Winning customer business by providing the most personalised product and service recommendations possible is something all companies need to do. To be able to meet that aim, you’re going to need all the help you can get. Lucky then that you have the key tool you need: all the customer data you’ve accumulated.

The trick, of course, is knowing how. Ideally, you need to look up the customer’s past purchases, query that history then match the customer to the product or promotion that’s the closest match to their interests, based on their social network footprint as much as previous buying patterns. To make your suggestions as sharp and relevant as possible also requires the ability to instantly capture any new interests shown in the current visit.

Fortunately, a new form of working with that data using ‘graph databases’ is giving all sorts of firms an invaluable leg-up in terms of making sense of all this super-connected data.

Compelling and relevant offers

Graph databases help due to their ability to effortlessly match historical data with live session data. It’s important to realise that they differ from traditional (relational) business databases in that they specialise in identifying relationships between very large numbers of data points, and so help you work with data better. They are also very powerful when it comes to working at scale, with large datasets: a recommendation engine in a graph database can be a million times faster than a relational database, offering 1,000x performance improvements despite a 1,000x increase in data size.

Many retailers are starting to get the graph message. Global sports and athletics giant adidas Group recently turned to just this technology to help it provide the most compelling and relevant data to its consumers, as well as offering enhanced features such as product recommendations.

Whether it’s to help direct a fan to a great piece of Real Madrid football merchandise or tailor content based on a consumer’s favourite basketball team, by combing through that growing digital consumer data pile (products, markets, social media, digital assets, brand content etc.), graphs are enabling adidas to deliver these new personalised features such as product recommendations and more.

Walmart in the US is also another leading-edge brand implementing graph database technology to take information gained from customer purchases at its physical and online stores to the next level. We’re also starting to see real-time personalised recommendations built on graph being used across numerous industries including travel, health and media.

Graph database tools and techniques are now also widely available. Originally, such advanced architectures were in-house, proprietary solutions, developed by web giants for their own proprietary use. But now, these great technologies are available off the shelf, either as open source or as commercial releases, or as a combination.

Meeting the challenge of personalisation

The bottom line is that any UK business can now do digital connections work that only a Facebook could do five years ago. Little wonder that the market is growing exponentially. According to a recent report by industry watcher DB-Engines, graph databases are gaining traction more than any other database category – growing at a staggering 300% since January 2013, for example.

Forrester Research estimates that one in four enterprises will be using the technology by 2017. Analysts are watching the graph explosion with sharp eyes, with Gartner predicting that for data-driven operations and decisions, graph databases are now “possibly the single most effective competitive differentiator”.

The attraction of graph databases is in their sheer power, as they can provide a 360-degree view of a customer in real time. This is why you and your team need to be looking at them to meet the personalised recommendation challenge that digital marketing seems to be crystallising into.

 

By Emil Eifrem, co-founder and CEO of Neo4j. 

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