UA and monetization tactics for puzzle and match 3 games

Puzzle and match 3 (also known as swap) games are an extremely resilient and profitable mobile gaming segment, consistently attracting millions of players worldwide. In 2024, the genre recorded 9.7 billion downloads, with a 14% year-over-year (YoY) increase in in-app purchase (IAP) revenue, accounting for 14.9% of total gaming IAP revenue.

Top studios such as Dream Games, Peak Games, and King have refined their strategies, turning flagship titles like Royal Match, Toon Blast, and Candy Crush Saga into global hits. Their continued dominance highlights the power of data-driven user acquisition (UA) and monetization strategies, demonstrating the importance of measuring key performance metrics to optimize marketing efforts with precision.

Launching a puzzle game in this competitive market requires a strategic pre-launch plan. A data-driven approach to positioning, testing, and analyzing early user engagement signals can determine whether a game gains traction or gets lost in the crowd.

How to position your game in a crowded market

New entrants must establish a clear differentiation strategy. Success depends on unique and engaging gameplay mechanics, social-driven engagement loops (like team-based competitions, asynchronous multiplayer challenges, gifting mechanics and leaderboards), or innovative monetization models.Understanding regional player behavior, refining UA strategies, and leveraging market trends are some of the essential components that can give developers an edge to drive early traction.

Pre-launch testing: A/B testing, ASO, and soft launches

A successful launch starts with rigorous testing to ensure optimal UA and retention:

• A/B testing ad creatives helps identify high-performing variations, maximizing UA efficiency from day one.
• App store optimization (ASO) is critical for organic discoverability—the top puzzle games continuously refine keywords, visuals, and conversion strategies. Read more about ASO in our definitive guide. 
• Soft launches in test markets provide valuable insights into retention, monetization, and gameplay balance, allowing studios to fine-tune performance before scaling globally.

Building an engaged user base before global rollout

Retention can actually start before launch. Studios that invest in pre-registration campaigns, community building, and influencer collaborations can generate early traction and secure an engaged player base ahead of the global or new-market release. Social features, live events, and exclusive beta access help drive initial engagement, setting the stage for a strong app store debut. 

According to Adjust data, puzzle games are growing steadily, with global installs up 12% and sessions rising 17% YoY in 2024. Regional trends also highlight distinct opportunities, with some markets far outpacing others.

The average session length for both puzzle and match 3 games increased in 2024. Puzzle games grew from 23.89 minutes in 2023 to 24.48 minutes. Meanwhile, match 3 games recorded longer session durations, rising from 28.31 minutes to 28.97 minutes. This rising in-app engagement trend makes it critical for developers to refine gameplay loops (the repetitive cycle of actions, rewards, and progression that keeps players engaged in a game) and monetization strategies to sustain long-term growth and make the most of the hype.

Retention rates for puzzle and match 3 games saw slight declines in 2024, reflecting increased competition for player attention. The longer session lengths indicate, however, that while less players are retaining, they’re spending more time in-app.  On day 1, puzzle games retained 20% of players, down from 21% in 2023, while match 3 games retained 24%, compared to 25% the previous year. 

By day 7, puzzle game retention remained steady at 7% in both years, showing no YoY change. However, match 3 games saw a slight drop from 12% to 11%, reinforcing the need for features like live events, social elements, and rewarded incentives to keep players invested beyond the first week.

Adjust data also shows some stability and some shifts in UA and monetization metrics:

Lifetime value (LTV)
LTV remains the most critical metric for long-term success, measuring the total revenue a player generates over their lifetime. In puzzle games, where monetization generally relies on a hybrid model of IAPs and ad revenue, accurately predicting LTV allows developers to optimize UA spend and increase profitability. 

Return on ad spend (ROAS)
ROAS determines the efficiency of paid UA campaigns and helps studios align acquisition costs with revenue potential. 

With rising UA costs, puzzle and match-3 game developers must ensure every ad dollar contributes to real growth. These games rely on a blend of paid UA, organic installs, and cross-promotion. However, traditional last-click attribution can inflate UA performance metrics by failing to separate organic users from ad-driven installs—if your data and analytics aren’t set up for full visibility.

Measuring true ad impact

Incrementality testing quantifies how many installs, sessions, and IAPs are directly influenced by ads. By distinguishing true paid impact from organic demand, developers can refine UA strategies, reduce wasted spend, and focus budgets on high-performing networks. Given the competitive landscape, incrementality testing ensures that ad spend is directed toward acquiring new high-value users rather than “re-engaging” those who would have installed anyway.

How to measure as ad effectiveness

Leading studios use structured experiments to assess real ad effectiveness, employing methods such as:

• A/B testing: Comparing a control group (no ads) against a test group (exposed to ads) to measure true ad-driven conversions.
• Budget holdouts: Temporarily pausing ad spend in select markets or user segments to establish a baseline for incremental lift.
• Causal inference: Using synthetic control groups to filter out external factors such as seasonality, cross-promotions, and store feature placements, ensuring precise attribution of ad impact.

How to implement incrementality tests for puzzle and match 3 games

To accurately measure ad impact, studios should:

1. Define test objectives: Measure installs, session engagement, retention, and monetization lift.
2. Segment test and control groups: Ensure a statistically valid split to prevent biased results.
3. Select the right testing method: Budget holdouts and causal inference are best for puzzle games with high organic discovery rates.
4. Monitor external factors: Adjust for store promotions, competitor activity, and seasonal trends to maintain accuracy.
5. Analyze results and optimize spend: Shift budgets to channels with the highest true incremental impact, avoiding overspending on non-converting players.

By accurately measuring ad effectiveness, developers can ensure they invest in players with the highest long-term revenue potential. This is where predictive lifetime value (pLTV) plays a crucial role—helping studios move beyond short-term UA tactics to build sustainable monetization models that maximize player lifetime value.

Predictive lifetime value (pLTV) leverages machine learning and historical player data to estimate a user’s long-term revenue potential early in their lifecycle. Unlike traditional LTV models, which require weeks or months of post-install data, pLTV enables developers to optimize UA spend in near real-time. This agility is critical in competitive markets, where reacting quickly to user behavior can be the difference between profitability and wasted budget.

How pLTV helps studios optimize UA budgets dynamically

Top studios integrate pLTV to dynamically adjust UA budgets, ensuring that resources are allocated efficiently. By analyzing engagement trends, developers can:

• Scale campaigns targeting high-retention players while reducing spend on low-LTV users.
• Bid more aggressively on ad networks where high-value users are more likely to convert.
• Adjust spend based on early revenue signals, scaling investment in regions or channels with stronger long-term profitability.

For example, markets like Brazil and Indonesia often yield higher ROAS due to lower CPIs and strong engagement rates, making them ideal for pLTV-driven UA strategies.

Machine learning’s role in pLTV and gaming growth

Advancements in machine learning (ML) have made pLTV models more precise, allowing studios to refine monetization and retention strategies based on behavioral patterns. For puzzle and match 3 games, ML-powered pLTV helps:

• Predict churn risk and trigger re-engagement campaigns before players leave.
• Optimize IAP pricing dynamically, offering personalized bundles based on spending behavior.
• Automate bid optimization across ad networks, ensuring the most efficient budget allocation.

Adjust empowers game developers with the tools to measure, analyze, and optimize user acquisition, engagement, and monetization. Adjust Measure provides precise attribution and analytics across paid and organic channels, enabling studios to identify high-value users, improve retention, and maximize return on investment (ROI).

Adjust’s InSight provides AI-driven incrementality testing, helping marketers measure the true impact of their campaigns and ensuring budget allocation is optimized. By isolating organic installs from paid UA, InSight prevents budget cannibalization, allowing developers to focus on high-performing channels. With real-time data and over 95% confidence intervals, studios can boost ROI.

Adjust’s Recommend leverages predictive analytics to forecast user pLTV, enabling dynamic UA budget adjustments and optimized retention strategies. By analyzing early user behavior, studios can identify high-value players, refine ad targeting, and scale campaigns efficiently. 

Ready to explore how Adjust’s next-generation solutions can transform your app marketing? Schedule a demo today.