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Don’t just follow the trends, create them ! Let’s turn those hidden connections into a sales surge!
What is this article about
Real world application of graphs and the K-NN algorithm to decode why customers buy certain products together. Think of it like predicting the perfect side dish for your main course (their original purchase)!
Why Read It
Understand how the K-NN algorithm, a cool machine learning tool, works in a practical business setting.
Learn to build a visual roadmap that turns complex data into actionable insights for your sales team
For anyone who loves the intersection of tech and retail!
The Problem
Customers are unpredictable, and traditional recommendation systems often miss the mark on those “add-on” purchases.
This means lost opportunities for businesses to boost sales and make their customers happier.
The Solution
Building a graph network that maps relationships between different products.
Using Neo4j GDS K-Nearest Neighbors (K-NN) algorithm to find patterns and predict what else customers will like based on past purchases
Why You Can’t Miss This
Get a unique, data-driven perspective to personalize the shopping experience for everyone.
Practical learning of boost sales by anticipating “surprise” purchases that go perfectly with the main item, same concept can be used to may diff business use cases
Know your customers on a deeper level – understand their secret needs and motivations!
Let’s go!
Jump into the fun of simulating an e-commerce network, testing the algorithm, and visualizing the results.
Let’s get cooking!
Explains the code behind the scenes in a simple, step-by-step way.
Closing thoughts
* This is just the start! Graphs can uncover even more about customer behavior, inventory, and so much more.
Recommendation systems are getting smarter by the day, and graphs are at the center of it.
This method is surprisingly easy to implement and gives you fantastic results
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