Project Summary:

As a Data Consultant, one of my specialties is analyzing client/ customer data, specifically for fitness studios. This Looker dashboard is a sample of a client and revenue analysis, using synthetic data generated by AI. Businesses often have thousands or millions of data points, but downloading large CSV files each time a new insight is needed is time consuming and does not result in a quick analysis. This dashboard is equipped to be connected to a database via API for live data updates as often as every 15 minutes. Now that’s some real At-A-Glance insights.

Objective:

Create a user-friendly dashboard for technical and non-technical stakeholders to quickly access, understand, and interact with client and revenue data needed for weekly reporting. Provide insights for overall trends to help management identify areas of opportunity for additional revenue.

process

1. CSV files with synthetic data generated by AI were imported into spreadsheets in Google Sheets.

   This is a great solution for one-time report generation, but for live data updates I would use BigQuery instead of Sheets. I then would have connected directly to the business database using an API key, eliminating the need to pull reports manually.

2. The Looker dashboard was created, with each chart connecting to the corresponding Sheets.

3. I created 5 pages to cover the typical data needs of a fitness studio who is focused on revenue and client foot traffic.

   1. Revenue and client traffic at a glance, to quickly monitor progress for the current month, quarter, or year.

   2. Revenue for all locations and singular locations, to monitor yearly and year-over-year trends.

   3. Client traffic, to quickly identify attendance trends by class and payment type.

   4. Memberships, to monitor client churn and identify popular demographics per location.

   5. Negative account balances, to keep track of which locations have large negative balances on client accounts (due to overdrafts, expired payment methods, etc.) that require immediate attention to resolve.

4. Data was filtered, sorted, and categorized by color to ensure integrity and consistency across all charts.

5. Date and Location filters were added to allow stakeholders to filter their data and see the precise numbers that they need.