I recently joined fellow marketing colleagues at a meeting of the Midwest Chapter of the ANA B2B Committee in Chicago. The focus of this meeting was on lead generation, and we enjoyed brainstorming solutions to the customer acquisition problems marketers face every day.
Speedeon Data Blog
Modern marketers often frame their role as developing customer-centric marketing strategies to maximize some metric of importance, such as response rates, ROI, or cost per acquisition (CPA). Predictive models are analytical tools that provide us a formal way for maximizing (or minimizing) those metrics. This post is Part II in a series offering a deep dive into how predictive models are developed and can be used in new customer acquisition. You can read Part I here.
Modern marketers often frame their role as developing customer-centric marketing strategies to maximize some metric of importance, such as response rates, ROI, or cost per acquisition (CPA). Predictive models are analytical tools that provide us a formal way for maximizing (or minimizing) those metrics. This post is Part I in a series offering a deep dive into how predictive models are developed and can be used in new customer acquisition.
Successful marketing programs depend on knowing which customers to target, with what offers, and when. When you need to improve the effectiveness of your marketing efforts but aren’t sure how, consider investing in predictive analytics.
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.
Are you hoping to become a social media marketing superhero? The importance of social media as an engagement channel and rich resource for customer insights is well established. Brands have been on a continued quest to harness the underlying value contained within the considerable volumes and varieties of social media and other digital data sources. Increasingly, they are turning to predictive analytics to uncover customer behaviors and sentiment, identify emerging trends, and ultimately predict consumer intent and future behavior. In the latest edition of EngageNow, our feature article, “Becoming A Social Media Marketing Superhero” examines the growing importance of social media and the increasing interest in predictive analytics as a means of harnessing social media’s considerable potential.
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:
Predictive analytics are on the rise and part of a confluence of rapidly evolving technologies and new data sources that are driving enterprise-wide optimization and real time, one-to-one customer engagement.