Spillover Effects and Freemium Strategy in the Mobile App Market
Bibliographic record
Abstract
“Freemium,” whereby a basic service level is provided free of charge but consumers are charged for more advanced features, has become a popular business model for firms selling digital goods. However, it is not clear whether the launch of a free version helps or hurts the demand of an existing paid version. The free version may allow consumers to sample the product before making a purchase decision and subsequently increase demand of the paid version, but it may also cannibalize demand of the paid version. We use a comprehensive data set on game apps from Apple’s App Store that tracks the launch of both the paid and the free versions of individual apps on a daily level to identify whether a freemium strategy stimulates or hurts demand of an existing paid version. We estimate the spillover effects between the free version and the paid version of the same app under a difference-in-difference framework, relying on the fact that app developers cannot predict the exact launch date of the free version of the app due to Apple’s review and approval of apps prior to release and accounting for app-level product heterogeneity. We find that the launch of a free version increases demand of the paid version of the same app. Under the main specification, if the daily number of ratings before the free version’s launch is at the mean, then all else equal, the launch of the free version leads to an 8.9% increase in the daily number of ratings. We then describe multiple robustness checks. Finally, we present evidence that the results are driven by consumers sampling the free version as well as enhanced app discovery and explore the relative importance of the two mechanisms. This paper was accepted by Matthew Shum, marketing. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2022.4619 .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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 source (direct Gemma or distilled Codex), 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".