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Dynamic Pricing for Subscription Mobile Apps
Dynamic pricing for subscription mobile apps is the practice of adjusting subscription prices or offers at the individual user level based on predicted purchase probability, lifetime value, and behavioral signals. Instead of showing every user the same paywall price, dynamic pricing personalizes monetization decisions to maximize long-term revenue.
On iOS and Android, subscription apps typically operate with static pricing. One annual price. One monthly price. Maybe one discount. Everyone sees the same offer. Dynamic pricing changes that. It uses data and machine learning to estimate how different users respond to different price points and selects the price most likely to generate the highest expected revenue for that specific individual.
For subscription businesses scaling paid acquisition, improving LTV by even 5 to 10 percent can materially impact growth. Dynamic pricing is designed to unlock that lift.
Why Static Pricing Breaks at Scale
Static pricing assumes that all users have similar willingness to pay. In reality, willingness to pay varies significantly across users.
Within a single country, you may have:
- Users on premium devices with high purchasing power
- Users who complete onboarding quickly and show strong engagement signals
- Users who hesitate at the paywall
- Users who only convert with discounts or trials
A single global price compresses all of that variation into one number.
At small scale, static pricing can be acceptable. At larger scale, especially when spending hundreds of thousands per month on acquisition, that compression leaves revenue on the table.
Many teams start with pricing experiments or A/B testing to find a better average price. That is a strong first step. But even the best-performing global price is still an average. Some users would have paid more. Some would have converted at a lower price. Static pricing optimizes the middle of the distribution and ignores the tails.
Dynamic pricing addresses that distribution directly.
How Dynamic Pricing Works
Dynamic pricing systems typically follow five steps.
1. Data Collection
The system ingests user-level data such as:
- Acquisition channel
- Geography
- Device type and operating system
- Onboarding behavior
- Engagement depth
- Time to paywall
- Historical subscription behavior
For subscription mobile apps, most pricing decisions need to happen early in the lifecycle. Often within the first session or first few sessions.
2. Predict Purchase Probability
The first model estimates the likelihood that a user will subscribe at all.
In many consumer subscription apps, 90 percent or more of users never convert. Modeling purchase probability helps separate high-intent users from low-intent users early.
Machine learning techniques such as gradient boosting or tree-based models are commonly used because they handle nonlinear interactions between features effectively.
3. Predict Lifetime Value
The second layer estimates expected lifetime value conditional on conversion.
Some users convert quickly but churn after one billing cycle. Others retain for months. LTV prediction incorporates retention curves, renewal rates, and cohort behavior to estimate long-term revenue impact.
Purchase probability and LTV together determine expected revenue.
Expected revenue = conversion probability × expected lifetime value.
4. Select the Optimal Price
Given multiple price tiers or subscription options, the system estimates which price produces the highest expected revenue for that user.
Some users may tolerate a higher annual price.
Others may respond better to a lower upfront price or discounted offer.
The objective is not simply maximizing conversion rate. It is maximizing expected revenue per user over time.
5. Deliver the Paywall
The selected price is delivered through the app’s existing paywall infrastructure.
Dynamic pricing systems typically integrate with subscription infrastructure such as RevenueCat, Adapty, Superwall, and modern paywall builders, ensuring compliance with Apple and Google platform policies.
The user experience remains seamless. The decision logic changes behind the scenes.
Dynamic Pricing and Localized Pricing
Localized pricing adjusts subscription prices by country to reflect purchasing power differences. App Store Connect and Google Play Console support country-level price tiers.
Localized pricing is important, but it does not capture all variation in willingness to pay.
Within a single country, there is still wide dispersion in purchasing behavior. A user on a new iPad Pro may behave differently from a user on a four-year-old device. A highly engaged user may behave differently from a hesitant user. A high-value acquisition channel may produce stronger retention cohorts than another.
Dynamic pricing builds on localized pricing. Localized pricing sets the baseline for each region. Dynamic pricing personalizes within that baseline using behavioral and predictive signals.
For global subscription apps, both layers are necessary.
Dynamic Pricing vs A/B Testing
A/B testing compares price variants across randomized groups to measure aggregate performance. It answers questions such as whether 59.99 or 69.99 performs better overall.
Dynamic pricing answers a different question: which price is most appropriate for this specific user?
Traditional frequentist A/B tests require large sample sizes to detect statistically significant differences. If you split traffic across many price points and many segments, each cell becomes small and difficult to evaluate independently.
Machine learning–driven dynamic pricing learns from patterns across features rather than isolated splits. It can detect relationships between behavior, device, acquisition channel, and price sensitivity that would be impractical to test individually.
In practice, experimentation and dynamic pricing complement each other.
- Pricing experiments define viable ranges and generate clean training data.
- Dynamic pricing operationalizes those learnings at the user level.
A/B testing optimizes a global average. Dynamic pricing optimizes per user.
Is Dynamic Pricing Allowed on iOS?
Dynamic pricing is allowed on iOS and Android as long as it operates within the approved subscription products and price tiers configured in App Store Connect or Google Play Console.
Apps cannot dynamically create arbitrary prices outside platform rules. However, they can select among pre-configured subscription products and pricing tiers based on user characteristics and behavioral signals.
Compliance requires:
- Transparent subscription terms
- Proper use of Apple and Google subscription APIs
- Consistent in-app purchase configuration
When implemented correctly, dynamic pricing operates within platform guidelines.
How Botsi Implements Dynamic Pricing
Botsi is a dynamic pricing platform built specifically for subscription mobile apps.
Botsi ingests user-level event data, trains predictive models for purchase probability and lifetime value, and selects the optimal subscription offer per user. The platform integrates with existing subscription infrastructure, such as RevenueCat and Adapty, and works alongside modern paywall builders.
Botsi focuses on:
- Predicting LTV before conversion
- Optimizing expected revenue, not just conversion rate
- Supporting experimentation frameworks
- Operating within Apple and Google compliance
- Improving payback windows for paid acquisition
For apps spending heavily on user acquisition, small lifts in LTV can meaningfully improve unit economics. Botsi is designed to unlock those lifts through predictive monetization decisions rather than static price experimentation alone.
Related Articles
To go deeper:
- Pricing Experiments: The Backbone of Monetization
- Signal Engineering 101
- What Is Dynamic Pricing?
- Is Dynamic Pricing Allowed on iOS?
Related Concepts
For supporting definitions:
Dynamic pricing is becoming increasingly important for subscription mobile apps as competition rises and acquisition costs increase. Static pricing may work in early stages. At scale, personalization of monetization becomes a structural advantage.
Botsi exists to make that personalization possible.
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