About Wordling mobile game by Joyglitch
Wordling offers a captivating word game experience that combines relaxation and mental stimulation. The indie game grew rapidly and took its spot among the most well-received projects in the vertical.
Joana Marques Co-Founder of JoyglitchOur marketing efforts became profitable 2 months after starting to work with AppAgent. They are very proactive in raising issues and solutions. Whenever we needed to adapt our strategy, they were the first to come up with alternatives.
The Brief
Stop the declining DAU and determine whether paid user acquisition can become the new growth engine.
The Solution
We enhanced marketing analytics with new predictive models helped to optimize the campaigns based on user value. The early performance signals enabled a swift pivot from underperforming campaigns.
With a better understanding of data, highly effective campaigns emerged from ad/GEO experiments on Google, Meta, and TikTok Ads.
Keyword optimization on iOS allowed us to double the volume of organic users from the US, Spain, and Italy.
AppAgent’s Conclusion
“The flexibility of the Growth Program allowed us to prioritize holistic growth. By utilizing ASO, UA, analytics, and ad production operations, we were able to effectively achieve the client’s goals. Moreover, having additional control over delivery time was immensely beneficial in promptly addressing any issues. This streamlined the development team’s workload and enabled them to primarily concentrate on enhancing the product, leading to improved monetization and ultimately completing the growth cycle.”
Frequently Asked Questions
What problem was Wordling facing before working with AppAgent?
Wordling was growing overall but struggling at the top of the funnel: daily active users were declining, and it wasn’t clear whether paid user acquisition could become a reliable growth engine. The key question was whether UA and ASO, done properly, could reverse the trend and deliver profitable, scalable growth.
How did AppAgent make Wordling’s marketing efforts profitable in just two months?
We started by enhancing marketing analytics with new predictive models focused on user value. Early performance signals helped us quickly shut down underperforming campaigns and reallocate spend to stronger segments. Combined with structured ad/GEO experiments on Google, Meta, and TikTok Ads, this approach allowed the team to reach profitable UA within two months.
What role did ASO play in tripling monthly installs?
ASO was a key part of the result. Through keyword optimization on iOS, we were able to double organic users from the US, Spain, and Italy. This organic uplift supported the paid efforts: more relevant traffic, better store conversion, and a stronger foundation for campaigns to scale.
How were predictive models used to optimize UA campaigns?
The predictive models helped us estimate user value early in the lifecycle. Instead of waiting weeks to see full LTV, we used early metrics to decide which ads, audiences, and countries were worth keeping. This approach turned UA into a more data-driven, low-latency decision process, reducing wasted spend and improving ROAS.
Why did you test campaigns on Google, Meta, and TikTok Ads specifically?
Each of these networks brings different strengths:
– Google Ads for intent and broad reach,
– Meta for interest-based targeting and creative testing,
– TikTok Ads for high-impact creatives and trend-driven formats.
By running ad & country experiments across all three, we could identify where Wordling’s audience responded best and where performance was strong enough to justify scaling.
What is the AppAgent Growth Program and how did it help Wordling?
The Growth Program is our framework for managing ASO, UA, analytics, and ad production together, rather than as separate workstreams. For Wordling, it meant we could prioritize the most impactful tasks, react quickly to new data, and keep the marketing roadmap aligned with the product team. This reduced pressure on development, improved monetization, and helped complete the “growth cycle” from acquisition to revenue.
What was the main risk in Wordling’s UA setup before working with AppAgent?
The main risk was investing in UA without clear visibility on long-term user value. Without predictive models, it was difficult to know which campaigns would generate profitable users. By introducing early value signals and structured testing, the team reduced uncertainty and made scaling decisions more reliable.
What can other indie studios learn from the Wordling case?
The main lesson is that UA and ASO can become a true growth engine even for a small team—if they’re backed by solid analytics and a clear testing strategy. Starting with predictive models, focused country experiments, and targeted keyword optimization can quickly reveal whether paid user acquisition is a viable path, without overspending on unproven campaigns.