Bayesian A/B testing uses Bayes’ theorem: P(A|B) = P(B|A) x P(A) / P(B), where P(A|B) is the posterior probability (updated belief), P(B|A) is the likelihood, P(A) is the prior, and P(B) is the evidence. In practice, tools compute this using beta distributions for conversion rates.
Frequentist testing gives you a binary answer (significant or not) after collecting enough data. Bayesian testing gives you a probability that each variant is the winner, updated in real time as data flows in. Bayesian is more intuitive (‘there is a 92% chance B is better’) and lets you make decisions earlier, but requires more computational setup.
Use Bayesian methods when you need faster decisions (like pricing tests where delayed results cost revenue), when sample sizes are small, or when you want to incorporate prior knowledge. It is especially valuable for apps with lower traffic where reaching frequentist significance could take months.
Not necessarily. Many modern experimentation platforms (including Botsi) handle the math behind the scenes and present results as probability distributions. You just need to understand how to interpret ‘probability of being best’ rather than p-values.
The server-side system that determines which features or content a user can access based on their subscription status. Typically integrated with app store receipt and tools like like Botsi, it ensures consistent access across devices and platforms.
User engagement that occurs without directly launching the app, such as interacting with push notifications, widgets, or voice assistants. While less visible, this activity can influence retention and should be factored into engagement metrics.
The practice of grouping users based on in-app behavior — such as content viewed, session frequency, or purchase history — to tailor messaging, paywalls, and retention strategies. Behavioral segmentation enables personalized user experiences and drives more efficient monetization.
The interval at which a subscription renews and the user is charged — typically monthly, quarterly, or annually. Understanding billing cycles is important for tracking revenue recognition, renewal trends, and user lifetime value.
The automated process by which subscription platforms (e.g., Apple, Google, or a backend service) attempt to recover failed payments due to issues like expired cards or insufficient funds. Effective retry logic is essential for reducing involuntary churn and maintaining revenue continuity.
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