How do you build the software that helps a client analyze BILLIONS of data points to effectively find useful meaning in their big data?
Big businesses collect billions of customer data points — including purchases, locations, page views, and phone calls. Most of this data isn't very useful on its own; and most businesses don’t have the ability to easily combine such diverse data, let alone make sense of it in realtime. That's why Intent HQ uses it to build profiles that help clients understand customers as unique individuals, including their interests, personality types, and emotional drivers.
At Agilogy we are helping Intent HQ develop the software needed to achieve their goals. We contribute to two key features of their product: the Topic Graph and the Insights Dashboard. The Topic Graph comprehends a series of artefacts that represent the human knowledge and helps understand the affinities between topics and how people relate and interact with them.
Generating the Topic Graph is a process that involves obtaining and parsing a vast amount of data from different sources, Wikipedia among others. After the collection of the data, it is then analyzed and processed to extract human interests and establish the relations between them. The data is then polished to more accurately reflect a human-like understanding of each end-customer.
The Insights Dashboard is a web application that provides Intent HQ's clients with an easy way to access their customers' data and analytics in real time. This tool can help companies effortlessly answer questions like "What are my female customers who live in New York interested in?" or "How do my lowest and highest value customers compare in terms of age?
The Insights Dashboard has a very unique but simple interface connected to a powerful backend programmed in Scala that is in charge of all the complex algorithms and computations that have to be performed on the data in order to provide reliable insight and statistics. In essence, we help Intent HQ to deliver technology that makes vast amounts of consumer data points understandable, meaningful and actionable to their users of the system. Not to mention more user friendly and in realtime. This is easier than it sounds when you’re dealing with Big Data.