Marketing success for your business depends on making the right decisions. Sophisticated technologies are used by businesses to acquire, warehouse and maintain an immense amount of data. However, raw data has no value. With the vast array and complexity of customer data available, you no longer have to act on hunches. Your business needs to know what to do with the information gathered.

The following information is an overview of custom data mining solutions available to transform your data into actionable information that is truly useful to your marketing efforts ALL IN RECORD TIME!

• ACQUISITION Acquisition modeling enables you to acquire new customers more cost-effectively. When combined with estimates of potential customer value, you can target the most desirable prospects, those who will become high-value, long term customers.
• PROXINITY/ PROPENSITY Proximity/ Propensity modeling is particularly effective for retail businesses. These specialized acquisition models identify prospects with proximity to store locations that have a propensity to behave like best customers.
• Cross-Sell/ Up-Sale Cross-sell/Up-Sale models identify customers who are the best prospects for the purchase of additional products and services and for upgrading their existing products and services. The goal is to increase share of wallet. Revenue can increase immediately, but loyalty is enhanced as well due to increase customer involvement.
• Attrition/Defection Attrition/Defection models enable you to identify customers who are at risk of dropping or switching their service to other providers.
• Lifetime Value Lifetime Value models help you to make acquisition decisions on a financially rational basis. Effective prediction of the net present value of future revenue streams from customers gives you the most powerful tool for financially optimizing your marketing investment in both the acquisition of new customers and the retention and growth of existing ones.
• Profiling Profiling groups can help you develop a detailed understanding of how they differ in demographics and behavior.
• Segmentation Classification/Analysis Segmentation Classification/ Analysis partitions the customer base into mutually exclusive and cumulatively exhaustive groups. The members of each are as similar to each other as possible, and as different as possible from members of the other groups. Segmentation enables the fine-tuning of messages based on the unique needs and values of each group. Plus, the effectiveness of many predictive models can be improved by modeling within rather than across segments.
• Revenue &Profit Prediction Revenue & Profit Prediction can often tell you more than modeling simple response / non-response, especially if order sizes, monthly billing or margins differ across goods and services. Not all responses have equal value, and a model that maximizes responses doesn't necessarily maximize revenue or profit.

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