What is Dynamic Pricing?

What Is Dynamic Pricing for Subscription Mobile Apps?

Botsi is a dynamic pricing platform built specifically for subscription mobile apps.

Dynamic pricing for subscription mobile apps means adjusting subscription prices or offers in real time based on user behavior, predicted lifetime value, geography, and engagement signals.

Instead of showing every user the same offer, dynamic pricing personalizes monetization to drive higher long-term revenue and stronger retention. 

On iOS and Android, this usually happens at the paywall, where pricing, trial length, subscription duration, and promotional offers can vary by user segment.

At Botsi, we think about dynamic pricing as predicting a user’s expected lifetime value and selecting the subscription offer that maximizes long term revenue.

It shifts pricing from a single global decision to a per user optimization problem.

Why Static Pricing Leaves Revenue on the Table

Most subscription apps still use static pricing.

One annual price.
One monthly price.
Maybe a single discount.
Everyone sees the same offer.

That assumes willingness to pay is relatively uniform. It is not.

In most consumer subscription apps:

• A small segment would subscribe even at a higher price
• A larger middle segment converts only if pricing feels reasonable
• A meaningful segment converts only with discounts or trials
• A large portion never converts at any price

Static pricing compresses all of that variation into one number.

Let’s walk through a simplified example.

Assume you acquire 100,000 new users per month.

With a single annual price of 59.99:

• 5 percent convert
• That equals 5,000 subscribers
• Revenue is 5,000 × 59.99 = $299,950

Now assume the audience actually breaks down like this:

• 20 percent are high intent
• 30 percent are medium intent
• 30 percent are price sensitive
• 20 percent are unlikely to convert

If you could align pricing to those groups:

High intent segment
20,000 users
10 percent convert at 79.99
Revenue = 20,000 × 10 percent × 79.99 = 159,980

Medium intent segment
30,000 users
6 percent convert at 59.99
Revenue = 30,000 × 6 percent × 59.99 = 107,982

Price sensitive segment
30,000 users
5 percent convert at 39.99
Revenue = 30,000 × 5 percent × 39.99 = 59,985

Unlikely segment
20,000 users
Minimal conversion

Total revenue becomes 327,947.

That is roughly 28,000 more in monthly revenue from the same 100,000 users.

This example is simplified, but it shows the core idea: different users respond to different price points, and expected revenue per user changes depending on price.

Now zoom out.

If your blended CAC is 40 and your average LTV increases by 10 percent because pricing better matches willingness to pay, your payback window shortens. On 500,000 per month in paid acquisition, even a modest LTV lift can represent hundreds of thousands in annual incremental value.

How Dynamic Pricing Works Step by Step

Dynamic pricing is structured and data-driven. It is not random discounting.

Dynamic pricing is allowed on iOS and Android.

Step 1: Collect Behavioral and Contextual Signals

Most subscription apps already collect meaningful signals before a user reaches the paywall.

Common inputs include:

• Country and purchasing power
• Device type and operating system
• Onboarding completion
  
• Content consumption depth
• Time to paywall
• Engagement frequency
• Trial history
• Acquisition source
• Historical subscription behavior
  
  

Step 2: Predict Lifetime Value

Strong dynamic pricing systems typically model two related outcomes.

First, the probability that a user will subscribe.

Second, the amount of long term value that user is expected to generate if they do subscribe.

In practice, teams often use machine learning approaches like gradient boosting or decision tree models trained on historical subscription data. Because most users do not convert, the model focuses on identifying the small percentage who are likely to subscribe and estimating their expected value.

A practical structure looks like this:

1. Predict purchase probability early in the user journey
2. Group users into broad intent bands such as low, medium, and high probability
3. Assign pricing strategies based on those bands
4. Estimate expected revenue per pricing option

Example:

User A:

• High probability to convert at 59.99
• Expected 1.8 renewals
• Predicted LTV of 108
  
  

User B:

• Lower probability at 59.99
• Higher probability at 39.99
• Expected 0.8 renewals
• Higher expected revenue at the lower price
  
  

User B generates more expected revenue at the lower price point.

The pricing decision is based on expected revenue per user, which combines conversion probability with projected subscription value.

Timing matters. Many subscription conversions happen within the first few sessions or first 24 hours after install. Predictions need to happen early so pricing can influence the decision before the user exits the funnel.

Step 3: Select From Predefined Subscription Products

Dynamic pricing does not invent new prices on the fly.

On iOS and Android, the system selects from predefined subscription products, base plans, and promotional offers.

Each price must already exist as a configured in app purchase inside App Store Connect or Google Play Console.

Step 4: Serve the Personalized Paywall in Real Time

When the paywall loads, the pricing engine returns the optimal paywall to show the user.

The model has determined based on the possible available options which one will maximize the LTV for each user.

There can be many different implementations of outputs. Botsi returns a signal paywall that you should show to this user, but each paywall can have many different variations:

• Which subscription product to show
• Whether to include a trial
• Which duration to emphasize
• How the pricing should be positioned

This decision usually happens in under 200 milliseconds, so the user experience remains seamless.

Step 5: Measure and Improve

Dynamic pricing is ongoing.

Systems track:

• Conversion rate
• Revenue per user
• Renewal rates
• Refund rates
• CAC payback period

Models are retrained regularly as new subscription data accumulates. Over time, prediction accuracy improves and revenue lifts compound.

Dynamic Pricing vs Geo Pricing

Localized pricing adjusts subscription prices by country based on purchasing power and platform price tiers.

For example:

• US annual subscription at 59.99
• Brazil at 29.99
• India at 19.99

These localized prices are important. They account for broad economic differences between regions and are typically configured directly inside App Store Connect or Google Play Console.

Dynamic pricing builds on top of that.

Within a single country, willingness to pay still varies significantly.

A US user on a four-year-old entry-level Android device is not economically identical to a US user on a brand new 2,000 dollar iPad Pro. Even within the same geography, other data points like device type, engagement behavior, and user inputs can signal very different price sensitivity.

At the same time, a user in India using a brand new iPad may resemble a high-intent US user more than they resemble the average user in their own country.

Localized pricing captures macro level purchasing power.

Dynamic pricing captures micro-level differences in behavior and predicted value.

In practice, strong pricing systems combine both:

1. Start with localized baseline pricing
2. Predict purchase probability and expected value per user
3. Select the most appropriate subscription product within the allowed price tiers
   
   

Localized pricing sets the floor and ceiling by region.

Dynamic pricing decides where a specific user should land within that range.

Both are necessary for global subscription apps that operate at scale.

Read about how Mojo has had success with their localized pricing tests.

Dynamic Pricing vs A/B Testing

A/B testing compares price variants across randomized groups to measure aggregate performance. You might split traffic 50.50 between 49.99 and 59.99, then use statistical tests to determine which performs better overall.

This works well for validating broad pricing hypotheses. It tells you which price performs better on average across the entire audience.

But A/B testing optimizes toward a single global winner.

If 59.99 produces slightly higher total revenue than 49.99, that becomes the chosen price, even though part of the audience may have converted at a higher price and another segment may only convert at a lower one.

Traditional frequentist A/B testing also requires relatively large sample sizes to reach statistical significance. If you split users into many subgroups, it becomes difficult to detect meaningful differences because each segment has limited traffic.

Machine learning driven dynamic pricing works differently.

Instead of asking which price wins overall, it models how different user characteristics relate to conversion probability and expected value. That allows it to detect patterns in willingness to pay across smaller effective sample sizes by learning from combinations of features rather than isolated splits.

For example, the model might learn that:

• Users on premium devices with fast onboarding completion tolerate higher price points
• Users with slower onboarding progression respond better to lower upfront pricing
• Certain acquisition channels correlate with stronger annual plan retention

These patterns can emerge even when each individual segment is too small to power a standalone A/B test.

Multivariate testing can often play an important role at the beginning of building an ML model for dynamic pricing. Running structured price experiments across multiple tiers helps generate clean, labeled data that the machine learning model can train on. In many cases, experimentation comes first, modeling comes second, and dynamic pricing becomes the operational layer that applies those learnings continuously.

Another important difference is objective function.

A/B testing often optimizes for total revenue at a single price point. That can skew results toward a higher price that increases revenue per subscriber but reduces the total number of subscribers. Over time, that may affect retention, referrals, or downstream expansion revenue.

Dynamic pricing evaluates expected revenue at the user level. Some users will see higher prices because their predicted willingness to pay is strong. Others will see lower prices because expected revenue is higher at that level. 

And there are almost always segments within an audience that are willing to pay more than the current global price.

Looking for more A/B test ideas to try for your app? Find more here.

Can You Show Different Prices to Different Users on iOS?

Yes, when implemented correctly.

You can:

• Create multiple subscription products
• Configure different price tiers
• Use introductory pricing
• Use promotional offers
• Use offer codes
• Use win back offers

Dynamic pricing works by selecting from these predefined products.

On Android, Google Play provides similar flexibility through base plans and offers.

The key is that every price corresponds to a valid in app purchase and is clearly disclosed.

Risks and Compliance Considerations

Dynamic pricing needs guardrails.

Regulatory Considerations

Avoid segmenting on protected characteristics.

Ensure pricing is clearly disclosed.

Keep refund and cancellation policies consistent.

This article on Retention.Blog provides a helpful overview. 

Platform Compliance

Every price must correspond to a configured subscription product in App Store Connect or Google Play Console.

Do not attempt to bypass in app purchase systems.

User Trust

If price differences are extreme, users may react negatively.

A common best practice is keeping price variance within a reasonable range, often 10 to 30 percent.

Model Bias

If your acquisition channels skew toward certain user types, your pricing model may reflect that skew.

Mitigate this with:

• Regular audits
• Monitoring revenue and outcomes by segment
• Guardrails on minimum and maximum pricing

Dynamic Pricing Software for Subscription Apps

Not all monetization tools support true dynamic pricing.

A/B testing platforms randomize exposure.

Rule based systems rely on predefined segments.

Dynamic pricing software requires:

• LTV prediction
• Per user revenue optimization
• Real time decisioning
• SKU level orchestration
• Continuous retraining

Without predictive modeling, pricing personalization becomes manual segmentation.

How Botsi Implements Dynamic Pricing

Botsi is built specifically for subscription mobile apps on iOS and Android.

The system:

1. Connects to subscription event data
2. Trains models to predict 90 day, 180 day, and 365 day LTV
3. Engineers pricing signals for monetization and growth teams
4. Selects from predefined App Store and Google Play subscription products
5. Returns pricing decisions in real time

Botsi integrates with tools like RevenueCat, Superwall, and Adapty.

Pricing decisions are optimized around expected long term revenue, while guardrails prevent excessive discounting and extreme variance.

For teams scaling paid acquisition, dynamic pricing can become part of the growth infrastructure.

Who Should Use Dynamic Pricing?

Dynamic pricing makes the most sense when:

• Have $50-$100k MRR +
• You generate at least 500 conversions per month
• Your annual subscription price is above $30

Early stage apps with low volume often do fine with static pricing.

At larger scale, pricing variance increases, and the impact of personalization becomes meaningful.

Frequently Asked Questions About Dynamic Pricing

What is dynamic pricing in mobile apps?

Dynamic pricing adjusts subscription prices or offers per user based on predicted lifetime value, engagement, contextual, and behavioral signals.

Is dynamic pricing legal?

Yes, when implemented transparently and without discrimination based on protected characteristics.

Does Apple allow dynamic pricing?

Apple allows apps to present different subscription products and promotional offers as long as each product follows App Store Review Guidelines.

Is dynamic pricing the same as A/B testing?

No. A/B testing compares randomized groups. Dynamic pricing optimizes pricing at the individual user level.

Does dynamic pricing increase churn?

When implemented carefully, it often improves retention by aligning price with willingness to pay.

What data is required for dynamic pricing?

At minimum:

• Subscription events
• Conversion history
• Renewal history
• Engagement signals
• 500-100 conversion events per month

More data improves prediction accuracy and revenue impact.

Conclusion: Dynamic Pricing as Growth Infrastructure

Dynamic pricing turns pricing into an adaptive system rather than a fixed setting.

As acquisition costs rise, lifetime value becomes the limiting factor in growth.

Static pricing applies the same monetization logic across the entire audience.

Dynamic pricing adjusts monetization at the individual user level.

For scaled subscription apps on iOS and Android, dynamic pricing increasingly functions as core revenue infrastructure.

Botsi was built to provide that infrastructure by combining LTV prediction, pricing signal engineering, and real time paywall optimization without requiring internal data science teams.

Over time, the revenue difference compounds.

Want to learn more about personalized pricing for your app?