Do you know who your most profitable customers are? (Business Credit Selected Topic).

Sifting through reams of accounting data on hundreds or even thousands of customers manually is costly, inefficient and largely ineffective in identifying customers that are either (a) Are not generating the desired return on investment or after tax profit, or (b) unprofitable. Historically, companies have generalized about the profitability of individual customers. In some cases, where additional incentives such as co-op advertising programs were offered to customers, these costs were factored in when estimating the profitability of an account based on corporate profitability. However, there were fundamental limitations to this process. The most striking of these limitations or weaknesses was the fact that these profitability models tended to use average gross profit margin (company wide) as the starting point for customer profitability analysis. The flaw in this process is obvious: depending on a variety of factors including the mix of products sold, the customer's ability to negotiate prices, and any sales incentives offered, an individual customer's gross profit margin and o perating profit may have little in common with the seller's average margins.

Realizing that a manual approach to evaluating information stored in a company's computer system(s) is not an efficient way to extract and analyze meaningful data, some companies have invested the time and resources to acquire the software and hardware necessary for extracting data, which is then used to create "information warehouses" stocked with customer data. Competition has become so fierce that companies must look at every tool that can be used to maintain a competitive advantage. This is much more evident in relation to revenue and profitability than ever before. Increases in revenue and market share alone do not drive business as they once did. More than ever, there is more emphasis in measuring what any increase in revenues is contributing to the seller's bottom line.

Technology has become a tool to further educate company managers on ways to alter strategies so that the company can increase revenues and grow profitably. It is accurate to state that it is counterproductive to increase gross revenues at the cost of decreased net profits. However, without the right tools, it is not always possible for a company to identify which customers contribute to profitability and which do not. For this reason, companies are using new technology to measure account specific profitability. One such technology is generically called "data mining."

Data mining involves companies using software to extract information from accounting data and other records to help managers throughout the organization to control, direct and drive the business toward increased sales and increased profits Data mining technology works on the premise that some customers are capable of adding more revenue than others, but additional revenues do not necessarily translate into additional profits: not all customers are profitable irrespective of how much revenue they generate for the seller. For example if a manufacturer has to add a second shift on the assembly line to meet the requirements of one customer, it is possible and even likely, that the incremental profits on the additional sales would be more than offset by the additional costs associated with staffing, managing and supporting a second shift.

Data mining allows companies to compare the complex mix of:

* Price

* Product cost