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Predictive Analytics for Marketing

Learn and apply analytic techniques to measure and improve marketing performance.

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Predictive analytics

At a Glance

Type
Courses
Enrollment
Open Enrollment
Duration
8 weeks
Format
Online
Cost
$2,500

Upcoming Dates

Course start

Program Details

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Learn and apply analytic techniques to measure and improve marketing performance.

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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

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

Learning Objectives

  • 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 Schedule

Introduction to Targeted Marketing Campaigns

Module 1

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.

Sources of Data

Module 2

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.

Segmenting the Audience—Part I

Module 3

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.

Segmenting the Audience—Part II

Module 4

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.

Determining Audience Sizing for Positive Return on Investment (ROI)

Module 5

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.

Using Analytics to Optimize Product Offer

Module 6

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.

Measuring Customer Lifetime Value

Module 7

Introducing expansion from campaign focus to lifetime value. Sharing techniques for appropriate audience sizing. Determining whether there is appropriate lifetime value across use cases.

Putting It All Together

Module 8

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.

Meet Your Instructor

David Cameron

David Cameron, MS

Data Science and Predictive Analytics Leader

Dave Cameron is a seasoned data science and predictive analytics leader in both the corporate and startup spaces. He brings over twenty-five years of experience to the University of Chicago. As a forward-thinking executive, Cameron has built and led teams on analytics thought leadership, statistical and product innovation, data-driven performance marketing, and attribution modeling for consumer acquisition, retention, and profit. 

He spent over twelve years at Nielsen Holdings, the global measurement and data analytics company, where he served in vice presidential roles in data science, customer segmentation, and statistical methodology. His prior positions include working as Director of Data Solutions at Merkle, a global performance marketing agency, and his role as Manager of Target Marketing for Ameritech (now AT&T). 

Cameron takes a strategic and pragmatic approach to solving challenging data problems, balancing learning and innovation with efficiency and automation. He holds an MSc in Applied Statistics from Ohio State and a BS in applied mathematics in computer science from University of Detroit Mercy.

 

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