The surprising lessons retailers can learn from online dating sites
Dating websites exploit data relationships to find great matches and the same approach can help retailers make a love connection with their customers.
Online dating is a huge success story. Estimated to be a $2 billion global business, about one in every ten Americans is using an online site or mobile dating app to look for love today.
But have you ever wondered what’s taking place to power this industry? Online dating sites excel at what they do because they are very good at manipulating large sets of connected data. These algorithms bring similar-minded individuals together, at scale.
And their sheer ubiquity and convenience prove that something is working, and that other industries can and should be doing the same thing: making data work harder for you and your customers, so that you too can offer a compelling offer to “swipe right” on.
The key is data. In large volumes, yes – but also data parsed, shaped and manipulated correctly. All online dating businesses are underpinned by data, with the most accurate and successful using a special approach to data called graph database technology to manage that related data.
Graph databases differ from traditional (relational, SQL) business databases in that they specialize in managing the relationships between large numbers of data points, not just the records themselves – and so help you leverage all that data more effectively.
Significantly, graph databases are a core technology platform used by the first wave of Internet giants like Google, Facebook and LinkedIn to disrupt their markets, as they promoted such facility with big Internet-scale datasets. LinkedIn digitally harnesses real-life relationship networks in such an efficient manner that it dominates the business social network market, for example – all thanks to graphs.
Not just relational, but relationships
The reason why a graph database is the secret heart of these social web giants’ successes is that graph database systems give equal prominence to storing both the data (customers, products) and the relationships between them (who bought what, who likes whom, which purchase happened first).
In a graph database, then, we don’t have to live with the semantically-limited data model and expensive, unpredictable joins from the SQL/relational world. Graph databases, by contrast, can support many named, directed relationships between entities or nodes that give a rich semantic context for the data. Now, you can learn a lot more about a customer if you are a supplier. Even better, queries are super-fast, since there is no join run-time “penalty.”
This makes graph databases especially suited to formulating recommendations, and why they have the potential to transform any business; in the same way they have in the online dating world, and indeed in the consumer Web. What both share is the enviable capability of matching prospective customers with the products or services most likely to appeal to them, in ever more tailored and immediate ways. And that’s what you need to be doing too.
The reason why is simple: In today’s business environment, only the strong survive. To make it in our increasingly digital, globalized world, uncovering the best recommendations (and in turn, maximizing value and the potential to up-sell) involves more than simply offering a new promotion. While promotions can be a successful part of a recommendation, today’s increasingly digital shoppers expect finely tuned personal recommendations designed just for them, as the one-size-fits-all approach becomes less and less attractive.
To step up here, brands need to look at the customer’s past purchases, quickly query this data, and relate the customer to the people that are the closest match to them, both in their social network and in buying patterns.
And to make suggestions as sharp and relevant as possible also requires the ability to instantly capture any new interests shown in the customer’s current visit. Again, graph databases are a great enabler in this respect, thanks to their ability to effortlessly match historical data with live session data.
Your always-on personal shopper
Many major retailers are already leveraging the power of the graph to drive sales. For example, Walmart, uses a graph database to combine information from customer purchases at physical and online stores in order to make real-time personalized recommendations.
Meanwhile, global sports and athletics giant adidas Group is using a graph database to offer enhanced features to website visitors, such as product recommendations.
Unlike other online retailers offering static content on their website, adidas wanted to personalize content based on user interests, local languages, regional sporting news and market-specific product offerings. As a result, its internal business users can categorize and search for user trend content across every platform and division of the enterprise. These include marketing campaigns, product specifications, contracted athletes and associated teams to sports categories, gender information and more.
Whether it’s directing a fan to a piece of their favorite team’s soccer merchandise, or making connections within a growing digital consumer dataset, graphs are enabling adidas to deliver the super-focused, hyper-relevant features to consumers we’ve been talking about.
Graphs are the analysts’ hot tip
You don’t have to be a big global brand like adidas Group to benefit from graph technology. Today, any business can take advantage of its digital connections in a way that only a company like Facebook could do five years ago. In fact, leading analyst group Forrester Research estimates that in a year’s time, one in four enterprises will use graph database technology . Similarly Gartner Research, reports graphs are the fastest-growing category in database management systems, predicting 70% of leading companies will pilot a graph database project of some significant kind by 2018.
The attractions of a technology that can provide a 360-degree view of a customer in real time are clear. Graph databases offer a new way of helping customers, and to connecting with them on a rich, personalized level.
Ultimately, graphs can also improve sales conversion rates and enhance customer loyalty, adding money to the bottom line and providing real competitive advantage.
And that’s got to be a genuine “love match,” surely?