Промышленный лизинг Промышленный лизинг  Методички 

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As these steps suggest, the key to success is incorporating data mining into business processes and being able to foster lines of communication between the technical data miners and the business users of the results.

Identify the Business Opportunity

The virtuous cycle of data mining starts with identifying the right business opportunities. Unfortunately, there are too many good statisticians and competent analysts whose work is essentially wasted because they are solving problems that dont help the business. Good data miners want to avoid this situation.

Avoiding wasted analytic effort starts with a willingness to act on the results. Many normal business processes are good candidates for data mining:

Planning for a new product introduction

Planning direct marketing campaigns

Understanding customer attrition/churn

Evaluating results of a marketing test

These are examples of where data mining can enhance existing business efforts, by allowing business managers to make more informed decisions-by targeting a different group, by changing messaging, and so on.

To avoid wasting analytic effort, it is also important to measure the impact of whatever actions are taken in order to judge the value of the data mining effort itself. If we cannot measure the results of mining the data, then we cannot learn from the effort and there is no virtuous cycle.

Measurements of past efforts and ad hoc questions about the business also suggest data mining opportunities:

What types of customers responded to the last campaign?

Where do the best customers live?

Are long waits at automated tellers a cause of customers attrition?

Do profitable customers use customer support?

What products should be promoted with Clorox bleach?

Interviewing business experts is another good way to get started. Because people on the business side may not be familiar with data mining, they may not understand how to act on the results. By explaining the value of data mining to an organization, such interviews provide a forum for two-way communication.

We once participated in a series of interviews at a telecommunications company to discuss the value of analyzing call detail records (records of completed calls made by each customer). During one interview, the participants were slow in understanding how this could be useful. Then, a colleague pointed out



that lurking inside their data was information on which customers used fax machines at home (the details of this are discussed in Chapter 10 on Link Analysis). Click! Fax machine usage would be a good indicator of who was working from home. And to make use of that information, there was a specific product bundle for the work-at-home crowd. Without our prodding, this marketing group would never have considered searching through data to find this information. Joining the technical and the business highlighted a very valuable opportunity.

When talking to business users about data mining opportunities, make sure they focus on the business problems and not technology and algorithms. Let the technical experts focus on the technology and the business experts focus on the business.

Mining Data

Data mining, the focus of this book, transforms data into actionable results. Success is about making business sense of the data, not using particular algorithms or tools. Numerous pitfalls interfere with the ability to use the results of data mining:

Bad data formats, such as not including the zip code in the customer address in the results

Confusing data fields, such as a delivery date that means planned delivery date in one system and actual delivery date in another system

Lack of functionality, such as a call-center application that does not allow annotations on a per-customer basis

Legal ramifications, such as having to provide a legal reason when rejecting a loan (and my neural network told me so is not acceptable)

Organizational factors, since some operational groups are reluctant to change their operations, particularly without incentives

Lack of timeliness, since results that come too late may no longer be actionable

Data comes in many forms, in many formats, and from multiple systems, as shown in Figure 2.2. Identifying the right data sources and bringing them together are critical success factors. Every data mining project has data issues: inconsistent systems, table keys that dont match across databases, records overwritten every few months, and so on. Complaints about data are the number one excuse for not doing anything. The real question is What can be done with available data? This is where the algorithms described later in this book come in.



External sources of

demographic, lifestyle, and credit information

summarizations, aggregations, views

Historical Data whose format and content change over time


Transaction Data with missing and incomplete fields

Data from multiple competing sources

F CUB

Data Mart

f v


Marketing Summaries

Operational System

Figure 2.2 Data is never clean. It comes in many forms, from many sources both internal and external.

A wireless telecommunications company once wanted to put together a data mining group after they had already acquired a powerful server and a data mining software package. At this late stage, they contacted Data Miners to help them investigate data mining opportunities. In the process, we learned that a key factor for churn was overcalls: new customers making too many calls during their first month. Customers would learn about the excess usage when the first bill arrived, sometime during the middle of the second month. By that time, the customers had run up more large bills and were even more unhappy. Unfortunately, the customer service group also had to wait for the same billing cycle to detect the excess usage. There was no lead time to be proactive.

However, the nascent data mining group had resources and had identified appropriate data feeds. With some relatively simple programming, it was



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