Digital Love Lessons
From match.com to eHarmony, from Tinder to Zoosk, these sites are clearly making their mark through the smart use of technology. But what technology is that? If you thought regular old relational database software, think again!
Online dating has taken off in huge way, easily now one of the most popular ways to meet a new partner, with usage estimated by Pew Research as 27% among 18 to 34 year olds, 12% of 55 to 64 year olds – and 5% of all American married couples started off their journey to love in cyberspace, too.
From match.com to eHarmony, from Tinder to Zoosk, these sites are clearly making their mark through the smart use of technology. But what technology is that? If you thought regular old relational database software, think again: the technology in question is one that digitises interactions and connections to make sophisticated recommendations, based on the interests and attitudes and life experiences that you have in common with others.
Rapidly exploiting connections for user delight
Which is at it should be. There is nothing revolutionary here: before the online personal advert was more than a pixel dream in someone’s head, the traditional way of meeting partners was through extending your relationship network through friends, family, colleagues and neighbours. Really, that’s all online dating is – putting people’s connections at the centre of an algorithm to help you search for others with shared interests and attitudes.
All the successful dating sites are all underpinned by this ability to explore large volumes of connected data in order to generate recommendations. This is where a technology called graph database makes a difference.
Graph databases power a lot of things you are familiar with, even if you’ve never visited an online dating app. Digital leaders like Amazon and Netflix owe their breakthrough to having adopted this approach early on, for example, with the former’s success owing to its ability to rapidly exploit connections between people and product, while the latter’s ability to digitally harness people and content in has led to market dominance.
All sorts of organisations, across all sorts of industries, are now building powerful data engines to offer powerful personalised offerings, finding whole new use cases for smart matches. And in the same way all online dating businesses are underpinned by personalised recommendations, with the most successful using graph database technology, graphs help in identifying the relationships between very large numbers of data points, and so help users work with data better.
Three relationships deep and real-time relevance
A secret to why graph technology is so good at modeling connections is that it doesn’t have to live with the semantically limited data model and expensive, unpredictable joins you get in the SQL/relational world. That’s because graph technology supports multiple explicit named, directed relationships between entities (‘nodes’) which give you a rich semantic context for the data. Developers can incorporate new data sources, using the most recent transaction data and interoperate with existing transactional systems; relational databases can’t flex in this manner.
Putting this altogether, you can understand the customer’s past purchases, quickly query the data, and match the customer to the bigger pattern that is their closest match in your database, both in their social network and in buying patterns, plus instantly capture any new interests shown in the customer’s current visit. Even better, your recommendations come together in real-time or near real-time, since there is no join penalty; graphs regularly traverse more than three levels deep of relationship while delivering real-time performance.
Falling in love is complex
All this makes graph databases especially suited to formulating recommendations – and it’s why they have the potential to transform all kinds of businesses like the online dating world.
Effective product recommendation algorithms have become the new standard in online retail — directly affecting revenue streams and the shopping experience. In parallel, routing recommendations allow companies to save money on routing and delivery, and provide better and faster service. Even soaraway leader Amazon, which taught us the value of personalised recommendations, may need to do more if rivals offer ever-better recommendations through graph-powered data analysis.
Companies are starting to find more ways to use the power of graph technology. It may seem odd to think of your business as being like a dating agency. But if you start to see graphs as a way to help customers fall more deeply in ‘love’ with you and your products and services, you’ll reap rewards.
Online UK-based art marketplace Artfinder’s CEO, for instance, says, "Falling in love with art is complex, and good art captures and attracts the viewer on many levels, not just the first visual impression – just like dating.”
So, what if you want to try graph technology? While the consumer Web pioneers built their own in-house graph data stores, off-the-shelf graph databases are available to any business. That means anyone wanting to use real-time recommendations to influence and get closer to their customers can do just that.
The author heads up Neo Technology, the company behind the world’s leading graph database Neo4j (http://neo4j.com/)