Acquisition and retention · Randomized field experiment and policy evaluation
Design and Evaluation of Optimal Free Trials
Yoganarasimhan, H., Barzegary, E., & Pani, A. (2023). Management Science, 69(6), 3220–3240.
Plain-English take
In this firm and experiment, a 7-day trial was the best uniform policy and increased subscriptions by 5.59% versus assigning everyone a 30-day trial. For your marketing: This may be useful when designing a local randomized trial-length experiment.
How much weight to give this
5.0/5 Strong design for its specific question
The 1–5 evidence-check score is a practical reading aid, not a formal peer review or a probability that a conclusion is true. It combines design strength (2 points), sample reporting (1), comparison/control clarity (1), and how much of the paper could be checked (1). Access affects how confidently this directory can assess a study, not the study's underlying quality. Blinding is reported separately because it is not relevant to every design.
Random assignment provides a strong comparison for effects in the tested setting.
Large sample reported (337,724); size alone does not guarantee a representative sample.
Randomized comparison; confirm the control condition and analysis in the paper.
An accessible full paper or manuscript is linked, making methods easier to check.
Double-blind? Blinding is not reported in the source reviewed; that does not establish whether blinding was or was not used.
Research question
What the authors investigated
How do different free-trial lengths affect subscriptions, and can trial length be assigned more effectively using customer characteristics?
Design
A SaaS firm randomly assigned new users to 7-, 14- or 30-day trials. The authors compare uniform policies and evaluate personalized assignment rules using experimental data and inverse-propensity-score policy evaluation.
Sample and setting
337,724 new users across Australia, New Zealand, France, Germany, Japan, the United Kingdom and the United States; subscription and retention were followed for two years. The firm and product are anonymized.
Reported result
What the study found
In this firm and experiment, a 7-day trial was the best uniform policy and increased subscriptions by 5.59% versus assigning everyone a 30-day trial. The best evaluated personalized policy, based on lasso, improved subscriptions by 6.81% versus that 30-day baseline; it also performed well on longer-run outcomes.
This is a paraphrase of the source. Check the original paper for definitions, estimates, statistical uncertainty and analysis details.
Scope and transfer
Limitations to keep in view
One anonymized SaaS firm, product, trial design and historical period. Personalized-policy performance depends on the covariates, experiment and evaluation method. The result is not a general recommendation to shorten every SaaS trial.
Possible founder application
Useful when designing a local randomized trial-length experiment. Treat the paper as a design and evaluation example; estimate outcomes for your own activation path, sales cycle and user mix.
Evidence access
Open author preprint; publisher version may require access
Full author preprint plus the peer-reviewed publisher record. Sample, random assignment, follow-up and headline results were checked in the paper text.
If only an abstract or metadata was available, the profile is limited to claims supported there. A paper's paywall does not change its evidentiary status, but it limits what we can substantiate from accessible text.