The latest articles on DEV Community by Bolivan Teikeng (@bolivan_teikeng_feb3f25a3). https://dev.to/bolivan_teikeng_feb3f25a3 https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F4107459%2Fa2662af9-14c3-4e0c-8585-ca1802533424.jpg https://dev.to/bolivan_teikeng_feb3f25a3 en Bolivan Teikeng Thu, 03 Sep 2026 07:47:15 +0000 https://dev.to/bolivan_teikeng_feb3f25a3/title-scientific-market-analysis-google-play-store-app-performance-khe https://dev.to/bolivan_teikeng_feb3f25a3/title-scientific-market-analysis-google-play-store-app-performance-khe <h1> Scientific Market Analysis: Google Play Store App Performance </h1> <h2> 1. Introduction &amp; Assumptions </h2> <p>As part of the strategic planning for <strong>My MobApp Studio</strong>, we conducted a data-driven market study on the Google Play Store dataset to identify business opportunities for our upcoming mobile app.</p> <h3> Initial Assumptions (Hypotheses): </h3> <ol> <li> <strong>H1:</strong> Free applications represent the overwhelming majority of total downloads compared to paid applications.</li> <li> <strong>H2:</strong> The <code>FAMILY</code> and <code>GAME</code> categories dominate overall market installations.</li> <li> <strong>H3:</strong> Paid applications in the <code>FAMILY</code> category have lower adoption rates, with downloads concentrated in a few specific niche genres.</li> </ol> <h2> 2. Experimental Methodology &amp; Cleaning </h2> <p>We analyzed the official Google Play Store dataset containing thousands of Android applications.</p> <h3> Data Pre-processing Steps: </h3> <ol> <li>Removed duplicate entries to prevent skewed statistics.</li> <li>Cleaned numerical variables: <ul> <li>Stripped <code>+</code> and <code>,</code> symbols from the <code>Installs</code> column and converted values to floats.</li> <li>Removed <code>$</code> signs from <code>Price</code> and converted strings to numeric values.</li> </ul> </li> <li>Handled missing data (<code>NaN</code>) across all features to ensure clean quantitative evaluation.</li> </ol> <h2> 3. Results &amp; Data Insights </h2> <h3> Market Size Overview </h3> <ul> <li> <strong>Total Market Installations:</strong> Hundreds of billions of app downloads worldwide.</li> <li> <strong>Category Distribution:</strong> <code>GAME</code>, <code>COMMUNICATION</code>, and <code>TOOLS</code> hold the largest percentage of total installations.</li> </ul> <h3> Family Category &amp; Paid Apps Focus </h3> <ul> <li> <strong>Top Paid Family Apps:</strong> Popular paid family applications maintain steady installation numbers despite pricing barriers.</li> <li> <strong>Popular Paid Family Genres:</strong> Niche categories such as Educational games and Casual games drive the majority of paid family app downloads.</li> </ul> <h3> Pricing Insights </h3> <ul> <li> <strong>Mean Price per Category:</strong> Finance and Business categories show higher average prices due to specialized enterprise utility.</li> <li> <strong>Most Expensive Apps:</strong> Outliers are predominantly niche utility apps or luxury/status applications.</li> </ul> <h2> 4. Conclusion &amp; Strategic Recommendations </h2> <h3> Key Findings: </h3> <ul> <li> <strong>Free-to-Play Model:</strong> Launching a free app with in-app monetization or ads yields significantly higher initial market reach than a paid model.</li> <li> <strong>Target Category:</strong> Entering <code>FAMILY</code> or <code>GAME</code> offers the highest volume of potential users.</li> </ul> <h3> Next Experiments to Conduct: </h3> <ol> <li> <strong>Sentiment Analysis:</strong> Analyze user review text to identify common pain points in competing apps.</li> <li> <strong>Monetization Optimization:</strong> Evaluate price elasticity for paid features vs. ad-supported models within the <code>FAMILY</code> category.</li> </ol> datascience python pandas dataanalysis