Optimizing Retail Performance: How Looker Simplified Actual vs Projected Analysis and Cost Per Unit Calculation for a Major US Retailer

Client Overview

The client is one of the giant retailers in the US that offers a variety of food supplies, and services. They have a strong reputation for providing high-quality products and outstanding customer service.

Client Requirements

Create a looker dashboard to automate the Cost Per Unit calculation process for each variable between the current and previous fiscal year to eliminate the manual ETL and CPU calculation process in excel from Snowflake data tables.
Build a looker dashboard to track the actual vs projected values for each variable for the current fiscal year and showcase the actual values for each variable for the previous fiscal year.

Generated multiple line graphs to showcase the daily and weekly CPU trendline for each variable between current vs previous fiscal years' data.
Built multiple bar graphs to project the monthly and yearly CPU values between both the fiscal year for each variable.
Added week, month, and a variable filter to show the desired CPU values based on the user selection.
Created multiple single-value card graphs to show this year’s vs. last year’s weekly, monthly, and yearly CPU values as well as calculated WoW, MoM, and YoY % change values for the selected variable.
Created an overview table to show the CPU values for both fiscal years for all the variables.

Created a line chart to project the actual as well as the forecasted values for the current fiscal year and actual values for the previous fiscal year.
Added a variable filter to showcase the actual and estimated values for both fiscal years based on the user-selected variable.