Can reminder emails compel Americans to save? A two-million-person megastudy
Bibliographic record
Abstract
In the United States, 24% of adults have no savings and 39% have less than a month of income saved. We present results from a megastudy where nearly 2 million customers of a US bank were randomly assigned to receive one of seven different 2-month email campaigns, each employing a different behavioral science insight to nudge one-time and recurring savings deposits and increase savings balances or to a control condition without such messages. These campaigns increased the probability of making a one-time savings deposit, on average, by 0.05 percentage points (a 0.51% increase over control). The best-performing campaign delivered weekly messages to customers that differed depending on recent savings behavior: messages to customers who had not made a savings account deposit in the last week included a simple reminder to save, while those to customers who had made a savings account deposit in the prior week were congratulated on this accomplishment. This top-performing campaign increased the monthly likelihood that a customer made a one-time savings deposit by 0.13 percentage points (a 1.32% increase). We estimate that rolling this 2-month campaign out to everyone in our megastudy population would have led to an extra $6,123,996 to $9,910,090 in savings. Together, our findings highlight that light-touch, frequent email nudges can cost-effectively create small increases in savings deposits in the United States. Ideally, to generate meaningful benefits, behavioral science insights would be incorporated into a wider range of communications and incentives designed by financial institutions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".