DATA MINING

Data Mining

Data Mining

Introduction

In today's business world, information about the customer is a necessity for a businesses trying to maximize its profits. A new, and important, tool in gaining this knowledge is Data Mining. Data Mining is a set of automated procedures used to find previously unknown patterns and relationships in data. These patterns and relationships, once extracted, can be used to make valid predictions about the behavior of the customer. Data Mining is generally used for four main tasks:

(1) To improve the process of making new customers and retaining customers;

(2) To reduce fraud;

(3) To identify internal wastefulness and deal with that wastefulness in operations, and

(4) To chart unexplored areas of the internet

The fulfillment of these tasks can be enhanced if appropriate data has been collected and if that data is stored in a data warehouse. According to Stanford University, A Data Warehouse is a repository of integrated information, available for queries and analysis. Data and information are extracted from heterogeneous sources as they are generated. This makes it much easier and more efficient to run queries over data that originally came from different sources. When data about an organization's practices is easier to access, it becomes more economical to mine. Without the pool of validated and scrubbed data that a data warehouse provides, the data mining process requires considerable additional effort to pre-process the data (SAS Institute Online).

How Data Mining and Statistical Modeling Brings Change

Although a marketer with a wealth of experience can often choose relevant demographic selection criteria, the process becomes more difficult as the amount of data increases. The complexities of the patterns increase, both with the number of customers being considered and the increasing detail for each customer. The past few years have seen tremendous growth in consumer databases, so the job of segmenting prospective customers is becoming overwhelming.

Data mining can help this process, but it is by no means a solution to all of the problems associated with customer acquisition. The marketer will need to combine the potential customer list that data mining generates with offers that people are interested in. Deciding what is an interesting offer is where the art of marketing comes in.

Evaluating Test Campaign Responses

Once you have started your test campaign, the job of collecting and categorizing the response behaviors begins. Immediately after the campaign offers go out, you need to track responses. The nature of the response process is such that responses tend to trickle in over time, which means that the campaign can go on forever. In most real-world situations, though, there is a threshold after which you no longer look for responses. At that time, any customers on the prospect list that have not responded are deemed "non-responses." Before the threshold, customers who have not responded are in a state of limbo, somewhere between a response and a non-response.

Building Data Mining Models Using Response Behaviors

With the test campaign response data in hand, the actual mining of customer response behaviors can ...