WPA Predictive Analytics Capabilities


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WPA Predictive Analytics Capabilities

  1. 1. WPA Opinion Research Predictive Analytics Capabilities Chris Wilson CEO & Partner 202.470.6300 CWilson@WPAResearch.com
  2. 2. Page 2 What are we doing? We are making predictions about the behavior of individual voters. In particular, we are predicting behavior on four key variables: •Initial Likelihood to Vote •Initial Vote •Informed Vote •Informed Likelihood to Vote We build each of these predictions using demographic, behavioral, and consumer information.
  3. 3. Page 3 How are we doing this? We use regression models, specifically an Ordered Stepwise Probit model, to make these predictions. •Starts with all available information •Narrows down the available variables to only those that are useful predictors We start with 200+ variables covering demographics, consumer information, and behavioral information. •Demographic: County, Age, Gender, Veteran Status, Head of Household, Presence of Children, Religion, etc. •Behavior: Art Collectors, Read Science Fiction books, Interest in camping or hiking, Follow current affairs/politics, Golfers, Investors, Interest in hunting, etc. •Consumer: Purchased craft products, Have American Express card, Buy electronics like computers, Purchased clothing (women’s, men’s, children’s), etc.
  4. 4. Page 4 Types of Variables These are the types of fields we have access to for variables to run predictive analytics
  5. 5. Page 5 Examples of Variables Demographic Variables Consumer & Behavioral Variables
  6. 6. Page 6 How the Variables are Used All of the variables we use are necessary. •They give us a very detailed picture of the voters. •It allows us to accurately predict the behavior of other voters who share similar characteristics. •Other data from the campaign can be used if there is sufficient data (i.e. donors, volunteers, etc.) Steps in the Model: •Use every variable we have to build an initial model based on the campaign’s observed data to predict the behavior under study. •Use an Ordered Stepwise Probit model to narrow down all the variables by running a series of regressions to determine which variables are the most effective at predicting the behavior. •The finalized model is applied back to the full list of voters to calculate a prediction for each voter.
  7. 7. Page 7 What the Data Means The data going back to the campaign is a prediction of each voter’s behavior. •Every voter on the file has a number from 0 (zero) to 1 (one). •That number is their prediction score for that behavior. •The higher the value the more likely that individual voter is to perform that particular behavior.
  8. 8. Page 8 Using the Data: Examples Voter Likelihood to Vote for the candidate Voter #1 0.8 Voter #2 0.2 Here, Voter #1 is much more likely to vote for the candidate than Voter #2 is. Voter Change on Vote Likelihood Voter #3 0.8 to 0.85 Voter #4 0.6 to 0.75 Here, Voter #3 is already very likely to vote for the candidate and probably does not need additional contact. Voter #4 though can be moved from “persuadable” to “likely” and should be contacted. Voter Likelihood to Vote for the candidate Likelihood to Vote Voter #5 0.8 to 0.85 0.2 Voter #6 0.8 to 0.85 0.8 Here, both voters are very likely to vote for the candidate…if they vote. The campaign should focus on convincing Voter #5 about the importance of voting and Voter #6 should be targeted in GOTV efforts.
  9. 9. Page 9 Using Across the Campaign The data can be used across the campaign for a variety of purposes: •Door to Door Walkers: May be more concerned about convincing people to vote for the candidate. So they are going to want to focus on “persuadable” voters identified by the initial model who had larger movements toward the candidate on the informed model. •Fundraisers: May be more interested in voters who are already likely to support the candidate. They won’t have to convince them why he is the right candidate – they can focus on why and how their donations will help re-elect the candidate. •Ad Buyers: May be interested in the demographic makeup of “persuadable” voters so they can place the right buys. The individual level data can be aggregated back to the population to help identify the best “groups” of voters to target.
  10. 10. Page 10 Using Across the Campaign The data can even be analyzed across models. •Did more voters move toward the candidate on model #2 which was run a month after model #1? •Who are these voters and what theories do we have about why they are moving toward or away from the candidate? •How can we continue the momentum or turn back the tide? All of this data can be uploaded to the campaign’s system. •This gives every department access to the data so they can pull lists of voters they care about rather than trying to wade through the voters on the file.
  11. 11. Page 11 Additional Uses for Modeling This type of analysis is not limited to vote behavior. It can be used to predict any behavior the campaign is interested in. •So long as there is enough data from the campaign about a behavior they are interested in we can model it. •Examples include: •Donations: Based on who has already donated to the campaign, who else is likely to donate? •Volunteers: Based on who is already volunteering, who are others that could be recruited to volunteer? •Yard Signs: Based on who has requested a yard sign, who else might be interested in displaying a sign if asked? •Events: Based on who has attended previous campaign events, which voters might be likely to attend a campaign event? Which of these voters are also likely donors or volunteers?
  12. 12. Page 12 How we will interact with the Data How will we interact with the Campaign? •The campaign will send us the information they have for the behavior they are interested in modeling. •We will clean the data and add all of the demographic, behavior, and consumer information obtained from Aristotle. •We will run our models to predict the behavior. •After the predictions are complete we will send the predictions back to the campaign along with each voters Voter ID number to upload back to their system.
  13. 13. Page 13 CONTACT
  14. 14. For additional information, please feel free to contact: Chris Wilson CEO & Partner 202.470.6300 CWilson@WPAResearch.com