Case Study

Mobile Growth · ASO

Google Play growth through ASO and acquisition optimization

Alakris built a conversion-focused ASO and traffic architecture that improved app discoverability and stabilized install flow from high-intent channels.

ASO systemmetadata, visual and ranking optimization
Install growthimproved conversion from store visits
Channel controlquality-focused acquisition structure
Client Context

Mobile product with growth potential but limited store performance

Business Goal

Grow installs without wasting paid budget

The product needed stronger visibility and better conversion from impressions to installs on Google Play.

Constraint

Ranking and conversion bottlenecks

Store listing quality, keyword structure and creative assets were underperforming for targeted traffic segments.

Challenge

Traffic existed, but install efficiency was unstable

Acquisition efforts did not fully convert into predictable growth due to weak listing economics and channel imbalance.

  • Inconsistent keyword ranking performance
  • Suboptimal listing visuals for conversion intent
  • No unified experimentation loop for store assets
  • Insufficient quality filtering in acquisition flows
Solution

ASO + performance framework with continuous optimization

Alakris implemented a practical growth engine combining store conversion optimization and channel quality management.

  • Keyword and semantic structure redesign for store discoverability
  • Visual conversion upgrades for icon, screenshots and first-screen communication
  • A/B testing cadence for incremental conversion lift
  • Campaign-quality controls to improve paid traffic ROI
Outcome

Higher visibility, stronger conversion, cleaner growth economics

Store Performance

Improved listing conversion

More install actions from comparable view volume due to better first-impression relevance.

Acquisition

More qualified installs

Traffic filtering and channel strategy improved install quality and post-install behavior.

Growth Model

Repeatable optimization loop

The team gained a system for continuous ASO iteration instead of one-off updates.

Why It Worked

Execution tied to conversion mechanics, not vanity metrics

Method

Hypothesis-driven optimization

Every change was tied to specific conversion assumptions and measured against business outcomes.

Focus

Quality over raw volume

The model prioritized sustainable growth economics, not short-term install spikes.

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