The CEO of DigitalMR, Michael Michalis describes how AI-based unstructured data analysis can make marketing strategies more nuanced; Data is fundamental to business decisions. A company’s ability to gather the right data, interpret it, and act on those insights is typically what’s getting to determine its level of success. But the quantity of knowledge accessible to companies is ever increasing, as are the various sorts of data available. Business data comes during an expansive sort of formats, from strictly formed relational databases to the last tweet. All of the entire data, all together in its different formats, are often categorized into two main categories: structured data and unstructured data.
The term structured data refers to data that resides during a fixed field within a file or record. Structured data is usually stored during an electronic database (RDBMS). It can contain numbers and text, and sourcing can happen automatically or manually, as long as it’s within an RDBMS structure. It depends on the creation of a knowledge model, defining what sorts of data to incorporate, and the way to store and process it.
Unstructured data is more or less all the info that’s not structured. Even though unstructured data might have an internal structure, which is not structured in a predefined manner. There is a small data model; the data is stored in its native format.
Few examples of unstructured data are rich media, surveillance imagery, text, social media activity, and so on. The amount of unstructured data is far larger than that of structured data.
Structured data, as indicated by numbers in tables or closed-ended survey questions, are unable to provide the marketing departments of companies with the nuances and subtlety offered by unstructured data. This subtlety is formed possible by both the sheer quantity of UD and therefore the sort of forms it can take, starting from terabytes of text and pictures on social media platforms to audio and video information. This quantity of UD is constant to grow. In fact, IDG predicts that 93% of digital data is going to be unstructured by 2022, so understanding the way to exploit it’ll be a competitive differentiator for any business.
It is everywhere and comprises the overwhelming majority of all data. Firms and corporations have access thereto in great quantities – but it’s often underutilized. In the world of business data, UD has usually mentioned as “dark” information thanks to its raw, hidden, and undigested qualities. But such data are of frequent use to marketers and performing what has been called “dark analytics”
Advances in AI mean that marketing and sales professionals now have the means to sharpen their strategies by paying attention to the voice of their customers (VoC) and leads.
By putting forth machine learning techniques and AI tools, large quantities of unstructured data are often transformed into fodder for improved marketing.
VoC can combat a selection of varied forms, including reviews, feedback on products, calls made to customer services, and their expressions of positive, negative, and neutral feelings towards competitor brands. Ever-increasing advances within the field mean that AI is capable of using UD to supply an increasingly sophisticated degree of sentiment analysis.
Unstructured Data also can tell marketers how all feel a few numbers of topics. This kind of study, achieved through social listening tools and fuelled by the increase of unstructured data, ultimately provides a much more sophisticated understanding of a given market, than the normal and outdated demographic segmentation practices upon which marketers were once reliant.
It is absolutely possible to plug into the distinct demographics as long as a corporation is prepared to leap into the “dark analytics” of UD. With AI lighting up the darkness and social listening tools able to detect the gold nuggets of customer feedback, marketers might just find – glinting within the unexplored trove of UD – that there’s treasure shining within the shadows, waiting to be claimed.