REPORTS

2020 Political Texting Report

How peer-to-peer texting drove voters to the polls in the midst of a global pandemic

Texting Still Makes a Difference

In five short years, peer-to-peer texting has shifted from a Hail Mary experiment to a key way for campaigns to Get Out the Vote. COVID-19 accelerated massive changes in the political landscape and restricted political mainstays like door-knocking and in-person events, and so political battles migrated online and onto voters' phones. All told, it’s estimated that Americans received more than 11.6 billion political messages in the last three months of the 2020 election

Political peer-to-peer texting exploded in popularity in 2020, but was it effective? Tech for Campaigns was active in 27 states and ran 36 texting programs for Democratic campaigns and committees, sending more than 1.4 million texts. After crunching the numbers, we’ve discovered that peer-to-peer texting remains a powerful way for campaigns to drive voters to the polls. But it’s not just a tool for your ardent supporters. Texting is a great way to drive unexpected, unlikely voters to cast a ballot.

The findings below are select learnings from our Get Out the Vote (GOTV) texting analyses. The analyses are correlational, not causal - though we controlled for as many variables as possible. While randomized controlled trials (RCTs) are the gold standard for analysis, they are extremely difficult to run in competitive political environments. More details on our methodology can be found at the end of this report.

TEXTING CONTINUES TO DRIVE AMERICANS TO TURN OUT AND VOTE

Registered voters that received a text turned out to vote 0.7 percentage points more than their untexted counterparts

In 2020, people texted by TFC turned out to vote 0.7 percentage points (p.p.) more than their untexted counterparts in the same districts. Candidates running for state legislative and county offices often win by the slimmest of margins. In these local races, the addition of a well-run peer-to-peer texting program can make all the difference. We should know — Dr. Liz Snyder, a TFC candidate in the Alaska House, won her 2020 race by only 11 votes!


WITH LOW COSTS AND WIDE REACH, CAMPAIGNS SHOULD TEXT EVEN THE ‘LEAST LIKELY’ DEMOCRATIC VOTERS

We found that texting unlikely voters is an inexpensive and effective way to increase turnout, particularly for down-ballot candidates. Traditionally, many campaigns avoid contacting unlikely voters, especially given the high cost of doing so via door-to-door canvassing or phone banking. But texting is so cheap (some platforms only cost 1¢ per text) that it is cost-effective and worthwhile to engage them. 

Texting the least likely voters (that are Democrats) is highly cost-effective and triples their turnout rates

TFC teams texted registered voters that were deemed the least likely1 to cast a ballot and found that they voted at a rate nearly three times higher than similar voters we didn’t text. Conversely, in the same districts, higher turnout voters often targeted in GOTV campaigns2 saw no change in turnout when sent a text.

1, 2. Voting or turnout likelihood defined by TargetSmart Turnout Scores. The ‘least likely’ cohort was any voter with a Turnout Score between 0 and 20 (the lowest quintile of projected turnout). The voters often targeted in a GOTV campaign were defined as any voter with a TargetSmart Turnout Score between 20 and 80.

SEGMENTATION AND AUDIENCE-SPECIFIC MESSAGING IS CRITICAL, ESPECIALLY FOR SWING VOTERS

As texting becomes more popular, it’s critical for even the smallest campaigns to send relevant messages to specific voting audiences. For example, TFC saw increased swing voter turnout when those voters were sent texts containing helpful voting information and prompts.

Texting swing voters with voting information is correlated with a 1.8 percentage point increase in their voting rate

In 2018, we found that people who received text messages with candidate or district-specific issues, such as Medicaid expansion or new environmental legislation, were 8.2% more likely to vote. This trend did not continue through the 2020 election cycle. In fact, we found that sending texts containing voting information and guidance — and no issue-based messaging — had consistently positive impacts. This occurred when controlling for various factors, including gender, age, ethnicity, and timing. Swing voters drove much of this shift. For them, getting helpful information on voting, such as their closest polling location or prompts to register for early voting, was correlated with an additional 1.8 percentage point increase in their voting rates (versus being sent issue-based text messages)

Sending issue-based messaging had little impact on voter turnout. The big exception was people who were likely to be Democrats AND likely to vote. With this in mind, we suggest sharing voting information with clear calls-to-action as Election Day approaches -- especially with voters who are likely to cast a ballot. Save issue-based messaging for likely Democrats, volunteer recruitment, and fundraising messaging earlier in the election cycle.


TEXTING YOUNGER VOTERS (STILL) HAS AN OUTSIZED IMPACT

No one demographic is a monolith, but there are some common themes that any campaign can follow to increase the impact of their texting program. For one, age continues to influence how texting impacts voting rates. 

In 2018, we noticed that voters 50 and younger tended to turn out at higher rates when sent a text. This generally held true again in 2020: voters below the age of 65 that TFC texted turned out at significantly higher rates than projected. Be it from low awareness or busy lives, one group stood out:

Texting had the biggest impact on the turnout of voters ages 30 to 44 

It’s not to say texting voters below the age of 30 isn’t worth it. Given the massive increase in turnout from younger voters this past election, it was difficult to quantify the impact of texting on their behavior.

(GOOD) CONVERSATIONS ARE A STRONG PREDICTOR OF TURNOUT

During an election cycle, it can be challenging to know if your texts are having an impact. TFC found that response rates and sentiment are a strong proxy for later voter turnout.

How you respond to a political text message is a strong indicator of if and how you will vote in that election cycle

Generating a positive reaction to a text is the best indication that someone will go vote. Registered voters that responded positively to a text message had a higher net turnout rate (+5 percentage points) than those who did not respond or those who opted-out from receiving future messages. 

However, keep an eye out for negative responses to your texts. In TFC campaigns, likely Republicans who responded negatively to a text voted at much higher rates (+6.1 percentage points) than similar Republicans who did not respond at all! 

Why This Matters

Before 2018, political texting was novel and unproven. But after the success of texting in the 2018 midterms, for 2020, campaigns went all in.

It’s understandable why. Texting offers political campaigns a personalized way to connect with voters at a much lower cost than other traditional outreach methods such as canvassing, a higher read and response rate than emails, and a significantly higher penetration rate than phone calls.3

COVID-19 and the passage of time have pushed political campaigns and organizations to finally shift some focus to digital platforms. Cost-effective tools like peer-to-peer texting have made it easier for local candidates to make their voices heard. However, with lower barriers of entry, these tools can be used by anyone, increasing the amount of noise and making it even more important for campaigns to be strategic in who they text and what they send. Hustle, a leading texting provider for progressive campaigns and groups, helped send 475 million texts in 2020, a 137% increase compared to 2018.

As peer-to-peer texting evolves, regulations and best practices will shift and present challenges for campaigns to effectively run their programs. While campaigns were gearing up for their 2020 Get Out the Vote drives, industry groups representing carriers like Verizon and AT&T changed how they regulated political texting, forcing campaigns to change their scripts and strategy at the last minute. 

New regulations are not unexpected with the rise of new technology. Looking forward to 2022, we are confident that carrier and government rules will continue to change, and campaigns will need to adapt their strategies at a moment’s notice. Despite any potential unpredictability, texting should remain an extremely effective tool for down-ballot candidates.


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Thank You

This project was made possible by some of the members of our amazing 16,000-person skilled volunteer community. Thank you to:

Anna Schneider, Cameron Bartok, Connor Kelley, Jocelyn Blumenrose, Lauren Fealey, Leah Nicolich-Henkin, Peter Spiro, Priya Gupta, Stella Mach, and Will Seaton.

Methodology and Sources

METHODOLOGY

We generated findings by combining the results of peer-to-peer texting campaigns with the TargetSmart national voter file. We first explored voting trends through differences-in-differences analyses, in which we analyzed the list of registered voters we texted, using their TargetSmart Turnout Score as a pre-treatment estimate of their turnout and their actual turnout as post-treatment. We could then compare their average Turnout Score with the actual turnout rate, and compare this difference across demographics. This enabled us to calculate the marginal vote increase for populations that received text messages versus those that did not when controlling for their TargetSmart Partisanship Score and other factors.

When we discovered any directional learnings, we then conducted regression analyses to try to uncover statistically suggestive and meaningful findings. With the list of potential voters we texted, we were able to control for various TargetSmart demographic attributes; such as age, gender, ethnicity, urban/suburban/rural environment, and more; and run a regression analysis to identify which behaviors were correlated with higher turnout rates. 

Impact of texting on overall turnout - 

Controlling for the turnout likeliness of each group, the group of voters TFC texted had a higher voting rate by 0.7 percentage points compared to uncontacted voters in the same districts (Figure 1). Voters with the lowest projected turnout rates (expected to vote about 0-20% of the time) experienced the highest increase in net turnout rates when sent a text. These ‘least likely’ voters voted at a statistically significant higher rate compared to uncontacted voters in the same group (Figure 2). Conversely, texting voters with higher Turnout Scores had a marginal impact on their relative turnout rates.

Impact of message content on turnout by partisanship - 

In our regression analysis, we found statistically suggestive evidence that swing voters that received voting information, rather than issue-based messaging, were more likely to vote when controlling for gender, age, and ethnicity. Swing voters were identified as those voters with a TargetSmart Partisanship Score between 30 and 70. This finding occurred for swing voters that had high Turnout Scores (Turnout Score > 70) and medium Turnout Scores (Turnout Score between 30 and 70). We excluded all data after October 22 to limit the impact that a “go vote” text message approaching Election Day would have on our findings. These findings further supported the directional learnings from our difference-in-difference analyses in Figure 3 above. 

Impact of texting on turnout by age -

To determine the impact of texting on voting rates in different age groups, we reviewed estimated and actual turnout rates for different populations when controlling for gender, ethnicity, and other demographics. While registered voters ages 18-29 that we texted had a smaller net turnout increase (+6.3 p.p.) compared to their untexted peers (+7.3 p.p.), the underlying regression analyses we conducted suggest that texting these younger voters still positively influences turnout rates. Additionally, our regression analyses suggested other age ranges were significantly impacted by peer-to-peer texting when controlling for multiple variables.

When running our regression analyses, we found that the turnout rates for voters ages 18-64 were positively impacted by receiving a text, especially when focusing on the ‘least likely’ voters within specific age brackets. Across all age groups (18-29, 30-44, 45-64), there was a statistically significant difference in turnout rates between those who were texted and those who were not when focusing on voters in the lowest turnout quintile (defined as voters with a TargetSmart Turnout Score between 0 and 20). Voters ages 30-44 that were sent a text were most correlated with increased voting rates. We even saw evidence that texting positively influenced turnout rates for voters ages 30-44 with a Turnout Score from 20-40 as well as 0-20 (the second-lowest and lowest turnout quintiles). As highlighted above, it appears that texting voters ages 18-29 is valuable regardless of their Turnout Score. We saw no evidence of a change in turnout when texting voters 65 years and older.

Impact of response, response type on turnout - 

In our regression analysis, we found statistically suggestive evidence that voters that responded to a text message were more likely to vote when controlling for estimated turnout (TargetSmart Turnout Score), estimated partisanship (TargetSmart Partisanship Score), the interaction between partisanship and response, gender, age, ethnicity, and urbanicity. While a positive response was indicative of higher turnout rates, a negative response was correlated with higher turnout rates for only one demographic group: likely Republicans. 

These findings further supported the directional learnings from our diff-in-diff analyses in Figure 4 above.

SOURCES

  • Voting records and demographics - All voting records and demographics were sourced from the TargetSmart national voter file.
  • Texting conversation data - Texting conversations, interactions with registered voters, and other key metrics were captured in exports from various texting platforms used by candidates working with TFC, including Hustle, ThruText, TextOut, Spoke, and Bluelink.
  • Response classification - Using manually-tagged responses sent by registered voters, we trained a response classification model that identified the response type of individual text responses sent by voters to the individuals texting on behalf of TFC candidates. This enabled us to classify all responses and use the trained model to infer if a particular text message had a positive or negative sentiment, or contained opt-out language. Negative responses were defined as any response that showed a negative attitude towards the candidate, the Democratic Party, or is rude or aggressive. Opt-outs contained any response asking not to be texted again (even if it was a generally positive message).