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 customer transaction data that organizations can collect without getting explicit approval from individuals. However, when companies request approval from site or app users to gather their customer transaction 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 (customer transaction) data provides demographic, geospatial, behavioral, interest, intent, and transactional information that people in all kinds of industries use. Businesses use customer transaction data 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.
X-Byte’s dataset – ‘X-Byte Social Media Data | Social Media Analytics | Scrape Data from Social Media Websites’ provides Social Data, Individual Data and Social Media Sentiment Data that can be used in Predictive Maintenance, Behavioral Targeting, Trend Forecasting, and Online and Social Media Performance Tracking
ViralMoment’s dataset – ‘ViralMoment’s Reddit Stock Ticker Mention and Sentiment’ provides , Social Data, Stock & Market Data and Individual Data that can be used in Trend Forecasting and
ViralMoment’s dataset – ‘ViralMoment’s Reddit Cryptocurrency Influencer Activity Tracking’ provides , Social Data, Stock & Market Data and Individual Data that can be used in and Trend Forecasting