yonilev/GooglePlayStoreApps

📱 Google Play Store 2020 — What Made an App Go Viral?

A data-driven EDA of 9,436 apps released in 2020, exploring the patterns behind viral success on the Play Store.

---

🎬 Video Walkthrough

Can't see the video? Click here to watch

---

🔬 Research Question

"What made an app released in 2020 go viral?"

Defined as reaching 1M+ installs by June 2021 — within 6–18 months of launch.

2020 was chosen deliberately: the COVID-19 pandemic drove unprecedented mobile app adoption, and since the data was collected in June 2021, apps from 2020 had a consistent 6–18 month measurement window.

---

📊 Key Visualizations

1. The Winner-Takes-All Market

86% of apps never exceed 10K installs. Viral apps (<1%) are a thin but real right tail.

---

2. Does Quality = Success? (Surprising Answer: No)

Rating and Installs show a weak negative linear correlation (r = −0.31). Viral apps attract polarising reviews — millions of users means more critics.

---

3. Monetization Strategy Matters

Ad-based and Hybrid (ads + IAP) apps reach significantly higher install counts. The price barrier for Premium apps dramatically limits reach.

---

4. The Gap Between Tiers is Enormous

Each tier is roughly 1,000x the previous — a textbook power-law gap.

Tier	Median Installs	n apps
Low	~100	8,135
Medium	~10,000	1,227
Viral	~1,000,000	74

🔑 Key Findings

1. The market is winner-takes-all. 86% of apps sit in the Low tier. Viral apps represent less than 1% of 2020 launches.
2. Monetization is the strongest predictor. Ad-based and Hybrid models (ads + IAP) significantly outperform Pure Free and Premium.
3. Higher ratings do NOT predict more installs (r = −0.31). Quality alone is not the driver — distribution and visibility are.
4. Having a rating at all is a strong proxy for traction. Rated apps contain virtually all Medium and Viral tier apps.
5. Fresher apps win. Regular updates correlate negatively with days_since_update (r = −0.25).
6. The Rating–Installs relationship is category-specific. A global r = −0.31 masks very different dynamics per category.

---

🗂️ Dataset

Property	Value
Source	Kaggle — gauthamp10/google-playstore-apps
Original size	2.31M rows × 24 columns
Subset	Apps released in 2020 only
Sample size	9,436 rows (Cochran's formula + stratified by Category)
Scraped	June 2021

Features in this dataset

Feature	Type	Description
App Name	string	Name of the app
Category	categorical	App category (48 unique)
Rating	float	Average user rating (NaN if unrated)
Rating Count	int	Number of user ratings
Installs_clean	int	Parsed install count
log_installs	float	log(1 + Installs) — for modeling
Size_MB	float	App size in MB (winsorized at 65.8MB)
Min_Android_ver	float	Minimum Android version required
Released	datetime	Launch date (all in 2020)
Last Updated	datetime	Last update date
Free	bool	Whether app is free
Ad Supported	bool	Whether app shows ads
In App Purchases	bool	Whether app has IAP
Editors Choice	bool	Whether app is Editor's Choice
has_rating	bool	Whether app has crossed rating threshold
app_age_days	int	Days from release to scrape date
days_since_update	int	Days from last update to scrape date
engagement_ratio	float	Rating Count / Installs
log_engagement	float	log(1 + engagement_ratio)
developer_app_count	int	Total apps by same developer in 2020
monetization_model	categorical	Pure Free / Ad-based / Freemium / Hybrid / Premium
success_tier	categorical	Target variable — Low / Medium / Viral

---

📓 Notebook

The full analysis notebook is available here: 👉 Open The Notebook in Google Colab

❓ Questions & Answers

Q1: Is the app market democratic — can any app go viral? No. 86% of apps released in 2020 never exceeded 10K installs. The market is winner-takes-all: less than 1% of apps reached the Viral tier (1M+ installs), and their median install count is roughly 1,000x that of Medium-tier apps.

Q2: Does a higher rating mean more installs? Surprisingly, no. Rating and Installs show a weak negative linear correlation (r = −0.31). Viral apps attract polarising reviews — millions of users means more critics. Quality alone is not the driver; distribution and visibility are.

Q3: Does monetization strategy matter? Yes — it's the strongest predictor in the dataset. Ad-based and Hybrid (ads + IAP) apps reach significantly higher install counts than Pure Free or Premium apps. The price barrier of Premium apps dramatically limits reach.

Q4: Does having a rating at all matter? Yes, dramatically. Apps with a visible rating contain virtually all Medium and Viral tier apps. This reflects a chicken-and-egg dynamic: installs drive ratings, ratings drive visibility, visibility drives more installs.

Q5: Does the Rating–Installs relationship hold across all categories? No. The global r = −0.31 masks very different dynamics per category. In Tools and Business, there is almost no relationship. In Entertainment and Games, high-install apps cluster at specific rating bands.

---

🔧 Key Decisions

Decision	What I did	Why
Subset to 2020	Filtered 544,882 apps released in 2020	Consistent 6–18 month measurement window for all apps
Stratified sampling	Cochran's formula → 9,436 rows, sampled proportionally by Category	Preserve category distribution while reducing computational load
Rating = 0 → NaN	Replaced zero ratings with NaN	Zero means unrated, not bad — imputing would distort analysis
Rating not imputed	Kept 54.9% of Rating as NaN, created has_rating instead	Missing rating is a meaningful signal, not random noise
Size_MB winsorized	Capped at 65.8 MB (IQR upper fence)	Extreme sizes are edge cases that distort correlations
Installs kept as-is	No capping on extreme install counts	Viral outliers ARE the story — removing them defeats the purpose
log_installs	Applied log(1 + Installs) transformation	Raw installs follow a power-law — log scale is needed for analysis
monetization_model	Combined Free + Ad Supported + In App Purchases into 5 categories	Three boolean columns carry more meaning as a single business model feature

## 👤 Author

**Yonathan Levy**
Econ & Entrepreneurship with Data Science Specialization
Reichman Uni
Class of 2028