Persuasion Analytics and Targeting
This course will teach you how to apply predictive modeling methods to identify persuadable individuals and to target voters in political campaigns.
Overview
In this course you will learn you how to apply predictive modeling methods with a focus on persuasion (uplift) models and how to target voters in political campaigns. You will cover which aspects of campaigns are most important, and the difference between traditional targeting and micro-targeting techniques. You will also learn what to measure, how to design appropriate surveys, the role of experiments and how to account for the impact of advertising.
Note: This course is also the lab component for students pursuing a Programming for Data Science Certificate.
- Intermediate
- 4 Weeks
- Expert Instructor
- Tuiton-Back Guarantee
- 100% Online
- TA Support
Learning Outcomes
In this course students will learn how to find appropriate voter targets, design survey instruments, and assess the effectiveness of voter contacts. You will implement various predictive models and testing methods to choose optimal campaign messages and media.
- Find voter targets that are appropriate to a campaign phase
- Assess the effectiveness of a voter contact
- Designing a survey instrument for use in a predictive model
- Implement predictive models with voter data
- Add uplift modeling to predict whether a voter responds better with a “treatment”
- Conduct an A-B test
- Include test or no-test indicator as a predictor
Who Should Take This Course
Data analysts who are familiar with predictive modeling and want to learn persuasion modeling, and how to apply it and predictive modeling in general, especially in the political world. Political consultants and staff who have had some exposure to predictive modeling, and want to dive deeper and learn how it is applied in a political campaign.
Our Instructors
Mr. Ken Strasma
Course Syllabus
Week 1
Background and Basic Campaign Concepts
- Why campaigns need to target
- Phases of a campaign
- Finding the right targets for the right phase
- Calculating the effectiveness of a voter contact
Week 2
Traditional Targeting vs. Individual Level Modeling and Beginning the Modeling Process
- Traditional targeting
- Micro-targeting – shifting the focus to the individual
- Deciding what to predict
- Survey instrument design
- The modeling process
Week 3
The Modeling Process in Detail
- Common pitfalls
- Missing values
- Building new indicators
- Evaluating models
- Combining models
Week 4
Persuasion (uplift) Modeling
- Controlled and natural experiments
- Combining A-B test with predictive modeling
- Persuasion: determining for whom the message works
- Targeting for broadcast television
- Targeting for online advertising
Class Dates
2023
Instructors:
2024
Instructors:
Instructors:
2025
Instructors:
Prerequisites
Predictive Analytics 1 – Machine Learning Tools
- Skill: Intermediate
- Credit Options: ACE, CEU
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Additional Information
Course Text
All materials will be provided during the course.
Software
To do the exercises in the course you will need access to and some familarity with data mining software.
For Certificate Students
Software use depends on whether you signed up for Persuasion Analytics or Applied Predictive Analytics, which is the version of the course that serves as a capstone project for students in the Programming for Data Science Certificate.
The data and exercises for Persuasion Analytics students are geared to minimize the issues with data handling, and facilitate the use of XLMiner, an Excel add-in, to allow students to focus on the statistical concepts being taught in the course.
The data and exercises for Applied Predictive Analytics students bring out the issues of data size and data handling, and require the use of R or Python.
You can choose either track once you are in the class. If you are familiar with R and Python and want to grapple with the data issues in this course, you could select the R/Python track. Otherwise, you should choose the XLMiner track.
Options for Credit and Recognition
ACE CREDIT | College Credit
This course has been evaluated by the American Council on Education (ACE) and is recommended for the upper-division baccalaureate/associate degree, 3 semester hours in data mining or computer science. Please note that the decision to accept specific credit recommendations is up to the academic institution accepting the credit.
Supplemental Information
Literacy, Accessibility, and Dyslexia
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