CBSE 12 AI Sample Project #7
🛒 Supermarket Customer Segmentation (AI Clustering)
Group customers into meaningful segments based on their monthly spending and visit patterns.
1️⃣ Choose how many segments you want
As a store manager, you can decide how many **customer groups** you want to analyse. For example, 3 broad groups (low / medium / high value) or 5–6 more detailed segments.
The system uses 1000 **fabricated customers** with realistic data (monthly spend, visit frequency, product preference, payment method) and groups them using K‑Means clustering.
2️⃣ See the segments on the chart
Each dot is one customer: vertical axis = Monthly spending (₹), horizontal axis = Number of visits per month. Dots with the same colour belong to the **same segment**.
3️⃣ Click a segment to view its profile
Click on a **Segment button** below to see a summary for that group – how many customers it has, how much they usually spend, how often they visit, and what they generally like to buy and how they pay.
4️⃣ What is this AI doing?
This tool uses K‑Means clustering, an unsupervised machine learning method, to automatically group customers who behave in a similar way. [web:221][web:225]
The algorithm: 1) Starts with K random centres, 2) Assigns each customer to the nearest centre (based on spending and visits), 3) Moves centres to the average of their assigned customers, 4) Repeats until the groups become stable. [web:211][web:216]
You can use these segments for **targeted offers**, **loyalty programs**, and **personalised marketing** (for example, special benefits for high‑spend frequent visitors).