The fact is, however, even with only the most basic information at their disposal, most companies operating today can use data mining to help them make more effective business decisions. For example, purchase activity can be used to segment customers based on a number of different dimensions, including dollars spent, number of transactions, date of last transaction, etc.

With this simple segmentation done, the marketer can then begin to look at more complex scenarios, such as the relationship between where their customers live and what segments they fall into. With aggregate level information such as Statistics Canada Census data in hand, a marketing organization can soon begin to look at such demographic information as average income, age, household size, and ethnic origin.

Thus, although the organization may have started out with relatively little information about its customers, a simple data mining exercise can quickly move it on to potentially profitable new discoveries about its best customers.

Moving forward, the door opens up to even more new marketing opportunities. Companies can, for example, look for areas that have similar demographics, but which have poor sales penetration. Or, new products and services can be developed for existing customers based on this new information.

The reality is that data mining need not be nearly as complicated as many people believe.

Here, then, are a few tips marketers should keep in mind when thinking of developing a data mining strategy.

1. Don't let the inability to acquire new technology become a barrier to entry

   With all the new technology available today, many companies believe they have no choice but to purchase new hardware or software if they want to get involved in data mining. While the ability to invest in the latest and greatest hardware and software can certainly provide an organization with an advantage, it's not a guarantee of success. Nor is it necessarily true that a company will fail in its data mining efforts if it isn't able to invest tens of thousands of dollars in new systems.

   As long as there is a focused and clearly articulated strategy in place, so-called "legacy systems" can often be up to the task demanded of them. And, of course, once one data mining "victory" is achieved, it often becomes much easier to convince those making budget decisions that an investment in a systems upgrade is a good idea.

2. Ensure that your data mining strategy is being driven by business needs

   The vast amount of information and data available to marketers today can quite easily lead to "analysis paralysis" if one's data mining process efforts are not focused. To prevent that from happening, you must ensure that your data mining strategy is based on key business issues and that the opportunities you're pursuing have been clearly identified in the planning process.

   Once identified, marketers need to understand which of their business objectives can best take advantage of the information that currently resides within the organization's database. These objectives then need to be prioritized based on the potential value they represent to the overall business.

3. Understand your end results up front

   Suppose, for instance, customer defection has been identified as a key business issue and you decide to bring in a data mining specialist to build a predictive defection model for your company. It must be made clear to everyone involved that the model will simply produce a ranking of customers based on their likelihood to defect. It will not, on its own, prevent further defections. The model will simply provide you with a targeted list of customers who should be receiving the primary focus for your retention marketing efforts.

4. Test what you've learned and measure your ROI

   Remember, the actions derived from data mining activities are almost always measurable. Ensure that programs using data mining findings are set up in such a way that you can clearly validate key findings and measure the benefit of your data mining investment.