Learn and apply analytic techniques to measure and improve marketing performance.
The University of Chicago’s eight-week, online Predictive Analytics for Marketing course will teach you how to analyze data; estimate campaign effectiveness; segment and size markets; and use predictive modeling to forecast customer lifetime value. Additionally, its holistic approach will enable marketing and sales teams to create better and more effective marketing campaigns, execute cross-selling and upselling more efficiently, and provide improved customer relations.
Designed for professionals interested in advancing their analytical acumen within the marketing discipline. Marketers, prospective marketers, managers, and other leaders who want to leverage analytic techniques to measure and improve marketing performance will benefit from the course.
Our Predictive Analytics for Marketing course focuses on optimal data analysis and predictive modeling. Over an eight-week period, you will gain the tools you need to extract insights to gain a better understanding of customers’ behavior, spending habits, and desires.
After completing the course, you will be able to:
- Effectively find and evaluate both internal and external sources of data
- Determine whether to buy an off-the-shelf market segment analysis or build a customer segmentation in-house
- Construct predictive equations for setting appropriate campaign sizing to meet financial targets
- Recommend the product mix to be used for marketing to each customer or prospect
- Measure individual campaigns toward long-term customer value
- Be awarded a certificate of completion from the University of Chicago and become part of the UChicago network
Online Learning Course Structure
- Eight weeks in length
- Weekly, self-paced interactive learning modules and assignments are time-sensitive and should be completed by the set deadlines
- Synchronous sessions and live question and answer sessions
- Mentors will provide continuous support and encourage a dynamic and positive learning environment
- Leverage data to target the right audience
- Appropriately segment audiences for optimal contact strategy
- Measure the impact of marketing campaigns
- Predict future customer purchases
- Forecast Customer Lifetime Value (CLV)
Weekly Online Course Schedule
Becoming familiar with the three case studies that will be used throughout the course: automotive (manufacturer), travel (cruise lines), and consumer packaged goods (retailer). Exploring sample targeted marketing campaigns, incremental revenue goals relative to advertising costs, and approaches to finding the appropriate audience.
Exploring potential data sources for three case studies. Determining how to define the marketing question for each using frequency of purchase, value of data relative to cost, and data specific to a use case versus across use cases.
Introducing off-the-shelf segmentation. Sketching out rule-based segmentation for each use case: product-based, location-based, and market basket-based. Determining how to apply different contact strategies for each use case: banner ads, email, direct mail, and apps.
Introducing k-means segmentation. Demonstrating a sample segmentation using Excel. Translating from customer segmentation to activation in each use case: product-based, location-based, and market basket-based.
Introducing marginal return on investment. Exploring techniques for appropriate audience sizing. Examining theory on linear versus logistic regression tying back to the three use cases: infrequent purchase, annual purchase possibility, and weekly purchase.
Introducing expansion from previous models to multiple product lines. Sharing techniques for appropriate audience sizing. Determining whether a contact strategy is appropriate at a high level across use cases.
Introducing expansion from campaign focus to lifetime value. Sharing techniques for appropriate audience sizing. Determining whether there is appropriate lifetime value across use cases.
Tying it all together into a coherent story for each use case. Sharing the latest in data ethics and privacy. Considering inherent error in predictive modeling and the associated risks.