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 hasnt 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.

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. 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.

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, 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.

WARNINGPrivacy 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.

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