Welcome to the Game of Pricing. Setting the right price for your IAPs isn’t one-size-fits-all. Many games stick to the most basic default setup: all products for the same price in all markets. This ignores how much players can actually afford or are willing to pay.
“Game of” is a series of articles that aims to explain various areas of growth optimization where we recognize discernible levels of progression, from the most basic solutions up to the most advanced ones with the biggest uplift potential.
About this article
This article aims to provide a beginner-friendly high level overview of different pricing strategies for IAPs. This is relevant to anyone who is working on a mobile free-to-play game launched on Google Play or App Store and who wants to learn about the possibilities for revenue growth through smarter IAP pricing.
About authors
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Motivation: Why $4.99 starter pack does not work everywhere?
You open a game and start playing.
You get quite engaged, mechanics are great, progression feels nice.
Then an offer pops up. Gems, gold and a legendary chest with the things you like.
You are in Philipines playing on a $80 Android phone. The offer’s price: 299 PHP (~5 USD). That single offer can cover your food for couple of days.
You tap X to close the offer.
This exact situation has just happened somewhere in some game right now as you read this sentence.
Setting the right price for your IAPs is not a one-size-fits-all. So why do we see so many games stick to the most basic default setup - with all products priced the same globally, just converted to local currencies?
Is it because it is the easiest? Probably yes.
Does this leave money on the table? Most likely also yes.
Ignoring how much players can actually afford or are willing to pay quietly caps your game's revenue potential. In lower-income countries, $0.99 might equal a meal's cost - a big ask for virtual gems. Flip to San Francisco, where a latte runs $10+, and that same $0.99 feels like pocket change. Take a $4.99 starter pack: it's 30 minutes of US minimum wage, 3+ hours in India, and just 15 minutes in Switzerland. Yet most games slap on the same price tag globally (spoiler: minimum wage isn't the sharpest metric - we'll unpack better ones ahead).
Smart pricing is your first power-up: it clears barriers in budget markets while harvesting more from high-spenders. This isn't about slashing prices blindly - it's tuning value to economic reality. Swiss players might happily pay premium, while Indonesian ones need tailored hooks to convert. And what if you want to level up and go even further and start taking into account individual user-level purchasing and gameplay behavior?
Let’s play the Game of Pricing and see how you can add 10%, 20% or maybe even 50% in net revenue uplift to your own game.
We provide our framework of four progressive levels of pricing optimization:
Tutorial: Static Pricing (Platform Default)
Level 1: Geographical Pricing
Level 2: Value-Based Localization
Boss Level: Behavioral Personalization
Each level increases in complexity, resource requirements, and revenue potential, with clear graduation criteria based on your studio's capabilities and current revenue.
Tutorial Level: Static Pricing (Platform Default)
What it is & primary objective
Static pricing is the default setup: one global price list auto-converted to local currencies by App Store or Google Play. The goal? Get your game monetizing quickly with minimal setup, establishing a baseline for future upgrades.
Players see uniform pricing worldwide - a $4.99 pack shows as ~₹415 in India or ~CHF 4.50 in Switzerland via platform conversion. No custom tweaks, pure plug-and-play simplicity.
Revenue potential
This is your baseline (100% revenue index). It works for early launches but limits growth - expect flat performance in diverse markets. This is the foundation for measuring improvements later.
When to move to the next level
You've gathered 1-3 months of stable data, hit $5K+ monthly IAP, and spot geographic disparities (e.g. low conversion in T2/T3 countries). Time to progress to Level 1.
Level 1: Geographical (Regional) Pricing
What it is & primary objective
Geographical pricing tweaks IAP costs based on country economics, going beyond auto-conversions. The objective? Align prices with purchasing power - boost conversions in budget markets while extracting more from affluent ones. Example pricing points of a starter pack: $2.49 in India, $4.99 in the US, $6.99 in Switzerland.
Player-facing changes & expected impact
Players encounter uniform adjustments per country - all IAPs scale by the same percentage. A 30% Swiss hike turns $9.99 into ~$12.99 and $49.99 into ~$64.99. It's a subtle shift: locals see prices that feel fairer, potentially sparking more buys.
Revenue uplift potential: 10-50% vs static pricing for affected countries. Overall uplift hinges on your market mix (e.g. heavy US reliance might mean smaller total gains).
And remember, the win isn't blanket conversion spikes - it's hitting that LTV sweet spot where ARPU gains outweigh any conversion dips. In other words, lower conversion at higher price point might outweigh the gain from higher conversion at lower price point.
Prerequisites & readiness check
To dive into this level, your game needs to be live with stable baseline metrics and a presence in at least three countries that contribute meaningful revenue - think of it as having enough map coverage to spot the opportunities. You'll want a minimum of $3-5K monthly revenue from non-primary markets to justify the effort and see real returns.
On the technical side, needs are minimal: pricing changes happen right within App Store Connect and Google Play Console, with basic analytics like Firebase or Unity Analytics providing the insights into the current revenue metrics.
Start with a manual process, then scale up using automation tools as things grow, and make sure you can export data for quick analysis in something straightforward like Google Sheets.
As for team expertise, a single person can handle this - even solo developers pull it off - as long as there's some data analysis know-how for evaluation and basic platform management skills to navigate the App Store or Google Play UI. No custom development is required, keeping this level accessible for smaller studios ready to expand their horizons.
Approach (how to do it)
1. Analyze the market:
Kick off by diving into your game's performance metrics, breaking down conversion rates, ARPU, and LTV by country and platform to uncover hidden opportunities - like regions where players browse but rarely buy. For best clarity into behavior utilize Day X cohorted metrics (at the start focusing on Day 7 conversion, ARPPU, LTV).
Layer in economic benchmarks to guide your adjustments, starting with accessible ones but choosing wisely: the Big Mac Index offers a quick, fun snapshot of affordability (comparing burger prices worldwide), but it's not sufficient on its own since it focuses on physical retail costs like ingredients and labor, which don't align perfectly with digital IAPs - plus, it's skewed by local factors such as taxes, import duties, and McDonald's limited presence in places like much of Africa, potentially overlooking nuances in gaming markets.
For better precision, turn to Purchasing Power Parity (PPP) data from sources like the World Bank or IMF, which compares a broad basket of goods and services for a comprehensive view of real buying power (pros: highly accurate for overall economies, accounts for inflation; cons: can be complex to apply and sometimes lags behind rapid market changes). OECD price level indices provide detailed comparisons for developed nations, highlighting relative costs across categories (pros: granular and reliable for T1/T2 markets; cons: limited to OECD countries, missing emerging T3 regions).
Even Spotify's regional pricing model can inspire, as it tailors digital subscriptions to local willingness-to-pay (pros: directly relevant to app-based entertainment; cons: data isn't always public and it's music-focused, not game-specific). Blend these for a balanced view before moving forward.
2. Build a pricing matrix:
With your research in hand, create a clear pricing matrix (in a spreadsheet) that applies percentage adjustments relative to a base country (typically the US) - for example, dropping prices 30-50% in T3 markets like India or Brazil while bumping them 10-20% in affluent spots like Australia. Keep it simple at first by picking just 1-2 countries with solid revenue streams for your initial test, ensuring you can measure impact without overwhelming your setup.
3. Implement the first changes:
Roll out the adjustments directly through platform tools like App Store Connect or Google Play Console, where you can set country-specific prices without custom coding. Once live, monitor for immediate glitches using basic analytics, and remember to communicate any big shifts transparently to avoid surprising your players.
4. Measure & evaluate:
Track progress with a close watch on Day 7/30 conversion rates, ARPU, and LTV by country, conducting monthly reviews against your pre-change baseline to spot wins or tweaks needed. Iterate by fine-tuning prices based on these insights - perhaps easing up if conversions dip too low - and gradually expand to more countries as patterns emerge, always aiming for that LTV sweet spot where revenue climbs without alienating players.
5. Scale up the effort:
As your regional setup grows beyond a handful of countries, introduce automation tools (like third-party platforms that handle bulk updates and currency fluctuations) to manage the expanding complexity without constant manual tweaks. This lets you respond nimbly to market shifts - such as economic changes or competitor moves - while running A/B tests on pricing variants to refine your matrix, ultimately turning geographical pricing into a seamless part of your monetization engine.
Operational complexity, time-to-signal, risks & mitigation
Complexity here is low, with no need for fancy data infrastructure or specialized tools - it mostly scales with the number of countries you're managing, like adding more zones to your game world.
You can launch in just days to 2 weeks, with first results appearing within a month, though the exact timeline depends on traffic volume in your target countries. Ongoing effort involves monthly performance reviews and monthly to quarterly pricing adjustments, keeping things manageable as you build momentum.
One key risk is player perception in T1 countries, where price increases might spark negative reactions - keep an eye on community feedback and reviews, and mitigate by implementing gradual changes for any planned larger hikes (similarly to what retail sellers use).
Technical risks are limited since the implementation is platform-native, reducing complications to a minimum. Overall, players generally expect regional pricing differences in digital products, much like varying costs in real-world markets, so acceptance tends to be high when handled thoughtfully.
When to move to the next level
You're ready to advance once you've validated success with positive results from multiple country tests over 3-6 months, showing your team is comfortable with country-level analysis and eager to explore player-level personalization. This will be the point where you are close to reaching diminishing returns from pure country-level adjustments, signaling it's time to taske a first step towards basic personalization which goes beyond basic geography for even greater rewards.
Level 2: Value-Based Localization - Beyond Currency, Into Culture
What it is & primary objective
Value-based localization builds on geographical pricing by adjusting not just the price tag, but the perceived value of IAPs to match local economic realities. This means offering more content or better deals in lower-purchasing-power markets (like extra gems or bonuses for the same local price) while providing premium positioning in affluent regions (smaller discounts or higher effective prices for the same content). The primary objective is optimizing first-time conversion rates without sacrificing long-term LTV, finding the sweet spot where increased value drives more buyers while maintaining healthy average revenue per paying user (ARPPU).
Essentially, you're experimenting with offer structures to balance conversion gains against ARPPU impacts - for example, deep discounts in India might spike initial buys but risk lowering overall spend if not calibrated right.
Player-facing changes & expected impact
Players in targeted countries see adjusted offer content that feels tailored to their market. In lower-WTP regions like India, a $2.99-equivalent starter pack might include 50% more gems or gold than the US baseline, effectively giving a bigger discount. In higher-WTP spots like Switzerland, the same pack could have a smaller discount (higher effective price) but maintain premium appeal.
Similarly to the previous level, the key isn't blanket conversion boost - it's hitting an LTV equilibrium where more first-time buyers offset any ARPPU dip, or steady conversion in premium markets yields higher ARPPU.
Expected revenue uplift: 10-30% additional growth in affected countries compared to Level 1, driven by 20-50% better first-time conversion rates without proportional ARPPU losses.
Prerequisites & readiness check
To step into value-based localization, your game should already be live, showing stable metrics across several regions and generating at least a few thousand dollars in IAP each month. You don’t need huge volume per individual pack; the aim here is to lift conversion on under-performing offers, not chase raw scale.
You can begin by rolling out one revised offer to an entire country and tracking month-over-month results, but the real advantage appears once you add a remote-configuration system that serves different variants to specific user segments. That setup enables proper A/B testing, which is essential for spotting cannibalization and other long-term effects. Supporting it requires user-level analytics and raw event exports so you can follow each buyer’s LTV well beyond the first purchase.
On the team side, you will need a data analyst to handle those exports and interpret results, a developer to hook the client into your remote-config pipeline, and a product manager to design and coordinate the offer experiments across price points.
Approach (how to do it)
1. In-game performance analysis & value mapping (2-3 weeks): Review conversion rates, ARPPU, and ARPU by country to identify underperforming offers. Group countries by purchasing power and test hypotheses - e.g. how much extra value (like bonus items) boosts conversion in T3 markets without slashing ARPPU.
2. Offer design & A/B testing setup (2-4 weeks): Create variants: increase value (deeper effective discounts) in lower-WTP countries for higher conversion, and decrease value (smaller discounts) in higher-WTP ones for ARPPU gains. Set up A/B tests with control groups on standard regional pricing, ensuring you can measure both short-term metrics and 90-day LTV.
3. Implementation & iteration (ongoing): Deploy via remote config, starting with starter packs. Monitor for the equilibrium where conversion rises meaningfully but ARPPU declines gradually - adjust if discounts prove too aggressive and tank revenue. When you find the winner - the right price point and further testing doesn’t seem to move the needle, release the offer to the whole market.
4. Optimization & scaling (3-6 months): Refine based on data, expanding to more countries and offer types and price points. Testing methodology stays the same.
Beyond value-based localization: Once mastered, extend into cultural adaptations - like festival-themed bundles - but treat this as a separate advanced step to avoid blending content changes with pure pricing strategy.
Operational complexity, time-to-signal, risks & mitigation
Complexity here steps up to medium, involving A/B testing and user-level tracking that makes it more hands-on than the platform-native tweaks of geo pricing, yet still less demanding than the data wizardry of behavioral personalization - think of it as upgrading from a simple map to a multi-layer strategy board. Operationally, it scales with the number of offers and countries you're testing.
You can see initial conversion results in 2-4 weeks, but full validation - including the crucial cannibalization checks - can take up to 2-3 months to gather reliable LTV data. Ongoing effort includes regular A/B reviews and value tweaks before you fully understand what works in each market.
The number one risk is the aforementioned cannibalization, where enhanced starter packs pull spending from future purchases, potentially leaving your revenue stream lighter than expected. Another is ARPPU erosion if value boosts get too generous and train players to expect more for less, while testing errors could lead to temporary revenue dips if your variants aren't properly balanced. To mitigate these, run extended A/B periods to capture those long-term effects, start with conservative value adjustments and scale up based on solid data, and always maintain control groups while monitoring ARPU. If the results are negative, you can always roll back to the previous version.
When to move to the next level
Advance when you've optimized value across 5+ countries with consistent positive ARPU impact over 3-6 months, hitting $50K+ monthly IAP. Your team should handle A/B testing and value calibration comfortably, with diminishing returns signaling readiness for individual behavioral personalization at the Boss Level.
