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The amount of data generated each day in retail is enormous. That data has to be harnessed and analyzed to gain value. Edges between nodes in the graph database perfectly correspond with the relationships between buyers and products. Key connections can be discovered from all that data and used to build a product recommendation system, detect communities of buyers or optimize the supply chain.


Applying graphs

Suply chain optimization

Supply chain optimization ensures the optimal operation of a manufacturing and distribution supply chain. That includes the optimal inventory placement within the supply chain, minimizing operating costs, including manufacturing, transportation, and distribution costs. The goal is to maximize the profitable operation of the manufacturing and distribution supply chain.

If we observe a product's life cycle, it starts from the raw materials from which it was made and ends as a complete product at the retailer. Naturally, that life cycle can be represented using nodes and relationships in a graph database. Next, using graph algorithms, the supply chain can be optimized.

Product recommendation

Systems that are recommending which book to buy, movie to watch, music to listen to, etc., are becoming more popular each year. Organizations using product recommendation systems increase their revenue and create stronger relationships with their buyers. Sellers want to recommend each buyer something according to their taste. That can be done by finding similar buyers, recommending some trendy products from the same category that the user has bought, or finding similar products to the one the buyer has purchased. All that data is connected and is best shown in a graph database. Using graph algorithms and machine learning, you can quickly implement a recommendation system that suits your needs.

Discovering communities

By choosing the right audience, some brands can offer genuine value to the buyer and increase their revenue. The buyer sees that brand as a perfect fit for its needs and frequently buys its products. Therefore, communities discovery plays a vital role in purchase decisions. With graph databases and graph algorithms, the discovery of different communities of buyers has proven to be valuable and straightforward.

Where to next?

This text is a summary of one area that fits perfectly with the application of graphs. Therefore, we would like to have you with us when implementing some of these solutions. Share opinions, experiences and problems you encounter when working with Memgraph on our Discord server. We are here for you and we will help you along the way.