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Home / News & Events / The Convergence of Business Issues and Analytics: Framing Marketing Objectives as Optimization Problems

The Convergence of Business Issues and Analytics: Framing Marketing Objectives as Optimization Problems

Presented: Tuesday, February 6th, 2007

Speaker: Paul Maiste, Lityx

Listen to the archived web cast. Download instructions on how to view the web cast.

Download the web cast presentation.

More and more, marketers are relying on sound analytic techniques, such as predictive modeling, to increase customer value, increase loyalty and retention, and maximize campaign effectiveness and ROI.  However, as issues become more complex and far-reaching, and analytic techniques more sophisticated, one of the most difficult challenges for a marketer is translating their business issues into the language of analytics.  In this web cast, we will lay the groundwork to better understand the power of advanced analytic techniques, and provide examples of how to cast common, but difficult, marketing problems using analytic language.

Paul Maiste, Lityx

Paul Maiste has over 15 years experience providing analytic, statistical modeling, and data mining consulting services to a wide-range of companies, focusing primarily in the areas of marketing analytics and customer relationship management.  He is a recognized expert in data mining and predictive modeling, and its applications to marketing, and has been an invited speaker at numerous conferences and symposiums on these topics both in the U.S. and abroad.

During nearly seven years at PricewaterhouseCoopers (PwC), he led a data mining practice focused on marketing and customer management solutions, and helped build a strong linkage between analytics, data mining, and data warehousing within the firm and for clients.  In addition, he was PwC’s global alliance manager for the relationship marketing practice.

Currently, Paul is founder and president of Lityx, a consulting firm that provides services and training in marketing analytics and strategy, predictive modeling, and data mining.  His past clients have included many Fortune 500 companies as well as small and mid-size companies, and span many industries.

Paul holds a B.S. degree in Mathematics from Loyola College in Maryland and a Ph.D. in Statistics from North Carolina State University.