How a Language Learning App Used Data Thinking to Revive Growth and Win the Market in 90 Days

01 | Background: Fully functional but lingering on the edge of growth

This is a mobile application focused on personalized language learning and spoken language training, aiming to enable every user to master a new language through interactive methods. The team is based in Taiwan, consisting of 15 members, and has gained some recognition in the East Asian market for its AI-powered speech correction system. In its first year, the company achieved steady growth through high-quality educational content and community features. However, this was followed by challenging realities:

- Insufficient keyword coverage, with nearly stagnant search volume from new users;

- Declining ROI on advertising campaigns, with high costs yet poor conversion rates;

- Weak growth in Western markets, where local user interests differ significantly from those in Asia;

- Fragmented data, with ASO and advertising operations functioning independently, making it difficult to develop unified strategies.

“Every day, we’re adjusting keywords, reallocating budgets, changing creatives, but no one can truly tell us what’s effective.”

— Iris Chen | Head of Growth

The team isn’t lacking effort; they simply lack a “growth roadmap.” They have data, but no actionable strategy has been formed.

02 | Core dilemma: Trapped in the quagmire of ‘emotional growth’

Before collaborating, their growth strategy was almost entirely intuition-based:

- Keyword selection based on “experience”;

- Ad optimization driven by “daily ROI”;

- Localization left to translators;

- Data review? “Not enough manpower, we’ll look at it next time.”

Sound familiar?

Their issues aren’t unique—they reflect a common problem among many small to medium-sized teams: actions without rhythm; data without insights.

03 | FoxData intervention: Implementing a three-step approach—“Insight → Localization”—to create a closed data loop

The first question posed by FoxData’s consulting team wasn’t about introducing new features but asking:

“Are you making decisions based on data, or just using data as an excuse?”

This question prompted the growth team to reevaluate and reorganize their fundamental approach to growth.

Stage One: Insight | Let Data Reveal the Real Problem

The partnership began with a complete scan using FoxData’s Health Diagnosis feature to assess the app’s keyword visibility.

No assumptions, no guesses—just a factual snapshot of performance.The Keyword Analysis panel revealed:

• Ranked Keywords: 617, just above 61% of competitors — meaning coverage was far from fully tapped;
• TOP3 Keywords: 35, slightly outperforming 67% of competitors — but too scattered to anchor brand visibility;

The report appears highly professional, yet it was a real wake-up call for us—

We believed we had already optimized numerous keywords, but the data revealed that only a handful of terms truly drive results.

— Fiona | Product Manager

FoxData’s diagnostic results not only highlighted areas marked in red as “To Be Optimized,” but also provided targeted recommendations:

which keywords should be prioritized for ranking improvements, which regions show potential gaps, and which terms are worth adding to the advertising test pool.

Based on this, the team outlined the next phase of action:

• Identifying high-potential keyword groups with low coverage;
• Setting short-term goals to increase the share of the “Top 3 Keywords”;
• Launching a bidding keyword plan to test the synergy between organic and paid strategies.

The key takeaway from this stage is that they finally understood the “problem” clearly—knowing what needs to be changed, where quick results can be achieved, and which areas no longer warrant time investment.

Stage Two: Action | Transitioning from Reactive Responses to Systematic Optimization

After receiving the diagnostic report, FoxData's consultants assist the team in transforming "problems" into "action plans"—

rebuilding the foundational operational logic for growth through the use of tools and structured rhythms.

Establishing a growth cadence, they develop three interconnected optimization cycles at varying frequencies, aligned with FoxData's recommendations.

Previously, the team was always reacting—editing copy when rankings dropped, pausing ads when conversions slowed.

Now every change followed a rhythm, supported by data, not impulse.

1.  Keyword Structuring: Order from Chaos

Using Keyword Explorer, the team organized their large keyword set into three groups:

• Core Keywords — directly tied to brand and primary features (e.g., “speaking practice”, “English pronunciation”);
• Expansion Keywords — capturing adjacent interests (e.g., “conversation learning”, “AI language assistant”);
• Test Keywords — 5–10 new terms evaluated per cycle.

This structure brought clarity: first strengthen the core, then expand reach efficiently.

2. Metadata Experimentation: Validate Before You Change

Through App Profile A/B testing, the team ran iterative experiments on titles, subtitles, and descriptions—each time changing one element only.

For instance, the subtitle “AI Speaking Coach” outperformed “AI Pronunciation Tutor” by +17% CTR in Southeast Asia after 10–14 days.

The takeaway? Micro-adjustments backed by data beat one-time overhauls.

Health Diagnosis also involves diagnostic optimization through metadata analysis.

3. Cross-Linking Ads and Organic Traffic | Creating the Feedback Loop

Previously, ad and ASO teams worked in silos.

With FoxData’s integrated ASA Keywords and Ad Creatives modules, both data streams now lived in the same dashboard.

FoxData's system not only displays the bidding distribution and popularity of keywords but also directly reveals which keywords generate downloads through synchronized advertising and ASO efforts. Through the Bidding Keywords Breakdown, the team can clearly identify high-potential terms (such as "Duolingo," "translation," "AI English learning") by analyzing their exposure and click trends in bidding, enabling the identification of the most valuable target keywords for organic optimization.

Meanwhile, the Ad Creatives display allowed the team to analyze performance by platform (Facebook, Instagram, YouTube) and compare conversion rates across regional creatives.

Using Custom Product Pages (CPPs), they matched ad creatives with the keywords and store listing versions that produced the best engagement.

This workflow established a measurable cycle:

1. Start with ASO to Validate Keyword Effectiveness

Use Keyword Explorer to monitor coverage and conversion performance of candidate keywords, running metadata experiments to identify which terms drive higher impressions and download conversion rates.

2.Scale High‑Performing Keywords through ASA Campaigns

Integrate keywords that demonstrate consistent performance in the ASO phase (high CTR and CVR) into ASA Keywords for precise bidding to amplify results.

3. Monitor Ad Results and Feed Insights Back into ASO

Continuously track click‑through rates and cost efficiency of paid keywords through Bidding Keywords Statistics; lower or pause bids for underperforming terms, while retaining high‑ROI keywords and incorporating them into ASO metadata to strengthen organic ranking.

ASO validation → ASA scaling → Data‑driven feedback,forming a low‑cost, controlled, and self‑reinforcing keyword growth loop.

Ultimately, they established a data-driven feedback loop: validating keywords for advertising campaigns, accumulating long-term ASO rankings, with the two systems mutually reinforcing each other to create a sustainable cycle of continuous growth.

4.  Listening to User Language | Turning Reviews into Search Signals

With AI Review Insights, the team aggregated thousands of app store reviews into actionable intelligence.

FoxData’s Smart Review Summary automatically classified feedback by sentiment and surfaced key themes:

• 45% negative (mainly performance and payment issues),
• 15% positive,
• 40% related to feature requests or minor bugs.

Among positive feedback, five recurring topics stood out:

1. Effective language practice — users valued structured and efficient training for improving speaking and listening;
2. AI feedback and correction — AI-driven pronunciation fixes built user confidence;
3. Tailored learning experience — personalized lesson plans matched different fluency levels;
4. Convenient and user-friendly — mobile learning made practice seamless;
5. Wide range of features — speaking drills, flashcards, and AI learning tools expanded engagement.

These “real user words” became data signals.

Phrases such as “AI pronunciation correction”, “personalized English course”, and “easy speaking practice” entered their next ASO iteration—creating a flow from user language → search language → conversion language.

5.  Localization | Translation Is Not Enough—Speak the User’s Language

During global expansion, the team noticed that while store pages were translated, search visibility remained weak.

For the first time, we analyze users' genuine search behaviors and semantic differences from a national perspective. The tool page clearly displays the keyword usage characteristics and popular search term frequencies across different country markets.

(Using the United States and South Korea as examples below):

🇺🇸 United States

Search results blended multiple languages and learning contexts, including “speak spanish”, “AI language tutor”, and “learn japanese speak”.

U.S. users frequently searched function-driven phrases like “how to speak english free app” or “AI language fluency software”, indicating an interest in multi-language learning aided by AI tools.

🇰🇷 Korea

Korean users favored local-language or mixed-script queries like “생활영어 (daily English)”, “무료영어공부 (free English learning)”, and “AI 영어대화 (AI English conversation)”.

The focus skewed toward oral practice and pronunciation support, showing cultural preference for conversational functionality and AI assistance.Based on these insights, the team localized both content and creative assets:

• For the U.S., emphasize “AI Language Coach” and “Speak Confidently”;
• For Korea, highlight “생활영어”, “AI 영어 연습”, and “무료 영어 회화 앱”;
• Align App Store visuals and CPP keywords with local linguistic nuances across markets for message consistency.

By validating localized keywords, they not only enhanced search relevance but also more accurately targeted learning motivations across different countries. Within three weeks, organic click-through rates in the US and South Korea increased by 22% and 27%, respectively, leading to a significant growth in cross-language search traffic.

04 | Conclusion: Data Makes Growth Scalable

This collaboration reshaped the team’s entire growth mindset:

Growth was no longer trial and error—it became a replicable, data-verified process.

With FoxData, they made three critical shifts:

• From reactive decisions → proactive insights;
• From gut instinct → data-based judgment;
• From direct translation → story-driven localization.

In the highly competitive EdTech industry, the team finally used data certainty to overcome growth uncertainty.

05 | Next Step: Build Your Growth Strategy on Data

Whether an early-stage startup or a global app publisher,

FoxData provides complete ASO and growth solutions—from keyword and competitor analysis to ASA ad optimization and multi-market localization

.👉 Start your free FoxData trial today and turn data into predictable growth.

FoxData — From Data to Predictable Growth.