What is Dynamic Pricing?
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 price, you're customizing the offer to match the user's value and likelihood to convert. A highly engaged user with high LTV potential might see the full price. A lower-engagement user might see a discount. A user at high churn risk might see a retention offer.
For scaled subscription apps on iOS and Android, dynamic pricing increasingly functions as core revenue infrastructure—not just an optimization tactic, but a fundamental monetization strategy.
The Static Pricing Problem: A Numerical Example
Let's walk through a concrete example to illustrate why static pricing leaves money on the table.
Imagine you have a meditation app with 100,000 monthly active users and a static price of $9.99/month for your subscription.
Your baseline metrics:
- Paywall conversion rate: 5% (5,000 conversions/month)
- Average revenue per user: $500/year
- Monthly recurring revenue: $49,500 (5,000 × $9.99)
Now, let's say you segment your users and realize:
- 30% of users are "highly engaged" (session frequency > 10x/month, feature exploration score > 80): these users have a 15% conversion rate and 18-month LTV
- 50% of users are "moderately engaged" (session frequency 3-10x/month): these users have a 5% conversion rate and 12-month LTV
- 20% of users are "low engagement" (session frequency < 3x/month): these users have a 1% conversion rate and 6-month LTV
With static pricing, you're charging all three groups $9.99/month. But they have very different value profiles:
Highly engaged users: These users would likely pay more. Their high engagement and LTV suggests they'd accept $14.99 or even $19.99. At $9.99, you're leaving money on the table. The revenue per these users is: 100,000 × 30% × 15% × $9.99 × 12 = $539,460/year
Low engagement users: These users have low purchase probability and low LTV. At $9.99, you might only convert 1% of them. But what if you offered them a 50% discount ($4.99)? You might convert 4%, and even though the price is lower, the volume uplift generates more revenue. The revenue per these users is: 100,000 × 20% × 1% × $9.99 × 6 = $119,880/year
With dynamic pricing, you'd offer high-engagement users $14.99 (converting at 12% instead of 15%, but at higher price = more revenue), moderate users $9.99 (unchanged), and low users $4.99 (converting at 4% instead of 1%, = more total revenue).
The estimated impact:
- Highly engaged: 100,000 × 30% × 12% × $14.99 × 12 = $647,640 (+20% vs. static)
- Moderately engaged: 100,000 × 50% × 5% × $9.99 × 12 = $299,700 (unchanged)
- Low engagement: 100,000 × 20% × 4% × $4.99 × 6 = $239,760 (+100% vs. static)
- Total: $1,187,100 vs. $959,040 with static pricing = 24% revenue uplift
This is why dynamic pricing matters. It's not magic, but it's a systematic way to optimize revenue by matching price to user value.
The Five-Step Process of Dynamic Pricing
Step 1: Collect Behavioral Signals
Your app collects data as users navigate:
- Feature usage (which features do they use?)
- Session frequency and duration
- Content consumption depth
- Onboarding completion (did they set goals, complete setup?)
- Device type, OS, location
- Acquisition source (organic, paid, referral)
This data is collected in real-time and stored in your backend.
Step 2: Predict Purchase Probability
Using historical data and machine learning, you predict: "What's the likelihood this user will subscribe if shown a paywall today?"
This is based on their behavioral signals compared to historical cohorts. A user who's spent 30 minutes in your app today has higher purchase probability than someone who installed it 2 hours ago.
Step 3: Predict Lifetime Value (LTV)
You also predict how much revenue this user will generate over their lifetime as a subscriber. This accounts for:
- Cohort retention curves (users from certain acquisition sources have different churn rates)
- Demographic factors (certain regions, age groups, device types have different LTV)
- Behavioral indicators (engagement level predicts retention)
Step 4: Select the Optimal Price
Your optimization algorithm determines: "Given this user's predicted purchase probability and LTV, what's the price or offer that maximizes expected revenue?"
Expected Revenue = Price × Purchase Probability
For example:
- User A: 30% purchase probability × $14.99 = $4.50 expected revenue
- User B: 8% purchase probability × $9.99 = $0.80 expected revenue
- User C: 3% purchase probability × $4.99 = $0.15 expected revenue
But you also care about LTV. A user with high predicted LTV should get a lower price to ensure conversion, since the lifetime value will more than make up for it. The optimization considers both immediate conversion and long-term value.
Step 5: Deliver the Personalized Paywall
Your app serves the user the paywall with their assigned price or offer. The user either subscribes or declines. You measure the outcome (conversion, churn, LTV) and feed this back into your prediction models to improve future decisions.
Dynamic Pricing vs. Geo Pricing
Geo pricing adjusts prices by country or region, usually to match local purchasing power. For example, an app might charge $9.99 in the US, £7.99 in the UK, and ₹399 in India.
Geo pricing is about location-based value adjustment. Dynamic pricing is about individual user value adjustment.
You can use both: geo pricing to adjust for purchasing power by country, and dynamic pricing to adjust for individual user engagement and LTV within each country.
Dynamic Pricing vs. A/B Testing
A/B testing shows different prices to random segments and measures which performs better overall. Example: Test A shows $9.99 to 50% of users, Test B shows $14.99 to 50%, and you measure which generates more revenue.
The result of the test: one price wins and becomes the new baseline.
Dynamic pricing is different: instead of finding a single winning price for everyone, you're determining the optimal price for each individual user.
A/B testing is more efficient for discovering which price is best in general. Dynamic pricing is more efficient for maximizing revenue at scale. Many apps use both: run A/B tests to understand user preferences, then implement dynamic pricing to operationalize those insights.
Can You Show Different Prices on iOS?
Dynamic pricing is allowed on iOS and Android. Apple allows subscription apps to:
- Create multiple subscription products at different price points
- Use introductory pricing and promotional pricing
- Show different products to different users based on eligibility rules
The key constraint: you can't change the price of a single product at runtime. Instead, you create multiple products (e.g., "Premium_$4.99", "Premium_$9.99", "Premium_$19.99") and determine which product to present to each user.
For more details, see our guide: Is Dynamic Pricing Allowed on iOS?
Risks and Compliance
Risk #1: User Backlash
If users discover they're being charged different prices, they may feel cheated. The key to avoiding this: be transparent. Users should always know the actual price before they tap "Subscribe." Hidden or misleading pricing is both unethical and violates app store policies.
Risk #2: Low Purchase Probability Spiral
If you show too aggressive a discount to low-engagement users, they may develop higher churn because they feel undervalued or don't have sufficient commitment. Dynamic pricing should be used to improve conversion, not to subsidize users with low inherent value.
Risk #3: App Store Rejection
If your dynamic pricing strategy violates app store guidelines (e.g., misleading pricing, deceptive framing), your app could be rejected. Use legitimate introductory/promotional pricing mechanisms, and ensure pricing is transparent and honest.
Dynamic Pricing Software Requirements
To implement dynamic pricing at scale, you need:
- Behavioral data collection: Track user actions in real-time
- Prediction models: ML models to predict purchase probability and LTV
- Pricing optimization engine: Logic to determine the optimal price for each user
- Product management: Ability to create and manage multiple subscription products (iOS/Android)
- Paywall rendering: Show the right product and price to each user
- Measurement: Track conversion, churn, and revenue impact
Building all of this in-house is complex. This is where platforms like Botsi come in.
How Botsi Implements Dynamic Pricing
Botsi's platform provides:
- Signal collection: Automatic collection of behavioral signals from your app
- Prediction models: Pre-trained ML models to predict purchase probability and LTV
- Pricing rules engine: Define your own pricing rules (e.g., "If LTV > $500, show Price A"), or let Botsi's optimization algorithm determine the best price
- Product management: Manage iOS and Android subscription products from one dashboard
- Paywall variants: Create paywall variants that adapt to each user's assigned product and price
- Measurement: Detailed reporting on conversion, churn, and revenue impact by segment
Who Should Use Dynamic Pricing?
Dynamic pricing is most effective for apps with:
- $50-100k+ MRR: You need sufficient scale to benefit from personalization. Smaller apps should focus on optimizing single prices.
- 500+ conversions/month: You need enough conversion data to build accurate prediction models.
- Diverse user base: Large variation in user engagement and LTV makes dynamic pricing more valuable.
- Willingness to test: Dynamic pricing requires running tests and iterating on strategy.
If you're below these thresholds, start with static price optimization: run A/B tests to find your optimal price, then focus on improving conversion rate and retention. Once you scale, implement dynamic pricing.
Detailed FAQ
Is dynamic pricing ethical?
Yes, when done transparently. Personalizing price to match user value is similar to how airlines sell tickets—different passengers pay different prices based on demand and value. As long as the actual price is clear before purchase and you're not using dark patterns, it's ethical.
Does dynamic pricing hurt retention?
Not when properly implemented. Users offered lower prices are carefully selected as having lower inherent value but higher price sensitivity. They should have similar or better retention than higher-priced users, because the price matches their willingness-to-pay. The risk is charging too high too fast, which can increase churn.
What's the ROI of dynamic pricing?
Varies by app, but we typically see 10-30% revenue uplift in the first year of implementation. The uplift comes from: increased conversion (users who were priced out at $9.99 but willing to pay $4.99), increased revenue from high-LTV users (willing to pay $14.99+), and improved retention (prices matched to willingness-to-pay).
How does dynamic pricing interact with paid UA?
Dynamic pricing and paid UA are complementary. Paid UA determines which users to acquire. Dynamic pricing optimizes monetization for those users. Together, they create a full-funnel optimization strategy: acquire the right users at the right cost, then monetize them at the price they'll accept.
Can dynamic pricing work without ML models?
Yes. You can use simple rules-based pricing: "If engagement > threshold, show Price A. Otherwise Price B." ML models are more powerful because they can consider multiple signals simultaneously, but rules-based pricing can also improve revenue.
How do I track whether dynamic pricing is working?
Key metrics: paywall conversion rate (did it improve?), 7-day and 30-day churn by price tier (is higher price causing higher churn?), and 13-month LTV projections by price tier (which price tier generates most long-term revenue?). You should also A/B test dynamic pricing vs. your current strategy to isolate the impact.
Real-World Examples
Read about how Mojo has had success with their localized pricing tests. Looking for more A/B test ideas to try for your app? Find more here.
This article on Retention.Blog provides a helpful overview.
Conclusion
Dynamic pricing is the practice of adjusting subscription prices at the individual user level based on predicted purchase probability and lifetime value. At scale, it can improve conversion, retention, and revenue—typically by 10-30% in the first year.
The key to success: collect behavioral signals, build or use prediction models, define a pricing optimization strategy, and measure impact on key metrics. Start simple (rules-based pricing), then advance to ML-driven optimization as you scale.
For apps at $50k+ MRR with diverse user bases, dynamic pricing is increasingly becoming table-stakes for revenue optimization. The apps not using it are leaving 10-30% of potential revenue on the table.
