If the election for U.S. Senate were held today, who would you vote for?
Smith (R)
43%
Roberts (D)
40%
Franklin (I)
3%
Undecided
14%
Crosstab Breakdown
Demographic
Smith (R)
Roberts (D)
Franklin (I)
Undecided
Most Important Issues
What is the most important issue facing your community today?
Crosstab Breakdown
Demographic
Economy
Immigr.
Health
Educ.
Crime
Taxes
Enviro.
Other
Message Testing
After hearing each message, are you more or less likely to support the candidate?
More Likely
No Difference
Less Likely
Top Performing Messages
53%
Tax Cuts
More Likely to Support
50%
Border Crisis
More Likely to Support
49%
Bureaucrats
More Likely to Support
Regression Analysis
Message effects on vote choice (logistic regression coefficients)
Message
Coefficient
Std Error
p-value
Significance
Tax Cuts
0.412
0.087
0.001
***
Border Crisis
0.381
0.091
0.003
***
Bureaucrats
0.294
0.094
0.012
**
Antisemitism
0.227
0.102
0.026
**
Tariffs
-0.048
0.098
0.624
n.s.
Domestic Drilling
0.011
0.099
0.912
n.s.
Double Talk
-0.102
0.096
0.287
n.s.
*** p < 0.01 ** p < 0.05 n.s. = not significant
Informed Ballot
Ballot numbers after message exposure (movement from initial ballot)
Smith (R)
47%
Roberts (D)
38%
Franklin (I)
3%
Undecided
12%
Smith: 43% -> 47% (+4)
Roberts: 40% -> 38% (-2)
Undecided: 14% -> 12% (-2)
Information Flow
Where do you get most of your political news and information?
Crosstab Breakdown
Demographic
Local TV
Social
Cable
Online
Radio
Print
Friends
Trend Analysis
Tracking key metrics across 6 survey waves -- June 2025 through January 2026
Horse Race Ballot Trend
Smith (R)Roberts (D)Undecided
Candidate Fav
52%62%
+10 pts
Opponent Unfav
36%44%
+8 pts (rising negatives)
Right Direction
33%42%
+9 pts
Wave-by-Wave Summary
Metric
Jun '25
Aug '25
Sep '25
Nov '25
Dec '25
Jan '26
Change
Smith Ballot
36%
38%
39%
41%
42%
43%
+7
Roberts Ballot
37%
38%
39%
40%
40%
40%
+3
Margin (Smith)
-1
Tied
Tied
+1
+2
+3
+4
Smith Fav
52%
55%
57%
59%
61%
62%
+10
Roberts Unfav
36%
38%
40%
41%
43%
44%
+8
Right Direction
33%
35%
37%
39%
40%
42%
+9
Undecided
27%
24%
22%
19%
18%
17%
-10
Methodology
Study design and data collection details
Sample Size
n = 1,570 Likely Voters
Field Dates
January 15-19, 2026
Margin of Error
+/-2.5% at 95% Confidence
Mode
IVR / Online Mixed Mode
Geography
Statewide
Universe
Registered Voters with Likely Voter Screen
Language
English and Spanish
Sponsor
Sample Campaign Committee
Sample Demographics
Demographic
Sample %
Republican
36%
Democrat
34%
Independent / Other
30%
Female
52%
Male
48%
18-34
18%
35-44
16%
45-54
19%
55-64
21%
65+
26%
White
62%
Black
14%
Hispanic
16%
Other
8%
Weighting Note
Data were weighted to match the expected electorate by party registration, gender, age, race/ethnicity, and geography using iterative proportional fitting (raking). The likely voter screen incorporated self-reported vote intention, past vote history, and stated interest level. All percentages reported are among likely voters unless otherwise noted.
Methodology
Study design and data collection details
Sample Size
n = 1,570 Likely Voters
Field Dates
January 15-19, 2026
Margin of Error
+/-2.5% at 95% Confidence
Mode
IVR / Online Mixed Mode
Geography
Statewide
Universe
Registered Voters with Likely Voter Screen
Language
English and Spanish
Sponsor
Sample Campaign Committee
Sample Demographics
Demographic
Sample %
Republican
36%
Democrat
34%
Independent / Other
30%
Female
52%
Male
48%
18-34
18%
35-44
16%
45-54
19%
55-64
21%
65+
26%
White
62%
Black
14%
Hispanic
16%
Other
8%
Weighting Note
Data were weighted to match the expected electorate by party registration, gender, age, race/ethnicity, and geography using iterative proportional fitting (raking). The likely voter screen incorporated self-reported vote intention, past vote history, and stated interest level. All percentages reported are among likely voters unless otherwise noted.
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Predictive Voter Modeling
Survey-grounded targeting at scale
SURVEY
1,500+ voters surveyed
Real opinions, not assumptions
PROFILE
500+ data points matched
Demographics, psychographics, consumer behavior
SCORE
Every voter scored 0-100
Individual-level predictions
ACTIVATE
Targetable universes delivered
Mail, digital, phones, doors
Survey Sample
1,500 voters surveyed
Target group identified by response pattern
Statistical modeling applied
Full Voter File
0
matching voters identified out of 650K in the district
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Target Segments
Example model types and scoring ranges
Persuadable Voters
Swing voters movable on your issues
040-65100
Message Movers
Most responsive to your specific messaging
055-80100
Issue Models
Pro-2A, pro-life, green energy, education
0varies100
Mobilization Targets
Supporters who need turnout motivation
070-95100
Opponent Soft Support
Opposition voters showing vulnerability
025-45100
"Most targeting starts with demographics and guesses what voters think. We start with what voters actually tell us -- then find everyone like them."
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Data Management
The foundation of every winning contact plan -- precision-built voter universes ready for activation across every channel.