Data is driving businesses forward. It is giving business leaders the information they need for informed decision making and providing an invaluable look at a number of critical areas, like the efficacy of businesses processes and potential pain points or problem areas. Critically, data can also inform organisations about consumer behaviour—something that you can then use to craft initiatives focused on improving customer interaction and satisfaction.
However, data on its own has relatively little value. The true value of data hinges on you being able to gain insights from it instead of merely collecting it. But to gain valuable, actionable insights, you will need to connect data points from voluminous data sets, spot these connections, figure out the relationships of these connections and then make sense of it all. Then, after connecting the dots so to speak, you will be able to gain a 360-degree view of your customers; provide relevant data-driven recommendations; build a complete network topology and ensure visibility that can prevent fraud.
In other words, connections unlock the value of data.
Unfortunately, this connecting of the dots is something legacy Relational Database Management Systems (RDBMS) cannot do efficiently and effectively—if at all. Part of the reason RDBMS are unable to handle these relationships is due to their tabular data models and rigid schemas, both of which make it extremely hard to add new or different kinds of connections. This shortcoming, in turn, will keep you from maximising the value of your data even if you have collected troves of it. And in this day and age of data, the inability to glean invaluable insights from it will set your business back, left behind by organisations that are able to get the most value out of their data.
The solve this dilemma, you need a database technology that can store relationship information as a first-class entity. In short, you need a graph database. This type of technology is the future, mainly because it enables a connections-first approach in which data relationships and connections are persisted throughout the data lifecycle. It starts right from the idea, down to designing in a logical model, all the way to physical model implementation and operation using a query language and finally ending in persistence within a scalable, reliable database system.
But how, exactly, does using graphs enhance customer interaction and satisfaction?
For starters, using graphs enables you to have a 360-view of your data, and this allows you to see connections on top of connections on top of connections, along with relationships you might have otherwise not seen using RDBMS. So, using traditional systems, you’ll probably see that one of your customers bought a t-shirt last December. Using graphs, however, you’ll know that said customer bought a blue, V-neck t-shirt every December in the past four years—with each a size larger than the previous one.
Now, imagine having that same level of detail for every customer. Among other things, you can turn every interaction intimate and fruitful, with your in-depth knowledge about the customer’s buying behaviour both fostering a sense of familiarity and enabling you to make relevant recommendations based on their purchase history and product queries. And in this era when consumers are becoming more and more discerning and demanding, being able to offer them exactly what they are looking for has become a key differentiator. More often than not is also the difference between winning over customers and keeping them or turning them off and losing them.
A case in point is eBay, the e-commerce giant that a few years back was looking for ways to help shoppers find exactly what they need. Specifically, eBay executives wanted their product searches to be able to infer contextual information so that each search yields truly relevant results that will lead shoppers a step closer to buying what they want.
To do so, eBay turned to Neo4j’s technology to serve as the company’s native graph database, holding the probabilistic models that will help enhance understanding in conversational shopping scenarios using the eBay App for Google Assistant. The Neo4j graph also contains both the product catalogue of eBay and shopper interaction data as customers did their product searches. This leads to an intuitive search experience where the app, thanks to Neo4j’s graphs, is able to anticipate what customers are likely to ask about next after an initial search and then check inventory to give the best matches.
Such are the kind of interactions that today’s demanding consumers are craving for—productive and to the point, easy and intuitive. Data can help make all this possible, as long as graph technology is used to make sense of it.