Individual data is information about individual customers or site users. This includes their transactions and behavior (online and offline) across all their devices and platforms. In short, this data is the sum of consumer transaction, identity, marketing attribution, and online/mobile data.
Most individual data comes from online sources. Companies and third-party data vendors collect information from email or SMS registrations, app use, ad exposure data, mobile use and location data of “smart” devices, and more.
Other sources of this data include reports from company CSR or sales teams, loyalty program users, and historical marketing campaign data.
New laws and regulations have reduced the amount of data that organizations can collect without getting explicit approval from individuals. However, when companies request approval from site or app users to gather their data, they increase the likelihood that people will provide their information.
Common attributes of individual data fall into two main categories: explicit and implicit. Also referred to as active data, explicit data is anything that you have to specifically request that a person give you. In other words, their name, phone number, email address, and similar information.
Implicit—aka passive—data, on the other hand, is any information that can be gathered without asking. Examples include time spent on an app, locations where the customer has visited or spent time in, amount of time spent exercising outdoors or socializing.
This individual data provides demographic, geospatial, behavioral, interest, intent, and transactional information that people in all kinds of industries use. Businesses use the information to market to customers, forecast price changes, improve brand affiliation, track social media performance, and improve customer satisfaction. Businesses also use this information to detect fraudulent transactions to keep them and their customers safe.
There are sometimes surprising uses of individual data. Dartmouth College, for example, reports that passive mobile data indicate clinically significant anxiety levels, suggesting brain connectivity problems that can then be shown with MRI images.
The best test of the quality of individual data is the update frequency and consistency of data within the dataset. This data category is one of the fastest-moving fields in the universe of big data, with updates appearing daily—at least.
It is also good to collect a set of historical data and test that against newer data or against the rate of conversions.
Aside from update frequency, the most important factors to consider when selecting an identity data set is the experience and methodology of the vendor. Does the vendor use a mix of offline and online data? Do they rely more on probabilistic or deterministic data? What is their match rate percentage? Do they have historical data to compare to past data?
Heathrow welcomes 206,800 visitors daily. Each person has different needs and little time to spend shopping or looking for a lounge that will suit them. Each business within the airport also has its own goals and challenges. However, with individual data—especially mobile data—Heathrow is able to market to each one of its 206,800 harried visitors effectively.
The Logz Data Platform enables any data engineer to manage any project, no matter the subject or scale. Their data management tools include cloud security, compliance, log tracing, and even developer analytics and visualizations.
LinkedIn Company Data includes not only data about for-profit and non-profit companies of all sizes, but also the people who work in them, from CEOs to interns. LinkedIn also tracks data on certifications, credentials, and events; they even offer professional training and host events themselves.
In all, this information improves social and professional networking while providing opportunities for employee recruitment and B2B sales.
Catalist Data Visualization and Reporting provides a variety of historical data dashboards and custom data research projects