Predicting Profits With Models

Understanding which customers will buy which product or service is at the heart of personalization, a booming and still evolving industry. In addition to better leveraging existing data and analytics, numerous new and rich sources of information are available to support predictive models that target the right consumers with the right products and offers.

Even with several high-profile data breaches, consumers are sharing personal information freely and frequently. This trend is partly driven by consumers themselves. For example, about 50 percent of Web users are using the social sign-on option when engaging with other websites and overwhelmingly value the benefits of blending their online persona and consumer persona offers. As a result, barring a truly catastrophic abuse of consumer data, regulatory policy will remain focused on clear cases of deception or omission, and the real debate about consumer privacy will likely take place once we, as marketers and consumers, have a moment to assess where we actually are.

If there is any overall theme for the new age of data-driven targeting, it is that no single data or analytical process will provide marketers with the differential advantage they need to align the right customer with the right product or service. The real answer is about data integration and being able to build the right analytical solutions to leverage these disparate data sources.

Author: Shiv Gupta

Shiv Gupta is a principal at Quantum Sight LLC. He helps clients develop data, analytics and digital technology strategies to drive compelling relationships with customers. In this blog, he'll discuss ways in which marketing organizations can regain their strategic bearings and leverage their tech stack for both short-term and long-term gains. Reach him at shiv.gupta@quantumsight.com.

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