I tried The Forage Job simulation, and I started with Quantium Task 1 Here is a review of the Task 1.
Task One: Data preparation and customer analytics
Conducting analysis on client's transaction dataset and identifying customer purchasing behaviors to generate insights and provide commercial recommendations.
What I learnt
- Understanding how to examine and clean transaction and customer data.
- Learning to identify customer segments based on purchasing behavior.
- Gaining experience in creating charts and graphs to present data insights.
- Learning how to derive commercial recommendations from data analysis.
Task Overview
Program: Quantium Virtual Experience
Platform: The Forage
Task: Data Preparation & Customer Analytics
Tool Used: Looker Studio
Dashboard: View Interactive Dashboard
📌 Overview
As part of the Quantium Job Simulation, Task One centered on preparing and analyzing retail transaction data to uncover customer behavior patterns and inform commercial decision-making. The process involved end-to-end data handling — from cleaning raw datasets to creating customer segments and deriving actionable insights.
The goal? Transform messy data into meaningful business intelligence.
🧠 Key Learnings
Through this task, I strengthened my ability to:
✅ Clean and structure transactional and customer-level data
✅ Segment customers by demographic and behavioral attributes
✅ Visualize business metrics through dynamic charts and graphs
✅ Derive commercial recommendations from data trends
🗃️ Data Dictionary (Selected Fields)
- Column Name
- Description
- DATE
- Raw date of transaction
- CLEANED_DATE
- Cleaned and standardized transaction date
- STORE_NBR
- Unique store identifier
- LYLTY_CARD_NBR
- Loyalty card number per customer
- TXN_ID
- Transaction ID
- PROD_NAME
- Name of the purchased product
- PROD_QTY
- Quantity of product purchased
- TOT_SALES
- Total transaction value
- LIFESTAGE
- Customer demography (e.g., Young Families, Retirees)
- PREMIUM_CUSTOMER
- Customer value tier: Premium, Mainstream, or Budget 🛠️ Task Breakdown
1️⃣ Data Preparation
- Checked for missing values, inconsistencies, and duplicates
- Standardized date formats and linked customer demographic data
- Ensured dataset integrity for seamless analysis
2️⃣ Customer Analytics & Segmentation
Used key metrics to uncover purchasing patterns and business drivers:
Metric
- Business Insight
- Total Sales
- Overall store performance
- Sales Drivers
- Top contributing customer & product segments
- Store Performance
- Regions generating the most revenue
- Demographic Analysis
- Lifestage and segment-based behavior trends
💡 Key Insights
Customer Segments:
Mainstream shoppers led with $749.7K in total sales, followed by Budget ($675.2K) and Premium ($505.5K) segments indicating high engagement from value-focused customers.
Lifestage Trends:
- Older Singles/Couples ($401.8K) and Retirees ($366K) topped the spending charts, reflecting strong engagement from mature consumers.
- Sales Over Time:
- December 2018 recorded the highest sales, likely due to festive season demand. February 2019 saw the lowest — a typical post-holiday dip.
Top Products by Sales:
- Dorito Corn Chips Supreme 380g
- Smith’s Crinkle Chips Original Big Bag 380g
- Smith’s Crinkle Chips Salt & Vinegar 330g
- Kettle Mozzarella Basil & Pesto 175g
- Smith’s Crinkle Original 330g
📊 Dashboard Overview
Explore the interactive Looker Studio dashboard, which highlights:
- Sales by customer segment
- Revenue by customer lifestage
- Product-level performance
- Top-performing stores
It serves as a live tool for analyzing business performance and identifying key opportunities at a glance.
🧠 Final Thoughts
This task offered a hands-on deep dive into customer analytics — from wrangling raw datasets to delivering actionable insights. It sharpened my ability to connect numbers with narratives and underscored how data can directly inform commercial strategy.
A standout experience in bridging data with decision-making.
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