Other typical "CRM" business examples would include:

  • modelling customer acquisition (for targeted marketing and other CRM initiatives)
  • assessing customer defection (for customer service and reclamation purposes)
  • monitoring risk of loss (for customer scoring and credit approval decision making)

However, the reach of data mining technology extends far beyond "CRM" to encompass any process involving the acquisition, interpretation and acting on of data (internally or externally sourced). In the business domain this would include areas as diverse as internal audit and expense control through to research and development for new products or services. Using our data mining components, a wide range of solutions can be developed directly and easily as applications integrated with our data mining engine or as integrated components of mainstream "CRM" "sales force automation", "call center" and other enterprise applications - by third party solution providers or in the case of a large organization by their internal IT personnel working in collaboration and its partners.

Technology Issues 

Data mining solutions are based on the implementation, through programming, of interfaces to generally available and privately developed algorithms, which enable the efficient exploration and organization of data. These algorithms support the identification of patterns, relationships and anomalies of potential interest to business decision-makers.

In addition to implementing these algorithms in a user accessible method, data mining technology also requires an understanding of various databases and implementation of data mining solutions to take advantage of features of such databases (if any) that make data mining tasks more efficient over large volumes of data.

In addition to algorithm implementation issues, key considerations relative to data mining solutions are data preparation issues and ensuring scalability and performance over very large volumes of data.
 


Praveen ranjan srivastava
Lecturer (Computer Science)
Banasthali Vidyapith (Deemed University)
Banasthali-304002 (Rajasthan)
Phone : 01438-228787
Email :
spraveen@banasthali.ac.in / pra_ranjan@rediffmail.com
 

Source : E-mail December 31, 2003

 

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