Upcoming WCAI Events
Sept. 20: Student & Alumni Networking Event in San Francisco - Register Here
Launched with a generous gift from Wharton alumnus Art Bilger and his wife, Dahlia, and an executive partnership with Omnicom Group, Inc., the Wharton Customer Analytics Initiative (WCAI) is the preeminent academic research center focusing on the development and application of customer analytic methods. Acting as "matchmaker" between academia and industry, WCAI has a broad impact on the practice of data-driven business decision-making, and the dissemination of relevant insights to managers, students and policy makers.
Based in the Wharton School's Marketing Department and designed to capitalize on Wharton's longstanding strength in conducting empirical research, WCAI is an interdisciplinary effort that brings a passionate data-driven perspective unmatched by any other business school.
The Wharton Customer Analytics Initiative (WCAI):
- Is the thought leader in data-driven customer-level analysis, applying these methods in a wide range of industries including interactive media, financial services, pharmaceuticals, telecom, nonprofits, and other areas where the use of detailed customer-level datasets is a key driver for business success.
- Helps companies understand how to fully leverage the individual-level data they collect about their customers.
- Promotes the development of new analytics methods that are both rigorous and relevant.
- Translates customer analytics research findings (academic and otherwise), making them accessible to all managerial audiences, including high-level executives and analytics professionals.
What Is Customer Analytics?
Customer Analytics refers to the collection, management, analysis and strategic leverage of a firm's granular data about the behavior(s) of its customers. There are several features that distinguish customer analytics from other types of data collection/analysis.
Customer Analytics can be characterized as:
- Inherently granular: must be individual-level
- Forward-looking: orientation towards prediction not just description
- Multi-platform: combining behaviors from multiple measurement systems, but with best efforts to do so at the individual level
- Broadly applicable (and industry agnostic): consumers, donors, physicians, clients, brokers, etc.
- Multidisciplinary: marketing, statistics, computer science, information systems, operations research, etc.
- Rapidly emerging: traditionally viewed as just one form of "business analytics," but starting to take on its own unique identity as a "standalone" area of analysis and decision making
- Behavioral: many firms' customer analytics problems incorporate descriptors such as demographics and attitudes; but, the customer analytics' primary focus is on observed behavioral patterns
- Longitudinal: it's ALL about how these behaviors manifest themselves over time