Fintech startup Analytics : Basics of Data analytics using SQL

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1.2 Fintech Analytics

Suppose we have a fintech startup that provides a mobile app for managing personal finances. We want to use data analytics to understand how our users are using the app and identify opportunities for growth.

To do this, we can use SQL queries to analyze data from our users’ transactions and app usage. Here are a few examples of questions we might want to answer, along with sample SQL queries that could help us do so:

  1. How much are our users spending per month, on average?
SELECT AVG(amount) as Average Spend
FROM transactions 
WHERE transaction_date >= DATEADD(month, -1, GETDATE());

This query calculates the average amount spent by our users in the past month (assuming the transactions table includes a column called amount and a column called transaction_date).

  1. What is the most popular category of transactions among our users?
SELECT category, COUNT(*) as Number of Transactions 
FROM transactions 
GROUP BY category 
ORDER BY Number of Transactions DESC;

This query groups the transactions by category and counts the number of transactions in each category. It then orders the results in descending order, so that the most popular category appears at the top.

  1. How many of our users are using the app daily?
SELECT COUNT(DISTINCT user_id) as Daily Active Users 
FROM app_usage 
WHERE usage_date >= DATEADD(day, -1, GETDATE());

This query counts the number of unique user IDs in the app_usage table that have used the app in the past day (assuming the app_usage table includes a column called user_id and a column called usage_date).

By using SQL queries to analyze data from our transactions and app usage, we can gain valuable insights into how our users are using the app and identify opportunities for growth. This can help us make informed decisions about product development, marketing, and other key areas of the business

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