Presented by: Eric Bradlow
K.P. Chao Professor, Professor of Marketing, Statistics, Education and Economics
Targeting individual consumers has become a hallmark of direct and digital marketing as it becomes ever easier to identify people’s repeated interactions with a company. But because there are so many tracking technologies and environments in which customers interact with companies, some customers cannot be consistently identified. The result is that companies’ CRM databases end up with a high number of anonymous visits. How can we turn this data into actionable information?
We have developed a statistical approach that can help companies assign these anonymous visits to existing and new customers, taking into account customer demographic information as well as the frequency and nature of interactions. In this webinar, we present studies that show this approach makes accurate inferences about individual customer preferences and their responsiveness to marketing tactics. Companies who use our method will be better able to use anonymous visit data to their advantage by targeting individual consumers and identifying potential new customers.
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