Why We Need to Get Super Slick on Retail User Data

Emil Eifrem, CEO of Neo4j, explains how highly-personalised data-driven digital brand experiences rely on graph databases.


To keep up with the Amazons of the world, every retailer needs to get better at using customer data – but not all of us have the budget of a retail giant. By being agile, there are ways to beat Amazon at its own game.

One example is London-based augmented reality e-marketing agency Quander. It’s leading the way in digital marketing by taking customer experience to a new level. Quander’s service is all about applying AR (augmented reality) experiences to add a more personalised experience to a real-world situation: for instance, the Sky Studios immersive experience at The O2 Centre in London is a high footfall venue, open 364 days a year, where visitors can enjoy a range of interactive television experiences and create their own shareable photos and videos. A Quander web app allows customers to touch multiple experiences and gather their photos and videos in one place — from immersive VR that puts them inside TV shows, to a news desk where they can step into the broadcasting spotlight.

Not only does this make social sharing easy for customers, it means that Sky is able to draw insights from the various digital and in person touchpoints. Data keeps the user experience fresh, but also allows Quander the ability to offer more to other companies within the space, and to make recommendations for the success of future campaigns. It does that by tracking and curating individual experiences, allowing the experiential space to ‘learn’ about the user from the moment they check-in.

Data drives recommendations in context

Increasingly, this sort of data-fuelled marketing experience is at the forefront of smart
marketing and analytics. The problem is that hyper-personal recommendations can only be generated with the right technology, as that’s the only way to embed more intelligence into a recommendation engine.

For digital marketing specialists like Quander, the answer that’s emerging to do this is a graph database – specifically, using the power of graph software to identify customer
behavioural patterns and analyse them in order to make recommendations in context .

Take another example: The eBay App for Google Assistant is another intelligent recommendation technology that shows the kind of graph-powered hyper personalisation that brands need to offer to keep the interest of their consumers.
The app is essentially a chatbot powered by a way of organising data that supports
conversational commerce, and is designed to provide a seamless, personalised shopping experience, enabling you to check out the prices of products you request and find the best deal, simply by asking.

The system, which runs off Google voice technology, works by asking qualifying questions so it can quickly serve up relevant product examples to choose from. To accomplish this requires a powerful combination of NL (natural language) processing, ML (machine learning), accurate predictive analytics, a distributed, real-time storage and processing engine — and a graph database on top, to make all the real-time data connections required. And as eBay’s Chief Product Officer has publicly stated, existing product searches and recommendation engines were unable to provide the right contextual information within a shopping request. ​

This prompted him and his team to develop a bridge between regular search and natural language search using a graph database to process all the real-time data connections required.

The SQL queries are too complicated

Why do both of these great personalised recommendations systems use graph technology?

Because it’s perfect for helping refine a search against inventory with context, with ways of representing connections inside your data based on shopper intent. This allows the retailer to build up its internal profile of the customer and working with that profile is a great way of generating its hyper-personal and relevant suggestions. US shopping giant Walmart judges it now has “a perfect tool for real-time product recommendations”. Graphs are helping it understand not just “our online shoppers’ behaviour” but also the relationship between its customers and products, providing a perfect tool for real-time product recommendations.

Meanwhile, Wobi, an Israeli financial services price comparison website uses details of
customers to provide best value offers to users. To achieve this detailed level of customer understanding, it constructed a single customer database so it could rapidly drill down into each individual’s history and add new information on the fly. This is exactly the kind of feature that graph databases provide.

Graphs are also exemplary at supporting real-time decision making: a purpose-built, native graph database offers exceptional performance — even with millions of connections, the application is highly responsive to user requests. And why can’t you do all this using a standard relational database? Writing, building and running the SQL queries you’d need to even try and do this is too complicated, and often can’t deliver the information quickly enough — and for really useful recommendations, real-time contextual information has to be accessible.

Retail brands have huge volumes of data, but are struggling to find the best way to use it to help their customers and their bottom lines. If they are going to succeed in building these hugely sophisticated recommendations engines, the sector has to be able to leverage data connections and join the dots between the relationships. This is where graph software can help firms from Quander to Wobi — and you and your next retail offer.

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