BlueLabs
www.bluelabs.com
@Blue_Labs
Predictive Analytics:
Impact
Field Programs:
Determine priority targets for
volunteer contact and Election day
turn out.
D...
Support
Turnout
Volunteer / Donate
Persuasion
Message / Issue
Channel
• Likelihood to support Democrat
• Likelihood to vot...
Example: Modeling Support
1. Collect Data
Select a random sample of
voters from the population.
Example: Modeling Support
1. Collect Data
Select a random sample of
voters from the population.
Example: Modeling Support
1. Collect Data
Collect relevant data on a
sample voters.
“Who are you voting for?”
Example: Modeling Support
1. Collect Data
D RDR
DDR R
Collect relevant data on a
sample voters.
“Who are you voting for?”
Example: Modeling Support
1. Collect Data
D RDR
DDR R
Collect relevant data on a
sample voters.
“Who are you voting for?”
...
Example: Modeling Support
1. Collect Data
D RDR
DDR R
Collect relevant data on a
sample voters.
“Who are you voting for?”
...
Example: Modeling Support
1. Collect Data
D RDR
DDR R
Collect relevant data on a
sample voters.
“Who are you voting for?”
...
Using in cycle testing and experiments, we build experimentally-
informed models that predict who is most likely to change...
Persuasion Scores ID Targets at
individual level
Strong TargetsAvoid Weak Targets
Example Likely Voter Universe by Persuas...
X%
Example: Targeting with the persuasion score
in VA
Independents
Random
Voters
High Persuasion
Scores
316 thousand
likel...
Case Studies
Wendy Davis: Texas 2014 Gubernatorial
Problems faced by Texas
• Big state with sparse targets
• Requires balance of
– Regi...
Terry McAuliffe - Virginia 2013
Gubernatorial
30 40 50 60 70
McAuliffe 2−Way %
VA Expected McAuliffe Support
Fully Integrated Analytics
Program
Analytics Program
Support Models
Turnout Models
GOTV Model
Persuasion Model
Undecided M...
Optimizing Field
ContactsOur modeling in VA in 2013 improved field program’s GOTV targeting in October GOTV
by over 20% co...
Data Driven Decision Making
Where we put offices Where we sent canvassers
1. Build Model 2. Match Targets 3. Media Optimizer
Match list of targets with
set-top box or online data +
cost per progra...
Persuasion and GOTV targets allow specific targeting on social media and ads
Individual Level Targeting & Social Media/Onl...
Changing Minds:
Persuasion Case Study:
In VA in 2013, our field persuasion program
alone succeeded in reaching an estimate...
Scaling to State Campaigns: Virginia
2014
In 2014, we helped 2 Democrats win special election races for state
senate in Vi...
Scaling to State Campaigns: NC-12 in
2014
In NC-12, Alma Adams faced a crowded primary
election in an electorate that had ...
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Aapc deck intro to modeling draft 6.6.14

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  • Waples mill
  • Aapc deck intro to modeling draft 6.6.14

    1. 1. BlueLabs www.bluelabs.com @Blue_Labs
    2. 2. Predictive Analytics: Impact Field Programs: Determine priority targets for volunteer contact and Election day turn out. Direct Mail: Determine priority households to receive issue-specific mail. Social Media: Target online persuasion efforts toward the most persuadable voters. Television Advertising Produce a list of efficient buys using the TV optimizer based on what persuadable voters are watching. Predictive Analytics
    3. 3. Support Turnout Volunteer / Donate Persuasion Message / Issue Channel • Likelihood to support Democrat • Likelihood to vote in the election • Likelihood to volunteer or donate • Likelihood to switch vote to Dem after exposure to campaign message • Identify the most effective message • Identify most effective channel or tactic to influence voter behavior Who to target What to say How to contact Core campaign data models What the data models predict Applications Experiments • Test real-world interventions to evaluate the impact of programs Informed using real world tests Modeling Opinions and Behaviors
    4. 4. Example: Modeling Support 1. Collect Data Select a random sample of voters from the population.
    5. 5. Example: Modeling Support 1. Collect Data Select a random sample of voters from the population.
    6. 6. Example: Modeling Support 1. Collect Data Collect relevant data on a sample voters. “Who are you voting for?”
    7. 7. Example: Modeling Support 1. Collect Data D RDR DDR R Collect relevant data on a sample voters. “Who are you voting for?”
    8. 8. Example: Modeling Support 1. Collect Data D RDR DDR R Collect relevant data on a sample voters. “Who are you voting for?” 2. Build Model Under 30 Union Member Hispanic Build statistical model to identify significant data points Hunter 40-49 years old Registered Republican Using data from a voter file, appended to additional data sources, we identify characteristics that are correlated with support of the Democratic candidate.
    9. 9. Example: Modeling Support 1. Collect Data D RDR DDR R Collect relevant data on a sample voters. “Who are you voting for?” 2. Build Model Under 30 Union Member Hispanic Build statistical model to identify significant data points Hunter 40-49 years old Registered Republican 3. Predict Outcome In the original universe, predict likelihood of support for each individual.
    10. 10. Example: Modeling Support 1. Collect Data D RDR DDR R Collect relevant data on a sample voters. “Who are you voting for?” 2. Build Model Under 30 Union Member Hispanic Build statistical model to identify significant data points Hunter 40-49 years old Registered Republican 3. Predict Outcome In the original universe, predict likelihood of support for each individual.
    11. 11. Using in cycle testing and experiments, we build experimentally- informed models that predict who is most likely to change their vote Next Generation: Combining Modeling & Testing 1. Conduct Experiment 2. Build Model D D Treatment Control 3. Predict Outcome R D R DR R R
    12. 12. Persuasion Scores ID Targets at individual level Strong TargetsAvoid Weak Targets Example Likely Voter Universe by Persuasion Score Persuasion Score Lift
    13. 13. X% Example: Targeting with the persuasion score in VA Independents Random Voters High Persuasion Scores 316 thousand likely voter targets 1,683 persuaded voters 0.5% 1.2% 3.9% 316 thousand likely voter targets 316 thousand likely voter targets 3,738 persuaded voters 12,193 persuaded voters • Modeling increased the efficiency of the persuasion program by a factor of 7 • The number of voters persuaded represents nearly a 25,000 vote swing Targets Call Capacity % Impact Votes won
    14. 14. Case Studies
    15. 15. Wendy Davis: Texas 2014 Gubernatorial Problems faced by Texas • Big state with sparse targets • Requires balance of – Registration – Persuasion – GOTV
    16. 16. Terry McAuliffe - Virginia 2013 Gubernatorial 30 40 50 60 70 McAuliffe 2−Way % VA Expected McAuliffe Support
    17. 17. Fully Integrated Analytics Program Analytics Program Support Models Turnout Models GOTV Model Persuasion Model Undecided Model Media Optimization Direct Mail EIP Models Tracking Polls Race/Ethnicity Models Analytics Tech Embedded Analytics Staff Campaign Strategy Field Program TV Advertising Direct Mail Polling Volunteer Recruitment Resource Allocation McAuliffe Win
    18. 18. Optimizing Field ContactsOur modeling in VA in 2013 improved field program’s GOTV targeting in October GOTV by over 20% compared to the most recent election, helping volunteers reach more strong Democrats and fewer Republicans and undecideds. -25% -20% -15% -10% -5% 0% 5% 10% 15% 20% 25% Strong Democrat Lean Democrat Undecided / Republucan Change in targets reached in late October as compared to 2009
    19. 19. Data Driven Decision Making Where we put offices Where we sent canvassers
    20. 20. 1. Build Model 2. Match Targets 3. Media Optimizer Match list of targets with set-top box or online data + cost per program estimates Optimizer produces list of efficient ad buys, with detail for specific programs/sites in key markets on specific days Use models to identify the individuals we want to reach with television ad buys $ $ $ $ $ Reach 18% more targets or spend 18% less money Optimizing Ad Buys – Maximizing Impact
    21. 21. Persuasion and GOTV targets allow specific targeting on social media and ads Individual Level Targeting & Social Media/Online Ads
    22. 22. Changing Minds: Persuasion Case Study: In VA in 2013, our field persuasion program alone succeeded in reaching an estimated 4x persuasion effect compared to traditional targeting. The program persuaded about 12,500 additional voters to support Terry McAuliffe, netting an estimated 25,000. Since many of these voters would have likely voted for the Republican candidate, the actual effect on the vote margin was much larger— which is especially significant given that the entire winning margin for McAuliffe was 56,435. 7x
    23. 23. Scaling to State Campaigns: Virginia 2014 In 2014, we helped 2 Democrats win special election races for state senate in Virginia by helping identify and mobilize their strongest targets with high accuracy. Holding these two seats was the difference between Democrats maintaining or losing the majority. SD-6: Lynwood Lewis SD-3: Jennifer Wexton Our modeled turnout: 21.9% Actual turnout: 22.5% Our modeled turnout: 20.2% Actual turnout: 20.4%
    24. 24. Scaling to State Campaigns: NC-12 in 2014 In NC-12, Alma Adams faced a crowded primary election in an electorate that had not tuned in yet and were still largely undecided. Partnering with EMILY’s List and Diane Feldman, and using a new analytical approach requiring smaller scale data collection efforts, we constructed a universe of voters receptive to Alma’s message. This universe allowed EMILY’s List to construct an effective mail program that was cost effective and targeted at the voters most open to Alma’s message. Alma won the primary with over 40% of the vote, securing her the Democratic nomination. 1. Messaging 2. Small scale test 3. Create targeted universe 4. Mail 5. Win

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