Mining for higher yields


One of the more powerful technological forces in a world of improving customer value is in data mining. In its simplicity, data mining explores high volumes of customer information and identifies key insights on customers and markets, the result being improved service delivery and profitability. In saying this, data mining does not guarantee that you will get the right answers. Great care needs to be taken to ensure the right questions are asked and that you are ready to challenge some of the underlying assumptions and theories you may be exploring.

Typically data mining provides answers to well-focused questions such as:

  • Who are the early adapters to products?

  • Which customers are mostly likely to drop their current service and shop elsewhere?

  • What is the probability that a customer will purchase at least $200 worth of merchandise from a direct mail campaign?

  • Which customers are most likely to respond to a particular offer?

  • Which demographic is most likely to purchase the latest product or service?

No doubt, if properly chosen , such questions can have an immediate impact on customer yield, retention and return on investment. It can also directly impact the execution of marketing campaigns and help to influence buying behaviour and maintain customer loyalty, particularly if campaign strategies deliver high-value offers and propositions to the customers who will be most interested.

As you explore case studies of data mining you will soon discover organizations like Scandinavian Airlines, IBM and the Ritz Carlton Hotel which have carefully managed the interface between data mining and customer relationship over the last decade . They have become proven performers and clearly understand the notion of customer share. The Ritz Carlton Hotel has maintained a database on its high-value customers and has used it to provide personalized service. For example, let us assume a customer arrives at their Melbourne hotel. He likes to have a bowl of fresh fruit in the room on arrival and always a glass of Pinot Noir with his room service evening meal. He also likes CNN on cable television. Such precise information is recorded in the database and enables the guest s experience to be met perfectly each time he stays in a Ritz Carlton hotel anywhere in the world.

Of course, such sophistication does not come cheaply. The logic that drives data mining can vary from complex machine learning and fuzzy logic, to the simple matching of patterns and clusters of analysis. It is worth noting that customer data can also be acquired from a variety of other external sources and third parties such as mailing houses , on-line retailers and credit card companies. They can also help provide a host of individual choices, demographics and competitive intelligence data.




Winning the Knowledge Game. Smarter Learning for Business Excellence
Winning the Knowledge Game. Smarter Learning for Business Excellence
ISBN: 750658096
EAN: N/A
Year: 2003
Pages: 129

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