The private banking group at one large bank was able to violate corporate information technology standards, bringing in Macintosh computers and AS400s, when the standards for the rest of the bank were Windows and Unix.
The private bank could get away with it; they were that profitable.
Also, just having large businesses as customers does not mean that each customers necessarily merits such close attention. Directories, whether on the Web or on yellow pages, have many business customers, but almost all are treated equally. Although the customers include many large businesses, each listing brings in a small amount of revenue so few are worth additional effort.
Mass Intimacy
At the other extreme is the mass intimacy relationship. Companies that are serving a mass market typically have hundreds of thousands, or millions, or tens of millions of customers. Although most customers would love to have the attention of dedicated staff for all their needs, this is simply not economically feasible. Companies would have to employ armies of people to work with customers, and the incremental benefit would not make up for the cost.
This is where data mining fits in particularly well with customer relationship management. Many customer interactions are fully automated, especially on the Web. This has the advantage of being highly scalable; however, it comes at a loss of intelligence and warmth in the customer relationship. Using technology to make the relationship stronger is a multipronged effort:
■■ Staff who work directly with customers (whether face-to-face, through call centers, or via Web-enabled interfaces) must be trained to treat customers respectfully, while at the same time trying to expand the relationship using enhanced information about customers.
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■■ Automated systems need to be flexible, so different messages can be directed to different customers. This clearly applies on the Web, but it also applies to billing inserts, cashier receipts, background scripts read while customers are on hold, and so on.
■■ Both staff and automated systems that work with customers need to be able to respond to new practices and new messages. Sometimes, these new approaches come from the good ideas of staff. Sometimes, they come from careful analysis and data mining. Sometimes, from a combination of the two.
This is an extension of the virtuous cycle of data mining. Learning—
whether accomplished through algorithms or through people—needs to be acted upon. Rolling out results is as necessary as getting them in the first place.
Success involves working with call centers and training personnel who come in contact with customers. Customer interactions over the Web have the advantage that they are already automated, making it possible to complete the virtuous cycle electronically. People are still involved in the process to manage and validate the results. However, the Web makes it possible to obtain data, analyze it, act on the results, and measure the effects without ever leaving the electronic medium.
The goal of customer understanding can conflict with the goal of efficient channel operation. One large mobile telephone company in the United States, TEAMFLY
for instance, tried asking customers for their email addresses when they called in with service related questions. Having the email address has many benefits.
For one thing, future service questions could be handled over the Web at a lower cost than through the call center. It also opens the possibility for occasional marketing messages, cross-sell, and retention opportunities. However, because the questions added several seconds to the average call length, the call center stopped asking. For the call center, getting on to the next call was more important than enhancing the relationship with each customer.
WA R N I N G Privacy is a major concern, particularly for individual customers.
However, it is peripheral to data mining itself. To a large extent, the concern is more about companies sharing data with each other rather than about a single company using data mining on its own to understand customer behavior. In some jurisdictions, it may be illegal to use information collected for operational purposes for another purpose such as marketing or improving customer relationships.
Team-Fly®
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Mass intimacy also brings up the issue of privacy, which has become a major concern with the growth of the Web. To the extent that we are studying customer behavior, the data sources are the transactions between the customer and the company—data that companies typically can use for business purposes such as CRM (although there are some legal exceptions even to this). The larger concern is when companies sell information about individuals.
Although such data may be useful when purchased, or may be a valuable source of revenue, it is not a necessary part of data mining.
In-between Relationships
The in-between relationship is perhaps the most challenging. These are the customers who are not big enough to warrant their own account teams, but are big enough to require specialized products and services. These may be small and medium-sized businesses. However, there are other groups, such as so-called “mass affluent” banking customers, who do not have quite enough assets to merit private banking yet who still do want special attention.
These customers often have a wider array of products, or at least of pricing mechanisms—discounts for volume purchases, and so on—than mass intimacy customers. They also have more intense customer service demands, having dedicated call centers and Web sites. There are often account specialists who are responsible for dozens or hundreds of these relationships at the same time. These specialists do not always give equal attention to all customers. One use of data mining is in spreading best practices—finding what has been working and has not been working and spreading this information.
When there are tens of thousands of customers, it is also possible to use data mining directly to find patterns that distinguish good customers from bad, and for determining the next product to sell to a particular customer. This use is very similar to the mass intimacy case.
Indirect Relationships
Indirect relationships are another type of customer relationship, where intermediate agents broker the relationship with end users. For instance, insurance companies sell their products through agents, and it is often the agent that builds the relationship with the customer. Some are captive agents that only sell one company’s policies; others offer an assortment of products from different companies.
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Such agent relationships pose a business challenge. For instance, an insurance company once approached Data Miners, Inc. to build a model to determine which policyholders were likely to cancel their policies. Before starting the project, the company realized what would happen if such a model were put in place. Armed with this information, agents would switch high-risk policyholders to other carriers—accelerating the loss of these accounts rather than preventing it. This company did not go ahead with the project. Perhaps part of the problem was a lack of imagination in figuring out appropriate interventions. The company could have provided special incentives to agents to keep customers who were at risk—a win-win situation for everyone involved.
In such agent-based relationships, data mining can be used not only to understand customers but also to understand agents.
Indirection occurs in other areas as well. For instance, mutual fund companies sell retirement plans through employers. The first challenge is getting the employer to include the funds in the plan. The second is getting employees to sign up for the right funds. Ditto for many health care plans at large companies in the United States.
Product manufacturers have a similar problem. Telephone handset manufacturers such as Motorola, Nokia, and Ericsson, would like to develop a loyal customer base, so customers continue to return to them handset after handset.
Automobile manufacturers have similar goals. Pharmaceutical companies have traditionally marketed to the doctors who prescribe drugs rather then the people who use them, although drugs such as Viagra are now also being marketed to consumers. Another good example of a campaign for a product sold indirectly is the “Intel Inside” campaign on personal computers—a mark of quality meant to build brand loyalty for a chip that few computer users ever actually see. However, Intel has precious little information on the people and companies whose desktops are adorned with their logo.
Customer Life Cycle
When thinking about customers, it is easy to think of them as static, unchanging entities that compose “the market.” However, this is not really accurate.
Customers are people (or organizations of people), and they change over time.
Understanding these changes is an important part of the value of data mining.
These changes are called the customer life cycle. In fact, there are two customer life cycles of interest, as shown in Figure 14.2. The first are life stages.
For an individual, this refers to life events, such as graduating from high school, having kids, getting a job, and so on. For a business customer, the life cycle often refers to the size or maturity of the business. The second customer life cycle is the life cycle of the relationship itself. These two life cycles are fairly independent of each other, and both are very important for business.