Key Takeaways
- Voting history data reveals which voters are most likely to participate in your election, allowing you to allocate resources up to 3x more efficiently than demographic targeting alone.
- Vote propensity scores — calculated from past turnout patterns across election types — help you prioritize outreach to voters with 70%+ likelihood of voting while deprioritizing those under 30%.
- Super voters (those who vote in 4+ of the last 5 elections) represent only 15-20% of registered voters but account for 60-70% of actual turnout in midterm and local elections.
- Combining voting history with demographic and geographic data creates multi-dimensional voter profiles that improve contact efficiency by 40-60% compared to single-variable targeting.
Understanding How to Use Voting History Data to Transform Your Campaign Strategy
Voting history data is the most powerful predictive tool available to political campaigns in 2026, yet many campaign managers still rely primarily on demographic assumptions when building their voter contact strategies. When you learn how to use voting history data effectively, you can identify likely voters with 75-85% accuracy, reduce wasted contact attempts by 40-60%, and allocate your limited resources to the voters who will actually determine your election outcome.
Voting history data shows which elections each registered voter participated in over the past decade or more. This participation record — available through state voter files and platforms like MailVotes — reveals behavioral patterns that demographics alone cannot predict. A 35-year-old suburban parent might fit your target demographic perfectly, but if they’ve only voted once in the last five elections, they’re far less valuable to your campaign than a 68-year-old retiree who votes in every single election.
The difference between campaigns that win and campaigns that waste resources often comes down to this fundamental insight: voter behavior predicts voter behavior better than any other variable. In the 2026 election cycle, campaigns that master voting history analysis consistently outperform those that don’t by 15-25 percentage points in voter contact efficiency.
What Voting History Data Reveals About Your Electorate
Voter turnout history provides a comprehensive view of electoral participation patterns that transforms how you understand your district. Each voter’s file typically includes participation records for the last 10-20 elections, coded by election type: presidential general elections, midterm general elections, primary elections, special elections, and local/municipal elections.
This election participation data shows you three critical patterns. First, it reveals participation frequency — how often someone votes relative to the number of elections they could have voted in. A voter who participated in 8 out of 10 possible elections demonstrates fundamentally different behavior than one who voted in 2 out of 10. Second, it shows election type preferences — some voters only show up for presidential elections, while others vote in every local school board race. Third, it reveals participation trends over time — whether someone is becoming more engaged or dropping off.
The most valuable insight from voting history is the identification of super voters. These individuals vote in 80-100% of elections regardless of type or national attention. Super voters represent approximately 15-20% of registered voters in most jurisdictions but account for 60-70% of actual turnout in midterm and local elections. In a local city council race, super voters might represent 80-85% of your actual electorate.
Conversely, voting history identifies sporadic voters who participate unpredictably, occasional voters who only engage in high-profile races, and non-voters who registered but have never cast a ballot. According to 2026 voter file analysis across swing states, approximately 35-40% of registered voters haven’t participated in any election in the past four years. These voters drain campaign resources if you treat them the same as reliable participants.
How Vote Propensity Scores Work and Why They Matter
Vote propensity scores translate raw voting history into actionable likelihood predictions. A vote propensity score is a numerical value (typically 0-100 or 0-5) that represents the probability a specific voter will participate in your election based on their historical participation patterns in similar election types.
A volunteer engages in a meaningful one-on-one conversation with a voter at their doorstep.
The calculation behind likely voter modeling combines several weighted factors. Primary election participation receives the highest weight because primary voters demonstrate the strongest civic engagement — someone who votes in primaries has an 85-90% likelihood of voting in the corresponding general election. Recent participation matters more than distant history, so votes in the last two cycles count more heavily than participation from eight years ago. Election type matching is crucial — a voter’s participation in previous midterm elections is the strongest predictor of participation in your midterm race.
Modern propensity models in 2026 also incorporate participation consistency (voting in consecutive elections indicates stronger habits than scattered participation), election competitiveness (voters are more likely to participate when they perceive races as close), and declining or increasing trends in an individual’s participation pattern.
A typical propensity scoring system might classify voters as follows: Score 5 (90-100% likelihood) — voted in 4+ of last 5 elections including primaries; Score 4 (70-89% likelihood) — voted in 3+ of last 5 elections; Score 3 (50-69% likelihood) — voted in 2 of last 5 elections; Score 2 (30-49% likelihood) — voted in 1 of last 5 elections; Score 1 (0-29% likelihood) — no participation in last 5 elections.
Your campaign strategy should allocate resources proportionally to these scores. Score 5 voters receive maximum contact frequency through multiple channels. Score 4 voters get regular contact with emphasis on mobilization rather than persuasion. Score 3 voters receive targeted persuasion and mobilization. Score 2 voters get limited contact focused on high-impact moments. Score 1 voters receive minimal or no contact unless they meet other high-value criteria.
Building Your Voter Targeting Strategy Around Historical Turnout
The foundation of effective voter targeting in 2026 starts with segmenting your universe by vote propensity. Begin by pulling voter file data for your district and calculating propensity scores for every registered voter. Platforms like MailVotes provide pre-calculated propensity scores along with raw voting history, saving weeks of analysis time.
Next, create distinct contact strategies for each propensity tier. Your highest-propensity voters (scores 4-5) should receive 60-70% of your total contact attempts despite representing only 25-35% of registered voters. These are your base voters and persuadable high-turnout voters — the people who will actually decide your election. Medium-propensity voters (score 3) receive 20-30% of contact attempts with messaging focused on motivation and issue persuasion. Low-propensity voters (scores 1-2) receive only 5-10% of contact attempts, concentrated in the final two weeks before Election Day.
This resource allocation contradicts many traditional campaign strategies that attempt to contact every registered voter equally. But the math is undeniable: if you have capacity to make 10,000 voter contacts and you spread them evenly across 30,000 registered voters, you’ll contact each voter 0.33 times on average. If you concentrate those 10,000 contacts on the 8,000 highest-propensity voters, you’ll average 1.25 contacts per likely voter — enough to make a measurable impact.
Combine voting history with other targeting variables for maximum effectiveness. Layer propensity scores with party registration to identify persuadable high-turnout voters from the opposing party or independents. Cross-reference with demographic targeting strategies to find high-propensity voters within your key demographic groups. Add geographic filters to identify high-turnout precincts where your message resonates best.
For example, a local school board campaign might target: super voters (score 5) with children in district schools, high-propensity independent voters (score 4) in swing precincts, and medium-propensity voters (score 3) who voted in the last school board election. This multi-dimensional approach combines behavioral data with demographic and issue-based targeting for superior results.
Identifying and Mobilizing Your Base Using Participation Patterns
Your base voters are those who share your party affiliation or ideological leanage AND demonstrate high vote propensity. These voters are your campaign’s foundation — they agree with you and they actually vote. Identifying your base using voting history data involves filtering for voters with both partisan alignment and participation frequency.
Start by pulling all registered voters who match your party registration (for partisan races) or who live in areas with strong historical support for candidates like you (for non-partisan races). Then filter this list by propensity score, keeping only those with scores of 3 or higher. This gives you your base universe — voters who support candidates like you and who actually show up.
Analyze your base’s participation patterns across election types. Do your base voters participate consistently in primaries? Do they drop off in local elections? Understanding these patterns helps you predict your base turnout and identify base voters who need mobilization reminders despite their generally high propensity.
For base mobilization, voting history data tells you which voters need minimal contact (super voters who never miss an election) versus which need moderate mobilization (voters who support you but sometimes skip your election type). A Republican congressional candidate might find that their base includes super voters who need only a single reminder postcard, plus reliable general election voters who skip primaries and need 2-3 mobilization contacts.
Don’t waste resources over-contacting your most reliable base voters. A super voter who has voted in 12 consecutive elections doesn’t need five door knocks and ten phone calls. One quality contact confirming their support is sufficient. Redirect those saved resources to medium-propensity base voters who need motivation or to high-propensity persuadable voters who could swing your way.
Finding Persuadable Voters Who Actually Show Up
The most valuable voters in any campaign are persuadable high-propensity voters — people who haven’t decided how they’ll vote but who definitely will vote. Voting history data helps you find these needles in the haystack by identifying voters who participate regularly but don’t have strong partisan indicators.
Start with voters who have propensity scores of 4 or 5 (high likelihood of voting) but who are registered as independents, no party affiliation, or in swing precincts that regularly split their votes. These are voters with the civic engagement to participate but without hardened partisan loyalty.
Analyze their participation patterns for clues about persuadability. Voters who participate in primaries are often more ideologically committed and harder to persuade than those who only vote in general elections. Voters who consistently vote but live in competitive precincts are more likely to be genuinely persuadable than those in landslide precincts.
Combine voting history with other data sources to refine your persuadable universe. Cross-reference high-propensity independents with demographic data that suggests openness to your message. Layer in geographic data showing swing precincts. Add consumer data or survey results if available. The goal is to identify voters who meet three criteria: they will definitely vote, they haven’t firmly decided, and they’re reachable with your message.
Allocate your persuasion budget heavily toward these high-propensity persuadables. A campaign with limited resources should spend 50-60% of their persuasion efforts on high-propensity persuadables, 30-40% on medium-propensity persuadables, and only 10-20% on low-propensity voters who claim to be undecided. The math is simple: persuading someone who won’t vote is worthless, while persuading someone who will definitely vote is campaign gold.
Optimizing Your Door-to-Door Canvassing With Turnout Data
Door-to-door canvassing is the most expensive voter contact method per interaction, making it essential to optimize your walk lists using voting history data. The difference between a canvassing operation that knocks random doors and one that targets high-propensity voters is the difference between 15% contact rates and 45% contact rates.
Build your walk lists by filtering for propensity scores of 3 or higher within your target universe. If you’re running a persuasion canvass, target high-propensity persuadables. If you’re running a base mobilization canvass, target medium-to-high-propensity base voters. Never waste canvasser time knocking doors of voters with propensity scores below 2 unless you have unlimited resources.
Organize walk lists by precinct-level turnout history to maximize efficiency. Precincts with 65%+ turnout in similar past elections should receive priority over precincts with 40% turnout. Your canvassers will complete more quality conversations per hour in high-turnout areas, and those conversations will be with voters who actually matter.
Use voting history to inform your canvassing message and approach. When knocking doors of super voters, your script should acknowledge their consistent participation: “I can see you vote in every election, and I wanted to make sure you knew about…” This recognition builds rapport and respects their engagement. For medium-propensity voters, your message should emphasize the importance of this particular election and why their participation matters.
Track canvassing results by propensity score to validate your targeting. You should see significantly higher contact rates and more productive conversations among high-propensity voters compared to low-propensity voters. If you’re not seeing this pattern, your propensity model needs refinement or your voter file data needs updating. Learn more about tracking canvassing data effectively to measure these results.
Creating Targeted Direct Mail Campaigns Based on Vote History
Direct mail remains one of the most cost-effective voter contact methods in 2026, and voting history data makes it exponentially more powerful. When you build effective voter mailing lists using turnout data, your mail reaches voters who will actually see a ballot, dramatically improving return on investment.
Segment your mail program by propensity score to deliver different message frequencies and creative approaches. Super voters (score 5) might receive 2-3 mail pieces total — one early persuasion piece and one mobilization reminder. High-propensity voters (score 4) receive 3-4 pieces with increased emphasis on your core message. Medium-propensity voters (score 3) receive 4-5 pieces with both persuasion and mobilization messaging. Low-propensity voters receive minimal mail unless they meet other high-value criteria.
Customize your mail creative based on participation patterns. For voters who participate in every election, use messaging that respects their civic engagement: “As someone who votes in every election, you understand that local decisions matter.” For voters who participate in presidential elections but skip midterms, use messaging that elevates the importance of your race: “This election will impact your daily life more than any presidential race.”
Time your mail drops based on when different propensity segments typically engage with elections. Super voters pay attention early and often — they can receive mail 6-8 weeks out. Medium-propensity voters engage later — concentrate mail in the final 3-4 weeks. Low-propensity voters who do participate often decide in the final week — if you mail them at all, do it in the last 7-10 days.
Test different approaches by propensity segment to optimize future campaigns. Run A/B tests on message, design, and frequency within each propensity tier. You’ll often find that high-propensity voters respond to substantive policy content while medium-propensity voters respond better to emotional appeals or social pressure messaging. These insights compound over multiple election cycles.
Platforms like MailVotes allow you to build precisely targeted mailing lists filtered by propensity score, party, demographics, and geography, then export them directly for mail house processing. This integration of data analysis and execution saves weeks of manual list building.
Analyzing Election Type Patterns to Predict Your Turnout
Not all elections are created equal in the eyes of voters, and voting history data reveals these preferences clearly. Understanding election type patterns helps you predict your actual electorate and adjust strategy accordingly.
Campaign team members coordinate their canvassing strategy during a pre-shift briefing.
Presidential general elections draw the highest turnout — typically 60-70% of registered voters in competitive states. These elections bring out low-propensity voters who only engage when national attention is highest. Midterm general elections see turnout drop to 40-50% of registered voters, filtering out casual participants. Primary elections draw only 15-25% of registered voters, representing the most engaged partisan activists. Local and special elections often see turnout below 20%, dominated almost entirely by super voters.
Analyze your voters’ participation patterns across these election types to predict who will show up for your race. If you’re running in a 2026 midterm, look at participation in 2022, 2018, and 2014 midterms. Voters who participated in 2+ of these three cycles are your likely electorate. Voters who only voted in 2020 and 2024 presidential elections probably won’t vote in your midterm race.
This analysis prevents a common campaign mistake: targeting voters based on presidential year turnout when running in a midterm or local election. A campaign that builds strategy around 2024 presidential turnout patterns will waste resources on 30-40% of voters who simply won’t participate in 2026.
Create turnout models specific to your election type. Calculate what percentage of registered voters typically participate in races like yours, then identify which specific voters comprise that percentage based on their historical participation in similar elections. This gives you a realistic universe size and composition.
For local elections, election type analysis is especially critical. A city council race might see only 12-15% turnout, meaning your entire campaign should focus on the 12-15% of registered voters who actually vote in city council races. Ignore the other 85-88% unless you have unlimited resources and a specific reason to believe you can activate them.
Combining Voting History With Demographics for Precision Targeting
The most sophisticated campaigns in 2026 layer voting history data with demographic and geographic data to create multi-dimensional voter profiles. This combination produces targeting precision that single-variable approaches cannot match.
Start with your propensity-scored voter universe, then overlay demographic filters relevant to your campaign. For a school board race, filter for high-propensity voters with children in district schools. For a healthcare-focused campaign, filter for high-propensity voters over age 55. For an economic development campaign, filter for high-propensity voters in key employment sectors.
This layering approach ensures you’re targeting voters who both care about your issues AND actually vote. A campaign targeting “parents with school-age children” might reach 15,000 households, but only 4,000 of those households contain voters who participate regularly in school board elections. Focus your resources on those 4,000.
Add geographic targeting to identify high-propensity voters in swing precincts or areas where your message resonates. Cross-reference voting history with precinct-level results from similar past races. Find precincts that had high turnout and competitive results — these areas contain both engaged voters and persuadable voters.
Use consumer data or survey results to further refine your high-propensity universe. Voters who demonstrate high propensity, match your demographic targets, live in swing precincts, AND show consumer behaviors aligned with your message are your absolute highest-value contacts. These voters might represent only 5-8% of registered voters but could determine 30-40% of your election outcome.
This precision targeting approach is explained in detail in our guide on how to segment voter data effectively, which covers advanced multi-variable targeting strategies.
Tracking and Updating Your Voter History Analysis
Voting history data is not static — it updates after every election, and your analysis must evolve accordingly. Campaigns that treat voter files as one-time downloads miss critical opportunities to refine targeting as new data becomes available.
Update your voter file and recalculate propensity scores after every election that occurs during your campaign cycle. A voter who sat out the spring primary just revealed important information about their engagement level. A voter who participated in a low-profile special election just demonstrated super voter behavior. These updates change your targeting priorities.
Track your actual voter contact results against predicted propensity scores to validate your model. If you’re seeing successful contacts and positive responses from voters with propensity scores of 2, but poor results from voters scored at 4, your propensity model needs adjustment. This feedback loop improves accuracy over time.
Monitor turnout trends in your district compared to historical patterns. If you’re seeing unusually high early voting among medium-propensity voters, that signals increased engagement that should shift your resource allocation. If super voter turnout is lagging expectations, you may need to increase mobilization efforts even among your most reliable voters.
Build institutional memory by documenting what you learn about voting patterns in your district. Which precincts overperform their historical turnout? Which demographic groups show increasing or decreasing participation? Which message frames activate medium-propensity voters? These insights become more valuable with each election cycle.
After your election, conduct a thorough analysis comparing predicted propensity scores to actual turnout. Calculate your model’s accuracy rate overall and within specific demographic or geographic segments. Identify where your predictions were most accurate and where they failed. This post-election analysis makes your next campaign significantly more effective.
Common Mistakes Campaigns Make With Voting History Data
Even campaigns that use voting history data often make critical errors that undermine their effectiveness. Avoid these common mistakes to maximize your data’s value.
Mistake one: Treating all registered voters equally. Many campaigns still allocate resources based on registered voter counts rather than likely voter counts. This wastes 40-50% of your budget on voters who won’t participate. Always filter your universe by propensity score before planning resource allocation.
Mistake two: Using outdated voter files. Voter files should be updated monthly at minimum, weekly if possible. A voter file from six months ago is missing new registrations, address changes, and recent participation data. Using outdated files means knocking wrong doors and mailing wrong addresses.
Mistake three: Ignoring election type differences. A voter who participated in the 2024 presidential election is not automatically likely to vote in your 2026 local race. Always analyze participation in similar election types, not just recent elections.
Mistake four: Over-contacting super voters while under-contacting medium-propensity voters. Super voters will vote regardless of how many times you contact them. Medium-propensity voters need multiple quality contacts to activate. Adjust your contact frequency accordingly.
Mistake five: Assuming voting history predicts vote choice. Voting history tells you who will vote, not how they’ll vote. You still need partisan, demographic, and issue data to determine voter preferences. Use voting history for turnout prediction and resource allocation, not for assuming support.
Mistake six: Failing to combine voting history with other data. Voting history alone is powerful but incomplete. The campaigns that win layer turnout data with demographics, geography, party registration, and issue preferences to create complete voter profiles.
Mistake seven: Not testing and validating propensity models. Accept that your initial propensity calculations might be imperfect. Test your model against actual results and refine continuously. A propensity model that’s 75% accurate beats no model at all, and iterative improvements can push accuracy to 85-90%.
Tools and Platforms for Accessing Voting History Data
Accessing and analyzing voting history data requires the right tools and platforms. In 2026, campaigns have multiple options ranging from state voter file purchases to full-service data platforms.
Volunteers spread across the neighborhood, bringing civic engagement to every doorstep.
State voter files are the primary source of voting history data. Most states sell voter files directly to campaigns, with prices ranging from $50 to $5,000 depending on state size and data fields included. These files typically include registration data, voting history for the past 10-20 elections, party affiliation, and basic demographics. However, raw state files require significant data processing and analysis expertise.
Commercial voter data platforms provide enhanced voter files with pre-calculated propensity scores, additional demographic overlays, and user-friendly interfaces. MailVotes offers voter data for Florida, North Carolina, Pennsylvania, Ohio, Oklahoma, and Arkansas with advanced filtering by voting history, demographics, and geography. These platforms save weeks of data processing time and provide analysis tools that would otherwise require dedicated data staff.
National voter file vendors like L2 and TargetSmart provide comprehensive data across all states with sophisticated modeling and scoring. These services typically cost $2,000-$10,000+ depending on campaign size and data needs. They’re most appropriate for well-funded campaigns running in multiple jurisdictions.
CRM and campaign management platforms like NGP VAN, Campaign Deputy, and others integrate voter file data with your contact tracking and field operations. These tools allow you to build walk lists, call lists, and mail lists filtered by propensity score, then track results back to individual voters.
For campaigns with data expertise, open-source tools like R and Python can process raw voter files and calculate custom propensity models. This approach offers maximum flexibility but requires significant technical skills and time investment.
Choose your tools based on campaign size, budget, and technical capacity. A local campaign with a $50,000 budget should use a mid-tier platform like MailVotes that provides pre-processed data and intuitive filtering. A statewide campaign with a $2 million budget should invest in a comprehensive national vendor plus dedicated data staff.
Putting It All Together: A Step-by-Step Implementation Plan
Implementing voting history analysis in your campaign requires a systematic approach. Follow this step-by-step plan to integrate turnout data into your strategy.
Step one: Acquire your voter file (weeks 1-2 of campaign). Purchase or access the most recent voter file for your jurisdiction. Ensure it includes voting history for at least the last 3-5 election cycles. Update monthly throughout your campaign.
Step two: Calculate or obtain propensity scores (week 2). If using a platform like MailVotes, propensity scores are pre-calculated. If processing raw data, calculate scores based on participation frequency, election type matching, and recency. Validate your scoring model against known turnout in recent similar elections.
Step three: Segment your universe (week 3). Divide your voter file into propensity tiers (high, medium, low). Within each tier, create sub-segments based on party affiliation, demographics, and geography. Identify your base voters, persuadable voters, and mobilization targets.
Step four: Build contact strategies for each segment (week 3). Determine contact frequency, channel mix, and messaging approach for each propensity tier. High-propensity voters receive maximum contact through multiple channels. Medium-propensity voters receive moderate contact focused on activation. Low-propensity voters receive minimal contact.
Step five: Allocate resources proportionally (week 4). Assign 60-70% of your budget and staff time to high-propensity voters, 20-30% to medium-propensity voters, and 5-10% to low-propensity voters. This allocation contradicts equal-treatment approaches but produces superior results.
Step six: Build your contact lists (weeks 4-5). Create walk lists, call lists, and mail lists filtered by propensity score and segment. Export lists in formats compatible with your phone banking, canvassing, and mail vendors. For direct mail, browse mailing list options that integrate propensity filtering.
Step seven: Execute your contact program (weeks 6-election day). Implement your tiered contact strategy, tracking results by propensity segment. Monitor contact rates, response rates, and voter feedback within each tier.
Step eight: Update and refine continuously (throughout campaign). Refresh your voter file monthly. Update propensity scores after any election that occurs during your cycle. Adjust resource allocation based on actual results versus predictions.
Step nine: Conduct post-election analysis (after election day). Compare predicted propensity scores to actual turnout. Calculate your model’s accuracy overall and by segment. Document lessons learned for future campaigns.
This systematic approach transforms voting history data from a static file into a dynamic campaign tool that improves results at every stage.
The Future of Voting History Analysis in Political Campaigns
Voting history analysis continues to evolve as data science techniques advance and voter behavior changes. Understanding emerging trends helps campaigns stay ahead of the curve in 2026 and beyond.
Machine learning models are increasingly replacing simple frequency-based propensity scores. These models incorporate dozens of variables beyond just voting history — including consumer behavior, social media activity, and real-time campaign engagement — to predict turnout with 85-90% accuracy. Campaigns with data science capacity should explore these advanced modeling techniques.
Real-time data integration allows campaigns to update propensity scores based on early voting participation, volunteer responses, and digital engagement. A voter who requests an absentee ballot or votes early just demonstrated high propensity regardless of their historical pattern. Campaigns that integrate these real-time signals into their targeting gain significant advantages.
Predictive analytics are moving beyond simple turnout prediction to forecast vote choice, persuadability, and optimal contact timing. Advanced models can predict not just whether someone will vote, but how likely they are to support your candidate and which message frame will be most effective.
Mobile and digital integration connects voting history analysis to digital advertising and mobile outreach. Campaigns can now match high-propensity voters to their mobile devices and social media profiles, enabling coordinated multi-channel campaigns that reach the right voters everywhere they engage.
Privacy regulations and voter file access rules continue to evolve, with some states restricting commercial use of voter data or limiting what information can be included in voter files. Campaigns must stay current on data access rules in their jurisdictions and ensure compliance with all applicable laws.
The fundamental principle remains constant: voter behavior predicts voter behavior. Campaigns that master voting history analysis will continue to outperform those that don’t, regardless of which specific tools and techniques they employ. The campaigns that win in 2026 and beyond will be those that combine sophisticated data analysis with strategic resource allocation and quality voter contact.
By implementing the strategies outlined in this guide, your campaign can transform voting history data from a raw file into your most powerful strategic asset. Start with the basics — calculate propensity scores, segment your universe, and allocate resources proportionally. As you gain experience, layer in advanced techniques like multi-variable targeting, real-time updates, and machine learning models. The campaigns that master these approaches don’t just work harder — they work smarter, reaching the right voters with the right messages at the right time.
Frequently Asked Questions
What is voting history data and how does it help campaigns?
Voting history data is a record of which elections each registered voter participated in, without revealing how they voted. This data helps campaigns identify likely voters, predict turnout patterns, and allocate resources efficiently by focusing on voters with demonstrated participation habits rather than wasting time on those unlikely to vote.
How accurate are vote propensity scores?
Vote propensity scores typically achieve 75-85% accuracy in predicting whether an individual voter will participate in a given election. Accuracy is highest for established voters with consistent patterns and decreases for newly registered voters or those with irregular participation. Combining propensity scores with other data points improves predictive accuracy to 85-90%.
What’s the difference between a super voter and a likely voter?
A super voter is someone who consistently votes in nearly every election (typically 4+ out of the last 5 elections), regardless of type. A likely voter is someone with a high probability of voting in your specific election based on their historical participation in similar election types. All super voters are likely voters, but not all likely voters are super voters.
Can voting history data predict which candidate someone will vote for?
No, voting history data only shows whether someone voted, not how they voted. However, when combined with party registration, demographic data, and geographic patterns, campaigns can make educated inferences about voter preferences. The real power is identifying who will show up to vote, then using other data to determine persuasion and mobilization strategies.
How far back should I analyze voting history for my campaign?
Analyze at least the last 3-5 election cycles (6-10 years) to identify reliable patterns. For most campaigns, examining the last two cycles of your specific election type (presidential, midterm, or local) plus general participation frequency provides the optimal balance of relevance and predictive power without overweighting outdated behavior.
