The purpose of data mining is to complement other customer service initiatives, not to replace them. Customer interactions take place through many channels—through direct mail pieces, through call centers, face-to-face, via advertising. Now that the “click and mortar” way of doing business is becoming standard, most businesses provide an online interface to their customers.
The Web, with its new capabilities for interacting with customers, has the potential to provide a wealth of customer behavior data that can be turned into a new window on the customer relationship. It is ironic that a technology that 447
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has largely replaced human-to-human interactions is allowing companies to treat their customers more personally.
This brings us back to the customer and to the customer life cycle. This chapter strives to put data mining into focus with the customer at the center. It starts with an overview of different types of customer relationships, then goes into the details of the customer life cycle as it relates to data mining. The chapter provides examples of how customers are defined in various industries and some of the issues in deciding when the customer relationship begins and when it ends. The focal point is the customer and the ongoing relationship that customers have with companies.
Levels of the Customer Relationship
One of the major goals of data mining is to understand customers and the relationships that customers have with an organization. A good place to start understanding them better is by using the different levels of customer relationships and what customers are telling us through their behavior.
Customers generate a wealth of behavioral information. Every payment made, every call to customer service, every click on the Web, every transaction provides information about what each customer does, and when, and which interventions are working and which are not. The Web is a particularly rich source of information. CNN does not know who is viewing or paying attention to their cable news program. The New York Times does not know which parts of the paper each subscriber reads. On the Web, though, cnn.com and nytimes.com have a much better indication of readers’ interests. Connecting this source of information back to individuals over time is challenging (not to mention the challenge of connecting readers interests to advertising over time).
Customers are not all created equal. Nor should all customers be treated equally, since some are clearly more valuable than others. Figure 14.1 shows a continuum of customer relationships, from the perspective of the amount of investment worthy of each relationship. Some customers merit very deep and intimate relationships centered around people. Other customers are too numerous and, individually, not valuable enough to maintain individual relationships. For this group, we need technology to help make the relationship more intimate. The third group is perhaps the most challenging, because they are in between those who merit real intimacy and those who merit feigned intimacy. This group often includes small businesses as well as indirect relationships. The sidebar “No Customer Relationship” talks about another situation, companies that do not know about their end users and do not need to.
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Consumers
Very small
Small and medium
Large businesses
(low intimacy)
businesses
businesses
(deep intimacy)
Many customers
Few customers
Each small contribution to profit
Each large contribution to profit
Very important in aggregate
Intimacy
Important individual and in aggregate
Technologies:
Technologies:
Mass intimacy
Sales force automation
Customer relationship management
Account management support
Figure 14.1 Intimacy in customer relationships generally increases as the size of the account increases.
Deep Intimacy
Customers who are worth a deep intimate relationship are usually large organizations—business customers. These customers are big enough to devote dedicated resources, in the form of account managers and account teams. The relationship is usually some sort of business-to-business relationship. One-off products and services characterize these relationships, making it difficult to compare different customers, because each customer has a set of unique products.
An example is the branding triumvirate of McDonald’s, Coca-Cola, and Disney. McDonald’s is the largest retailer of Coke products worldwide. When Disney has special promotions in fast food restaurants for children’s movies, McDonald’s gets first dibs at distributing the toys inside their Happy Meals.
And when Disney characters (at least the good guys!) drink soda or open the refrigerator—Coke products are likely to be there. Coke also has exclusive arrangements with Disney, so Disney serves Coke products at its theme parks, in its hotels, and on its cruises. There are hundreds of people working together to make this branding triumvirate work. Data mining, with even the most advanced algorithms on even the fastest computers, is not going to replace these people—nor will this process be automated in the conceivable future.
On the other hand, even large account teams and individual managers can benefit from analysis, particularly around sales force automation tools. Data mining analysis can help such groups work better, by providing an understanding of what is really going on. Data can still help find some useful answers: which McDonald’s are particularly good at selling which soft drinks?
Where are product placements resulting in higher sales? What is the relationship between weather and drink consumption at theme parks versus hotels?
And so on.
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NO CUSTOMER RELATIONSHIP
The streets of Tokyo are lined with ubiquitous convenience stores that are much like 7-11s or corner convenience stores in Manhattan. These stores carry a small array of products, mostly food, including freshly made lunches. There are three companies that dominate this market, Lawsons, Seven-Eleven Japan, and Family Mart, the third largest of which processes about 20 million transactions each day. Given that the population of Japan is a bit over 120
million, this means that, on average, every Japanese person purchases something from one of these stores every other day. That is a phenomenal amount of consumer interaction.
Dive a bit more deeply into the business. About the only thing these companies know about their customers is that almost everyone who lives in Japan is at least an occasional buyer. Transactions are almost exclusively cash-based, so the companies have no way to tie a customer to a series of transactions over time and in different stores.
The strength of these companies is really in distribution and payments. On the distribution side, they are able to make three deliveries each day to the stores, guaranteeing that lunchtime sushi is fresh and the produce hasn’t wilted. Many people also use the stores near their homes to pay their bills with cash, something that is very convenient in a cash-dominated society. Combining these two businesses, some of the stores are becoming staging points for orders, made through catalogs or over the Web. Customers can pay for and pick up goods in their friendly, neighborhood convenience store.
Japanese convenience stores are an extreme example of businesses that know very little about their end users. Packaged good manufacturers are another example, because they do not own the retailing relationship.
Manufacturers only know when they have shipped goods to warehouses. End-user information is still important, but the behavior is not sitting in their databases, it is in the database of disparate retailers. To find out about customer behavior, they might:
◆ Use industry-wide panels of customers to see how products are used
◆ Use surveys to find out about customers and when and how they use the products
◆ Build relationships with retailers to get access to the point-of-sale data
◆ Listen to the data they are collecting, via complaints and compliments on the Web, in call centers, and through the mail
Distribution data does still have tremendous value, giving an idea of what is being sold when and where. Inside lurks information about which advertising messages should go where and which products are more popular—and data mining can be used for these things.
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On the business-to-business side, even large financial institutions can benefit from understanding customers. One of the largest banks in the world wanted to analyze foreign exchange transactions to determine which clients would benefit from taking out a loan in one currency and repaying it in another rather than taking out the loan in one currency and exchanging the proceeds up front. The goal was to provide better products for the clients and a longer-term relationship. However, people are then needed to interpret and act on these results.
Although the deep relationship is often associated with large businesses, this is not always the case. Private banking groups in retail banks work with high net-worth individuals, and give them highly personalized service—
usually with a named banker managing their relationship. When a private banking customer wants a loan or to make an investment, that person simply calls his or her private banker. Private banking groups have traditionally been highly profitable, so profitable that they can get away with almost anything.