Recommendation engines

Many brick-and-mortar and online retailers collect data about their customers' shopping habits. However, many of them fail to properly utilize this data to their advantage. Graph databases, such as Neo4j, can help assemble the bigger picture of customer habits for searching and purchasing, and even take trends in geographic areas into consideration.

For example, purchasing data may contain patterns indicating that certain customers tend to buy certain beverages on Friday evenings. Based on the relationships of other customers to products in that area, the engine could also suggest things such as cups, mugs, or glassware. Is the customer also a male in his thirties from a sports-obsessed area? Perhaps suggesting a mug supporting the local football team may spark an additional sale. An engine backed by Neo4j may be able to help a retailer uncover these small troves of insight.