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Speedeon Data Blog
One of the common challenges facing today’s marketers is an abundance of customer data but the inability to translate that data into actionable insights. This is especially critical when deciding on which customers to focus your marketing resources. For most businesses, acquiring customers is at or near the top of their priority list. Acquiring new customers is not that difficult though. Acquiring profitable, long term customers is the real challenge. But where to start?
Saturation mail has long been used by marketers as a cost effective way to saturate a neighborhood with their direct mail communications via carrier routes. But traditional saturation mail strategies present marketers with a dilemma; big savings on list costs and postal rates versus imprecise targeting and potentially lower response rates. Response models can help marketers better optimize their saturation mail programs, resulting in increased targeting capabilities and response rates along with reduced print and postage costs.
Just like there are two-sides to a penny, there are two fundamental ways of considering marketing, sales and business performance overall. There is the topline and the bottom-line. Like the two sides of a coin, revenue and costs are intrinsically related to each other through the concepts of profit and return-on-investment. Although business activities most often tend to be viewed from a binary perspective, i.e., revenue-generating v. cost-generating endeavors, many activities provide simultaneous benefits. Among their growing usage in today’s data-driven business environments, many applications of predictive analytics provide simultaneous revenue generating (a penny earned) and cost saving (a penny saved) benefits. Consider the following:
Marketers are under growing pressure to effectively consolidate and leverage an increasingly complex landscape of customer data, which includes mobile, social and other consumer generated data, internal CRM data, and highly contextual, third-party lifestyle and life-stage trigger data.