Customer Relationship Management (CRM)

Whether you are prospecting for new customers, developing loyalty programs to keep current customers, or looking for the best cross-sell/up-sell opportunities for increasing customer lifetime value, we have powerful analytic and research solutions to support your efforts.

Customer Acquisition

We have executed many market research studies and database marketing analytic projects to identify and outreach to prospective customers for both new and existing products and services. We have vast experience identifying prospects who most resemble the 20% of a current franchise that may account for as much as 80% of current profit; and we can help determine key messaging components to be directed at these prospects.

We offer analytic solutions that achieve better, clearer, more actionable market segmentation and targeting than you could with traditional spreadsheet analysis or other simple statistical techniques.

Customer Loyalty and Retention

In an increasingly competitive world filled with advertising clutter, we can help identify and understand the key customer characteristics and competitive factors associated with loyalty and churn—whether via the design and execution of custom market research studies or the mining of client-proprietary databases and syndicated data. And we can help develop data-driven outreach/communications strategies to maximize loyalty and to intervene at just the right time to minimize churn among the most vulnerable customer segments.

Customer Lifetime Value Maximization

We can assist with development of effective up-sell, cross-sell, next-product-to-sell strategies to help you get the most out of your current franchise. Whether you want to get customers to buy more frequently, spend more per transaction, or both. We have powerful, data-based research and analytic solutions to help you succeed.

Other CRM Applications

Here are a few more examples of how analytic-and research-driven CRM solutions can help Marketing Managers:

• Clearer customer relationship definitions: For example, if your customer base includes a mix of businesses and consumers, there is often a subgroup of customers that are difficult to classify clearly as either businesses or consumers. Data mining can help you sort through and clarify your relationship with these customers, so that targeted business/consumer outreach efforts can better address their needs.

• Risk management (bad-debt or insurance-risk prediction, fraud detection, etc.): By developing and applying models using historical data, data mining can help you reduce the future incidence of these problems.

• More effective and efficient media spending: Data mining can be useful not only for improving highly personalized communications strategies, but also for optimizing media spending across media and markets. For example, sophisticated data mining techniques can supplement and enhance standard media-analytic techniques to better optimize TV spending by DMA or ADI; or to help you optimize a budget that involves several outbound and inbound marketing strategies in combination.
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