BlueLabs uses predictive analytics to target voters for field programs, direct mail, social media, and television advertising. Their models predict voter likelihood of supporting Democrats, voting, volunteering/donating, and changing their vote. They identify the most effective messages and tactics for different voter segments. Experiments test model predictions to refine targeting. Case studies show their approach increased persuasion and turnout effects in elections in Texas, Virginia, and North Carolina.

1. BlueLabs
www.bluelabs.com
@Blue_Labs

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. 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. Example: Modeling Support
1. Collect Data
Select a random sample of
voters from the population.

5. Example: Modeling Support
1. Collect Data
Select a random sample of
voters from the population.

6. Example: Modeling Support
1. Collect Data
Collect relevant data on a
sample voters.
“Who are you voting for?”

7. Example: Modeling Support
1. Collect Data
D RDR
DDR R
Collect relevant data on a
sample voters.
“Who are you voting for?”

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. 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. 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. 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. Persuasion Scores ID Targets at
individual level
Strong TargetsAvoid Weak Targets
Example Likely Voter Universe by Persuasion Score
Persuasion Score
Lift

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. Case Studies

15. Wendy Davis: Texas 2014 Gubernatorial
Problems faced by Texas
• Big state with sparse targets
• Requires balance of
– Registration
– Persuasion
– GOTV

16. Terry McAuliffe - Virginia 2013
Gubernatorial
30 40 50 60 70
McAuliffe 2−Way %
VA Expected McAuliffe Support

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. 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. Data Driven Decision Making
Where we put offices Where we sent canvassers

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. Persuasion and GOTV targets allow specific targeting on social media and ads
Individual Level Targeting & Social Media/Online
Ads

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. 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. 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