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interaction and the ability to use what has been learned to serve customers better. As a result, the company records every interaction with its customers and keeps an extensive historical record of these interactions.
An Ideal Data Mining Environment
The ideal context for data mining is an organization that appreciates the value of information. Bringing together customer data from all of the many places where it is originally collected and putting it into a form suitable for data mining is a difficult and expensive process. It will only happen in an organization that understands how valuable that data is once it can be properly exploited.
Information is power. A learning organization values progress and steady improvement; such an organization wants and invests in accurate information. Remember that the producers of information always have real power to determine what data is available and when. They are not passive consumers of a take-it-or-leave-it data warehouse, they have the power to determine what data is available, although collecting such data might mean changing operational procedures.
The Power to Determine What Data Is Available
In the ideal data mining environment, the importance of data analysis is recognized and its results are shared across the organization. Marketing people instinctively regard every campaign as a controlled experiment, even when that means not including some customers in a promising campaign because those customers are part of a control group. Designers of operational systems instinctively keep track of all customer transactions, including nonbillable ones such as customer service inquiries, bank account balance inquiries, or visits to particular sections of the company Web site. Everyone expects that customer interactions from different channels can be identified as involving the same customer, even when some happen at an ATM, some in a bank branch, some over the phone, and some on the Web.
In such an environment, an analyst at a telephone company trying to understand the relationship between quality of wireless telephone service and churn has no trouble getting customer-level data on dropped calls and other failures.
The analyst can also readily see a customer’s purchase history even though some purchases were made in stores, some through the mail-order catalog, and some on the Web. It is similarly easy to determine, for each of a customer’s calls to customer service, the duration of the call and whether the call was handled by a human representative or stayed in the IVR, and in the latter case, what path was followed through the prompts. Best of all, when the required
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data is not readily available, there is a team of people whose job it is to make it available—even when that means redesigning an application form, reprogramming an automated switch—or simply loading the data correctly in the first place.
The Skills to Turn Data into Actionable Information
The ideal data mining environment is staffed by people whose superior skills in data processing and data mining are only surpassed by their intimate understanding of how the business operates and its goals for the future. The data mining group includes database experts, programmers, statisticians, data miners, and business analysts, all working together to ensure that business decisions are based on accurate information. This team of people has the communication skills to spread whatever they may learn to the appropriate parts of the organization, whether that is marketing, operations, management, or strategy
All the Necessary Tools
The ideal data mining environment includes sufficient computing power and database resources to support the analysis of the most detailed level of customer transactions. It includes software for manipulating all that data and creating model sets from it. And, of course, it includes a rich collection of data mining software so that all the techniques from Chapters 5–13 can be applied.
Back to Reality
Readers will not be shocked to learn that we have never seen the ideal data mining environment just described. We have, however, worked with many companies that are moving in the right direction. These companies are taking steps to transform themselves into customer-centric organizations. They are building data mining groups. They are gathering customer data from operational systems and creating a single customer view. Many of them are already reaping substantial benefits.
Building a Customer-Centric Organization
The first component of the utopian vision that opened the chapter was a truly customer-centric organization. In terms of data, one of the hardest parts of building a customer-centric organization is establishing a single view of the customer shared across the entire enterprise that informs every customer
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interaction. The flip side of this challenge is establishing a single image of the company and its brand across all channels of communication with the customer, including retail stores, independent dealers, the Web site, the call centers, advertising, and direct marketing. The goal is not only to make more informed decisions; the goal is to improve the customer experience in a measurable way. In other words, the customer strategy has both analytic and operational components. This book is more concerned with the analytic component, but both are critical to success.
T I P Building a customer-centric organization requires a strategy with both analytic and operational components. Although this book is about the analytical component, the operational component is also critical.
Building a customer-centric organization requires centralizing customer information from a variety of sources in a single data warehouse, along with a set of common definitions and well-understood business processes describing the source of the data. This combination makes it possible to define a set of customer metrics and business rules used by all groups to monitor the business and to measure the impact of changing market conditions and new initiatives.
The centralized store of customer information is, of course, the data warehouse described in the previous chapter. As shown in Figure 16.1, there is two-way traffic between the operational systems and the data warehouse.
Operational systems supply the raw data that goes into the data warehouse, and the warehouse in turn supplies customer scores, decision rules, customer segment definitions, and action triggers to the operational system. As an example, the operational systems of a retail Web site capture all customer orders. These orders are then summarized in a data warehouse. Using data from the data warehouse, association rules are created and used to generate cross-sell recommendations that are sent back to the operational systems. The end result: a customer comes to the site to order a skirt and ends up with several pairs of tights as well.
Creating a Single Customer View
Every part of the organization should have access to a single shared view of the customer and present the customer with a single image of the company. In practical terms that means sharing a single customer profitability model, a single payment default risk model, a single customer loyalty model, and shared definitions of such terms as customer start, new customer, loyal customer, and valuable customer.
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Operational Data
(billing, usage, etc.)
Operational
Systems
Business
Users
Segments, Actions,
Common Def
Common
initions
Metadata
Common Repository
of Customer
Information
Figure 16.1 A customer-centric organization requires centralized customer data.
It is natural for different groups to have different definitions of these terms.
At one publication, the circulation department and the advertising sales department have different views on who are the most valuable customers because the people who pay the highest subscription prices are not necessarily the people of most interest to the advertisers. The solution is to have an advertising value and a subscription value for each customer, using ideas such as advertising fitness introduced in Chapter 4.
At another company, the financial risk management group considers a customer “new” for the first 4 months of tenure, and during this initial probationary period any late payments are pursued aggressively. Meanwhile, the customer loyalty group considers the customer “new” for the first 3 months and during this welcome period the customer is treated with extra care. So which is it: a honeymoon or a trial engagement? Without agreement within the company, the customer receives mixed messages.
For companies with several different lines of business, the problem is even trickier. The same company may provide Internet service and telephone service, and, of course, maintain different billing, customer service, and operational systems for the two services. Furthermore, if the ISP was recently acquired by the telephone company, it may have no idea what the overlap is between its existing telephone customers and its newly acquired Internet customers.
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Defining Customer-Centric Metrics
On September 24, 1929, Lieutenant James H. Doolittle of the U.S. Army Air Corps made history by flying “blind” to demonstrate that with the aid of newly invented instruments such as the artificial horizon, the directional gyroscope, and the barometric altimeter, it was possible to fly a precise course even with the cockpit shrouded by a canvas hood. Before the invention of the artificial horizon, pilots flying into a cloud or fog bank would often end up flying upside down. Now, thanks to all those gauges in the cockpit, we calmly munch pretzels, sip coffee, and revise spreadsheets in weather that would have grounded even Lieutenant Doolittle. Good business metrics are just as crucial to keeping a large business flying on the proper course.