Промышленный лизинг Промышленный лизинг  Методички 

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devising new products, and so on. That is, this group has technical responsibilities rather than business responsibilities.

We have seen data mining groups located in several different places in the corporate hierarchy:

Outside the company as an outsourced activity

As part of IT

As part of marketing, customer relationship management, or finance organization

As an interdisciplinary group whose members still belong to their home departments

Each of these structures has certain benefits and drawbacks, as discussed below.

Outsourcing Data Mining

Companies have varying reasons for considering outsourcing data mining. For some, data mining is only an occasional need and so not worth investing in an internal group. For others, data mining is an ongoing requirement, but the skills required seem so different from the ones currently available in the company that building this expertise from scratch would be very challenging. Still others have their customer data hosted by an outside vendor and feel that the analysis should take place close to the data.


Outsourcing Occasional Modeling

Some companies think they have little need for building models and using data to understand customers. These companies generally fall into one of two types. The first are the companies with few customers, either because the company is small or because each customer is very large. As an example, the private banking group at a typical bank may serve a few thousand customers, and the account representatives personally know their clients. In such an environment, data mining may be superfluous, because people are so intimately involved in the relationship.

However, data mining can play a role even in this environment. In particular, data mining can make it possible to understand best practices and to spread them. For instance, some employees in the private bank may do a better job in some way (retaining customers, encouraging customers to recommend friends, family members, colleagues, and so on). These employees may have best practices that should be spread through the organization.

TIP Data mining may be unncessary for companies where dedicated staff maintain deep and personal long-term relationships with their customers.

Team-Fly®



Data mining may also seem unimportant to rapidly growing companies in a new market. In this situation, customer acquisition drives the business, and advertising, rather than direct marketing, is the principal way of attracting new customers. Applications for data mining in advertising are limited, and, at this stage in their development, companies are not yet focused on customer relationship management and customer retention. For the limited direct marketing they do, outsourced modeling is often sufficient.

Wireless communications, cable television, and Internet service providers all went through periods of exponential growth that have only recently come to an end as these markets matured (and before them, wired telephones, life insurance, catalogs, and credit cards went through similar cycles). During the initial growth phases, understanding customers may not be a worthwhile investment-an additional cell tower, switch, or whatever may provide better return. Eventually, though, the business and the customer base grow to a point where understanding the customers takes on increased importance. In our experience, it is better for companies to start early along the path of customer insight, rather than waiting until the need becomes critical.

Outsourcing Ongoing Data Mining

Even when a company has recognized the need for data mining, there is still the possibility of outsourcing. This is particularly true when the company is built around customer acquisition. In the United States, credit bureaus and household data suppliers are happy to provide modeling as a value added service with the data they sell. There are also direct marketing companies that handle everything from mailing lists to fulfillment-the actual delivery of products to customers. These companies often offer outsourced data mining.

Outsourcing arrangements have financial advantages for companies. The problem is that customer insight is being outsourced as well. A company that relies on outsourcing customers analytics runs the risk that customer understanding will be lost between the company and the vendor.

For instance,one company used direct mail for a significant proportion of its customer acquisition and outsourced the direct mail response modeling work to the mailing list vendors. Over the course of about 2 years, there were several direct mail managers in the company and the emphasis on this channel decreased. What no one had realized was that direct mail was driving acquisition that was being credited to other channels. Direct mail pieces could be filled in and returned by mail, in which case the new acquisition was credited to direct mail. However, the pieces also contained the companys URL and a free phone number. Many prospects who received the direct mail found it more convenient to respond by phone or on the Web, often forgetting to provide the special code identifying them as direct mail prospects. Over time, the response attributed to direct mail decreased, and consequently the budget for



direct mail decreased as well. Only later, when decreased direct mail led to decreased responses in other channels, did the company realize that ignoring this echo effect had caused them to make a less-than-optimal business decision.

Insourcing Data Mining

The modeling process creates more then models and scores; it also produces insights. These insights often come during the process of data exploration and data preparation that is an important part of the data mining process. For that reason, we feel that any company with ongoing data mining needs should develop an in-house data mining group to keep the learning in the company.

Building an Interdisciplinary Data Mining Group

Once the decision has been made to bring customer understanding in-house, the question is where. In some companies, the data mining group has no permanent home. It consists of a group of people seconded from their usual jobs to come together to perform data mining. By its nature, such an arrangement seems temporary and often it is the result of some urgent requirement such as the need to understand a sudden upsurge in customer defaults. While it lasts, such a group can be very effective, but it is unlikely to last very long because the members will be recalled to their regular duties as soon as a new task requires their attention.

Building a Data Mining Group in IT

A possible home is in the systems group, since this group is often responsible for housing customer data and for running customer-facing operational systems. Because the data mining group is technical and needs access to data and powerful software and servers, the IT group seems like a natural location. In fact, analysis can be seen as an extension of providing databases and access tools and maintaining such systems.

Being part of IT has the advantage that the data mining group has access to hardware and data as needed, since the IT group has these technical resources and access to data. In addition, the IT group is a service organization with clients in many business units. In fact, the business units that are the customers for data mining are probably already used to relying on IT for data and reporting.

On the other hand, IT is sometimes a bit removed from the business problems that motivate customer analytics. Since very slight misunderstandings of the business problems can lead to useless results, it is very important that people from the business units be very closely involved with any IT-based data mining projects.



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