How companies can emulate the success of the online dating industry using graph databases

Growth in the online dating industry, driven by an increase in mobile Internet access for both shopping and socialising, shows no sign of slowing down. To take just one metric, market research company Mintel estimates that the market will be worth £225m by 2019 in the UK alone.

That same probe found that cyber dating is now the fourth most common way Internet users over 18 have met someone else. No wonder that in this country alone there are now a staggering 1,400 websites dedicated to matchmaking.

As a sort of official mark of online dating’s going mainstream, the government stats body, the National Office of Statistics, now includes subscription to such a service in its on-going ‘shopping basket’ to calculate national monthly inflation.

The online dating proposition is successful – but also very simple. What such sites do, ultimately, is provide access to communities of people looking for the same thing – love, in all its various guises.

> See also: Understanding the maturing role of graph databases in the enterprise

Before online dating, the traditional way to meet someone was via one’s social circle, leisure interests, or in the workplace. The leading dating apps such as and eHarmony work to replicate this, albeit in a large and highly automated way.

Their secret is to make what you get highly customised and personalised. They do this by putting user profiles, preferences, interactions and connections at the centre of the system’s algorithms. That means the most compatible candidates are quickly tagged without having to scroll through thousands of potential profiles.

A look under the bonnet

All online dating services are underpinned by data, with the most accurate matchmaking sites are managing and organising that data using a special back-end technology called a ‘graph database’.

Graph databases differ from relational databases – which supply the majority of business databases – in that they specialise in identifying relationships between multiple data points.

Dating websites aren’t the only ones using this powerful technology, by the way; it is also at the epicentre of what made online giants such as Google and LinkedIn grow so quickly. Google, for example, was able to exploit the connections in each and every Web document to get better and faster research results, which has laid the foundations for its phenomenal success.

It turns out that relationships can be targeted and followed up by systems in graph databases very quickly, because the relationships don’t need to be specified to run a query, as you’d have to do in normal SQL. This makes criteria connections between people, preferences and personal profiles, for example, much faster and easier to spot.

We’ve only discussed online entities, so far. But graph databases are applicable to far more firms than just a or a Google. Indeed, the retail sector can learn a lot from the use of graph databases – and how they hold the key to transforming the customer experience.

Using this technology they can create Amazon-style ‘customers who purchased that, purchased this’ models, but also go one further and second guess what people may buy.

Basically, it’s one thing to analyse online transactions as a series of tables or isolated business tables – but if you can look at them as a graph, they can start to perform sophisticated personal recommendations on the fly, quickly and efficiently.

Graph databases literally have the power to match prospective customers with products and services that have direct appeal to them, much like having a personalised shopper – only online. Great idea, but expensive, I hear you say.

Actually – no. While the likes of Google built their technology from the ground up, graph tools and techniques are now widely available. That means businesses, even SMEs, can now use off-the-shelf graph databases to exploit real-time recommendations to influence and get closer to their customers.

Graph databases are on the Fortune 500's radar

It is no surprise that graph databases are growing faster in popularity than any other type of database – by around 250% last year alone. Market research company Forrester Research estimates that one in four enterprises will be using the technology by 2017, stating in its last look at the market that, 'A graph database allows organisations to think differently and create new intelligence-based business opportunities that weren’t possible before.'

Some of the big retailers have already picked up on the trend. US retail giant Walmart, for example, is using the technology to exploit data gleaned from customer purchases at its bricks and mortar and online stores to better gauge customer preferences, for example.

But for all sorts of retail firms, the message is clear: graph databases can now put data in a more intelligent context, which enables retailers to have a clearer picture of how a new product has performed on the shelves for example, or get a better sense of what consumers want and at what price points.

The future

The most successful online dating websites have been quick to spot that relationships are as important as data itself in a connected world. Interest in graph databases has the potential to quickly spread across the retail chain in parallel, as data volumes continue to explode and companies realise the importance of bringing data relationships to the fore to improve their bottom line.

> See also: Is the consolidating NoSQL market good news for the database industry?

The multi-layered data representation that’s now possible enables retailers to also take a look at customers’ wider activity on social media and through other digital channels. Think, who are they connected to, and which customers are most likely to speak about a brand and recommend services to others – all important business questions graph databases can easily help you answer.

Graph database technology provides an easy way to monitor all of this, so retailers and brands can align themselves with emerging trends, and become more efficient and effective at influencing the influencers.

That matters, as in the future, retailers will need not only to understand a customer’s past purchases but be able to instantly combine that knowledge with the latest interest shown during the customer’s current visit, as well as in their social media activity, and interrogate this data at lightning speed to serve up uncannily relevant and tailored recommendations and offers.

The benefits to retailers speak for themselves. Using powerful, proven techniques like graph databases make you into a leader, not a follower – plus help you win customers’ hearts by providing the best match for their desires.

Sourced from Emil Efrim, co-founder, Neo4j

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Ben Rossi

Ben was Vitesse Media's editorial director, leading content creation and editorial strategy across all Vitesse products, including its market-leading B2B and consumer magazines, websites, research and...

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