Online dating is big business. No longer seen as a fringe activity, it’s now the main way single people meet due to its scale, reach and published success rates. In the UK alone, there are over 1,400 web-based matchmaking sites, for example, catering for people of all ages and situations.
The proposition is simple and effective; you get access to whole communities of single people who are all interested in the same thing as you. These software interfaces grant you vast volumes of potential dates to find your strongest matches and spy out potential new partners.
Before the advent of online dating, the usual way for someone to meet someone was by broadening their social circle, or taking up a new hobby to put one in the path of similar others.
That’s all Match.com, eHarmony and Zoosk are doing too – putting people’s interactions and connections at the centre of its algorithms to help you search for interests and attitudes and life experiences that you have in common with others.
The reason they are able to do this is because of a back-end technology called “graph databases”.
And it turns out that dating leaders aren’t the only Web pioneers succeeding through the technology, as it’s also at the heart of the breakthroughs of the eBays, Googles and LinkedIns of the digital world.
Graph databases differ from relational databases, the vast majority of everyday business databases, as they specialise in identifying relationships between multiple data points.
Google was able to exploit the connections in every web document to get back substantially better search results, a fundamental factor in its meteoric rise. LinkedIn, meanwhile, digitises real-life relationship networks, common business contacts and friends-of-friends in a way that has given it total domination of the business social market.
Relationships can be spotted and followed by systems using graph databases much more quickly than a relational database because those relationships (in the graph database world, “joins”) don’t need to be created in order to run a query.
This means improved query time, supporting speedier transactions. When done effectively, the graph database can query and display many, many connections between people, preferences, personal profile criteria, and so on.
Read more about customer loyalty:
- How to get and keep customers with gifts and rewards
- What can you learn from Waitrose’s “free hot drink” loyalty scheme
- Loyalty schemes can dramatically increase retailers’ basket value
How this applies to retail
The reason we are telling you this is that graph databases have the potential to transform the retail experience in the same way – allowing brands to match prospective customers with the products or services in ways most likely to appeal to them in ever more tailored, immediate ways.
How? By exploiting a graph database, retailers could deliver something even more personalised than Amazon-style “Customers who bought this, also bought this” recommendations, which are beginning to feel generic and dated.
After all, graph databases are no longer the province of the big players – in fact, the secret really is out, as these tools and techniques are now widely available in open source formats.
So while a Google or a LinkedIn had to build their own in-house data stores from scratch, off-the-shelf graph databases are now available to any business wanting to exploit real-time recommendations to get closer to their customers, online dating style.
Continue reading on page two…
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