Scalable Target Marketing with Big Data

Modern marketing often requires that firms tailor their strategies to the characteristics of specific customers to sell their products and services. Examples include decisions on which online advertisement to show, what price to charge, or which promotion to offer. However, current statistical methods are not designed to scale to the size of modern data sets. I present a new algorithm to close that gap. The method takes advantage of supercomputer. The key takeaway is that firms can now provide a win-win for their target customers. Customers win by having fewer annoying messages they need to process from firms, and the messages they do receive are spot-on in terms of meeting their needs. Firms win by increasing the efficiency of their marketing efforts at a reduced cost, earning larger returns on their smaller marketing budgets. I demonstrate the method for a charitable organization that wants to more efficiently target potential donors. Using the new algorithm, I predict an increase of $1.6 million to $4.2 million in incremental revenue per campaign, more than the amount of revenue using a traditional statistical approach.

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Tuesday, December 7, 2021 at 8:00am to 9:30am

Virtual Event
Event Type

Lecture/Presentation

Interests

Business

Audience

Alumni

College, School & Unit

Business

Tags

Alumni, reputation

Website

https://cuboulder.zoom.us/meeting/reg...

Group
Leeds School of Business
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