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Volume merchants use data-mining techniques to get the upper hand in client and supply chain management. New technologies have the potential to level the playing field for consumers.

Data-Mining for Transaction Patterns - BC

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High-volume merchants, including Walmart, Safeway, Save-on Foods, and others, have already implemented programs to learn more about their clients. Each transaction is stored in a database, where each client should have a unique number. Client participation is encouraged by providing benefits such as lower prices or better credit terms.

Initial, fairly innocuous, applications for the resultant data sets included optimizing the supply chain by arranging for lower inventories, automatically creating purchase orders for just-in-time (JIT) delivery of merchandise, tracking and managing spoilage, and maintaining year-to-year performance statistics.

More controversial applications have included measuring and reacting to the profitability or transaction patterns (favourable or otherwise) of clients. In other words, just as large, nationally regulated, databases are maintained to keep track of credit relationships between lenders and borrowers, which are now used by lenders to grant preference to more profitable and/or less risky borrowers, retailers and high-volume merchants are applying private and mostly unregulated databases to ordinary transactions such as purchases and returns of merchandise.

Several of the methods used by high-volume merchants to maintain customer data lack the property of being able to uniquely identify the client responsible for a transaction. For example, some vendors allow clients to use telephone numbers to get better prices, since it is easier to give a telephone number than to carry a special card. These systems, while sufficient for understanding broad trends and usage patterns, are insufficient for any true accountability, because the telephone number can be guessed easily, and there is no verification that the person making the purchase actually owns that telephone number. Such a system would not be a good candidate for the kind of industry-wide information sharing used in the credit industry.

Kroger stores in North America, and the METRO Group chain of grocery stores in Germany, are two examples of businesses which now use biometric data such as fingerprints to uniquely identify their customers. By taking this step, and acquiring appropriate permissions from their clients, grocery store chains could truly realize the benefits of sharing customer data.

And what are these benefits, one might ask? For instance, the best clients could easily be identified, and better purchase terms (prices, availability, or credit terms) could be offered to these clients. Conversely, clients with unprofitable transaction patterns could also be identified and discouraged from engaging in these patterns. For instance, customers who only purchase loss-leaders, or who routinely purchase clothing only to return the clothing a few weeks later.

Based on the above, it is easy to see that from the consumer point of view, the merchants who are maintaining databases against them seem to have several unfair advantages:

  1. Access to information in database format. This enables the merchants to discern patterns that are not easy to detect when the information is scattered.
  2. Lack of effective regulation that ensures that the information is accurate. Despite privacy laws such as PIPEDA in Canada, it is very difficult to keep track of all of the databases being maintained by merchants, and it may be economically impractical to conduct detailed audits, even of individual accounts.
  3. Merchants have an up-to-date transaction database spanning a large set of clients, but clients would not have a similar kind of database spanning a large set of merchants.

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