“Let’s see if I can find the item I’m looking for.”
“Found it! And the price is reasonable.”
Site Recommendation Engine: You May Also Like These Products.
“Yes, that’s exactly what I need. I think I’ll grab a few of those as well.”
Does this ring a bell? One of the main reasons why online shopping has taken off in a big way is the convenience and personalised experience that we as consumers can now get. But what goes on behind the scenes to make this happen?
More and more e-commerce and retail businesses are starting to understand the value of the recommender system in gaining a competitive advantage. The customer is now at the centre of the value chain. In order to adapt to this new reality, retailers must have real-time control of inventory, payment, and delivery systems.
Real-Time recommendations to online shoppers have been proven to not only increase revenue but also boosts sales and improve the customer experience. In fact, Amazon has demonstrated this by achieving double-digit revenue growth by incorporating recommendations into practically every step of the shopping process, from product discovery to checkout.
This ability to make compelling offers necessitates a new generation of technologies. That technology must be able to capture a customer's purchasing history as well as analyse their current decisions before matching them with the most appropriate product recommendations. All of this must be done in real-time before a customer visits a competitor's website.
The graph database is a key technology for enabling real-time recommendations, and it is quickly displacing traditional relational databases (RDBMS).
When it comes to connecting masses of data from the buyer and products (and connected data in general) to obtain insight into customer requirements and product trends, graph technology easily surpasses relational and other NoSQL data storage.
By design, graph databases are built to quickly query consumers’ previous purchases and capture any new interests displayed during their current online visit, both of which are critical for delivering real-time suggestions. Since relationships are recognised as first-class items in a graph database, retailers may connect customers’ browsing history with their purchasing history, as well as their offline product and brand encounters.
Graph databases are an important part of the technology platforms used by internet giants like Google, Facebook, and LinkedIn. However, although those early adopters had to develop their own in-house data stores from the ground up, off-the-shelf graph databases – such as Neo4j – are now available to any company interested in using real-time recommendation engines.
Because of its real-time data traversal performance, ACID-compliant transactions for data dependability, data modelling flexibility, and a wide range of developer productivity tools, retail enterprises can provide powerful new insights from data connections faster and at a lesser cost than before.
With Neo4j, retail will get to experience:
Increase in competitiveness: Neo4j allows new types of business capabilities that are often not available with other technologies, such as real-time data-driven decisions. Walmart, for example, employs Neo4j to make real-time product recommendations based on consumer preferences.
Better performance: Neo4j can explore any level of data in real-time due to its native graph architecture. When traversing data beyond three levels of depth, RDBMS and other NoSQL databases often suffer a severe performance hit.
Faster product time to market: Neo4j requires less code from developers than RDBMS alternatives. Less code means better quality and a greater project success rate. For connected datasets, Neo4j's performance is considerably improved, which can mean the difference between what a development team can do and what they can't.
Of course, these aren't the only advantages of Neo4j. Click here to learn more about Neo4j and how it can help you better analyse connected data and utilise relationships to deliver real-time product suggestions.