At first glance the world of online dating seems like it would have little correlation with that of digital marketing – but dig a little deeper and you will see that there is much the latter can learn.
Online dating has turned traditional romance on its head. And it is a major success story. Online dating is estimated to be a £225 million business by 2019 in the UK alone, according to Minitel.
Match.com, eHarmony, Tinder, Zoosk to name but a few have one thing in common. They all use technology to make sophisticated recommendations to help members find the best matches. Just like B2B marketers they are looking to select the highest prospects.
Online dating sites excel at what they do because they are highly skilled at manipulating large sets of connected data so they can bring like-minded individuals together, at scale. Digital marketers can use the same technology to make data work harder for them – data mining to find target consumers who are in a specific market and in search of a particular product or service.
The key to this is recommendations. All online dating businesses are founded on personalised recommendations, with the most accurate and successful using graph database technology to manage those algorithms. Graph databases differ from traditional (relational) business databases in that they specialise in identifying the relationships between very large numbers of data points, and so enabling users to work with large amounts of data far more efficiently and productively.
Standing out from the crowd
Graph databases are a core technology platform used by Internet giants like Amazon and Netflix. Amazon’s success is partly built on its ability to rapidly exploit connections between people and products, and flash up “Other people also bought” recommendations. Netflix digitally connects people and content together in such a smart way that it has carved itself a slice of the broadcasting media market.
The reason why graphs are at the very centre of these social web giants’ business strategy is that graph databases give equal prominence to storing the data (customers, products) and the relationships between them such as who purchased what, when, who likes what or whom etc. In a graph database, marketing does not have to depend on the semantically limited data model and expensive, unpredictable joins thrown out in the SQL/relational field. By contrast, graph databases support many named, directed relationships between entities or nodes that give the user a rich semantic context for the data. Developers can also incorporate new data sources, use the most recent transactional data (capturing any new interests shown in the customer’s current visit) and interoperate with existing transactional systems. Relational databases inherently do not have this flexibility.
Pull this together and marketing knows a lot more about customers, their preferences and buying habits. Another huge bonus is that queries are in real-time or near real-time, since there is no join penalty; graphs regularly cross more than three levels deep of relationship while delivering real-time performance.
Predicting made easy
These attributes make graph databases especially suited to formulating recommendations, and it’s why they have the potential to transform all kinds of businesses. Effective product recommendation algorithms have become the new standard in online retail, for example — directly affecting revenue streams and the shopping experience. At the same time, recommendations allow companies to save money on routing and delivery, and provides and more efficient and productive faster service.
Technology is being used in ever more creative ways to gain a competitive edge. Even the likes of Amazon and Netflix need to stay ahead of the game as more and more companies adopt personalised recommendations via multi-layered, graph-powered data analysis.
Businesses are breaking ground bringing smart database technology to play in their digital marketing strategies. As more marketing specialists realise the power of graph technology this will start to become the norm, as opposed to the exception, in understanding the customer and directing relevant messages at them to ensure they hit the sweetspot.
By Emil Eifrem, CEO of graph leader Neo Technology
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