Data-Driven Decisions: Leveraging Google Play Store Data for Profitable App Development with SQL
- majiriok
- Dec 13, 2024
- 4 min read
Success in the competitive app market requires more than an innovative idea—it necessitates data-driven decisions from start to finish. Analysing Google Play Store data with SQL can provide valuable insights, directing developers to the most lucrative app niches. We'll investigate app categories, trends in downloads, reviews, and user behaviour. Whether you're a data enthusiast or an upcoming app developer, I invite you to join me as I reveal the insights hidden in the data and explore the best pathways to app development success!
ASK PHASE:
What are the top-performing app categories on the Google Play Store?
Which app categories have growth potential but are currently underrepresented in the market?
What impact do pricing models (free versus paid apps) have on user ratings?
PREPARE/PROCESS PHASE:
DATA:
The dataset used in this analysis was sourced from Kaggle, a renowned platform for data science and analytics enthusiasts. Kaggle hosts a vast repository of datasets contributed by the community, providing valuable resources for research, analysis, and machine learning projects.
STEP 1:
The process begins with an initial assessment of the dataset in Excel. This involves reviewing the data structure, identifying any inconsistencies, missing values, or formatting issues that may need to be addressed.
Below is the initial overview of the dataset:
STEP 2:
Using Excel's data manipulation and cleaning functionalities, various tasks are performed to ensure the dataset is accurate and consistent :
Removing duplicate rows or entries.
Deleting columns not useful for analysis.
Correcting formatting inconsistencies, such as date formats and numerical representations.
Standardizing text fields for consistency and ease of analysis
Columns renamed to improve clarity and understanding
Below is an overview of the cleaned dataset:
STEP 3:
After cleaning the dataset, it is exported from Excel to a CSV (Comma-Separated Values) file format. This format is commonly used for data interchange and is compatible with most database management systems.
Finally, the cleaned dataset in CSV format is imported into PostgreSQL for further analysis. This is typically done using PostgreSQL's built-in tools or through SQL commands to create tables and import the data.
ANALYSE PHASE:
EDA(Exploratory Data Analysis):
Understanding the characteristics and structure of the data is crucial. This process often uncovers issues in the data set that require attention before proceeding with further analysis. Identifying these issues early can save time and effort in subsequent stages of analysis. By using SQL, I can reveal trends in app categories, ratings, and more.
Count of records
Group apps by category and analyse key metrics (installs, price, ratings)
Determine number of apps per category
Determine number of apps per genre
Overview of app ratings
INSIGHTS:
The query below categorizes apps as "PAID" or "FREE" based on their price and calculates the average rating for each category. It aims to determine if paid apps have higher ratings than free apps. The query provides insights into the relationship between app pricing and ratings, aiding data-driven decisions in app development strategies.
The query below identifies the top 10 performing app categories by sorting them based on total downloads and average ratings. It calculates the total downloads and average rating for each category using aggregate functions such as SUM() and AVG(), respectively. This query provides insights into the most popular and highly rated app categories in the Google Play Store dataset.
The query below identifies the bottom 10 performing app categories, making them potential markets to target. It calculates the total downloads and average ratings for each category. By ordering the results in ascending order of total downloads and average ratings and limiting the output to the bottom 10 categories, the query highlights app categories with lower performance metrics, suggesting opportunities for targeted marketing or development efforts.
This SQL query extends the analysis beyond app categories to focus on specific genres. By grouping data by genres and calculating total downloads and average ratings, the query highlights genres with lower performance metrics. Sorting the results and limiting the output to the bottom 10 genres pinpoint genres with lower average ratings and total downloads, guiding targeted marketing or development efforts.
RECOMMENDATIONS (ACT PHASE):
Paid apps tend to have slightly higher ratings than free apps. This is because users who pay generally have higher engagement, valuing the app more. Therefore, if a stakeholder develops a valuable app, it could be profitable to charge a certain amount for it.
Dating, Maps & Navigation, Lifestyle, and Business apps have lower ratings and fewer total downloads. This could potentially be a market to target due to less competition. The lower ratings suggest that user needs are not being adequately met in these areas. By fulfilling these needs, there is potential for higher user ratings and increased market penetration.
Parenting, Educational, and Art & Design apps have the lowest ratings, indicating a potential market to target. Furthermore, the Art & Design app genre has the lowest total downloads, suggesting that a unique app created for this market may have a higher potential for success.
Gaming, Communication, Tools, Productivity, and Social are the top five app categories with the most downloads and highest ratings. The large number of apps in these categories suggests market saturation. Therefore, while developing an app in these areas might be challenging due to stiff competition, the saturation also signifies a high user demand.
CONCLUSION:
The data has spoken, offering valuable insights to guide your app development journey. Remember, this analysis is just the beginning. The ever-evolving app landscape demands continuous learning and adaptation.
Thank you for joining me on this data-driven adventure! I encourage you to delve deeper into the world of app data and explore its potential to fuel your entrepreneurial spirit.
Happy Learning!!!
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