Find prospects who are the most likely to become—and stay—loyal customers. Wiland’s predictive analytics and modeling solutions identify the best new prospects from within our vast database. This approach means you can acquire new customers affordably and realize the best return on investment for your acquisition campaigns in all channels.
Understand your customers as never before. Our extensive data, analytics, and segmentation allow you to concentrate your resources on the most promising customers in your CRM database. We also help you avoid the high cost of continuing to pursue the wrong customers, and focus on those whose behavior, as indicated in our data, indicates brand loyalty and the likelihood of using multiple products offered by your institution.
Make past customers part of your future marketing plans with Wiland’s customer reactivation modeling. We can identify the inactive customers who are most likely to become loyal customers, based on their recent, relevant activity elsewhere as observed in the Wiland Database.
Focus your marketing efforts (and dollars) on the customers who are most likely to respond to your next offer. For financial services organizations that generate a large number of leads in their advertising efforts, our predictive models will score those leads and enable you to prioritize follow-up sales and marketing efforts for maximum ROI. Our predictive modeling also determines which customers within your CRM data have the greatest potential lifetime value. We will identify customers you currently market to who are unprofitable, and find inactive customers who are more likely to come back for their financial needs.
Cut the waste out of expensive prospecting and customer marketing. Our predictive models rank names from top to bottom based on customer behavior, giving you the confidence to remove low-performing names from online and offline prospecting efforts. Unresponsive names can be replaced with better prospects, or the savings can be applied to other campaigns or across other channels.