Life Stage Data
Top Life Stage Data APIs, Datasets, and Databases
Find the top commercial Life Stage Data sets, feeds and streams.
DDS Data Grid
France+ 48 others
|Use Case||Location Intelligence, Location Analytics + 3 more|
|Volume||350 buyer personas, 150M mobile device IDs|
|Quality||100% deterministic data used|
|History||3 years of past data available|
|Use Case||Consumer Intelligence, Consumer Data Enrichment + 3 more|
pH Segmentation Data
|Volume||242 Million US Adults|
|History||1 years of past data available|
|Use Case||Audience Segmentation, Marketing + 3 more|
Corporate Business (SoS) Data
|Volume||77.2M ADC Universe Count, 408K ADC Avg. Monthly Hotline|
|History||267 years of past data available|
|Use Case||Portfolio Analysis, Market Research + 3 more|
Online Keyword Raw Data Feed For AI Platforms
|Use Case||Algorithm, Programmatic Advertising + 3 more|
Top Life Stage Data Providers, Vendors, and Companies
Find the top Life Stage Data aggregators, suppliers, and firms.
Please create a free account if you want to get access to all of our resources.
The Ultimate Guide to Life Stage Data 2020
Learn everything about Life Stage Data. Understand data sources, popular use cases, and data quality.
Table of Contents
What is Life Stage Data?
Life stage data is a type of psychographic data. Psychographic data refers to data about an individual’s tastes, attitudes, interests, values that make up a personality. Life stage data deals with intelligence on all of these personality-defining factors with regards to how an individual’s age bracket or group shapes them. Life stage data is essential in market segmentation and in understanding just what contributes to a person’s preferences based on what stage of life they’re in.
How is Life Stage Data collected?
Life stage data is collected just as most consumer based data is collated. Collection of data can be indirect or direct. Direct collection methods involve the consumers giving out information on themselves through surveys, questionnaires, interviews or focus groups. Participants actively supply this information. Indirect data collection involves census monitoring or web scraping methods to pick up on an individual’s data. Indirect collection is usually a more scalable source of life stage data, although it’s sometimes less acurate than direct methods.
What are the attributes of Life Stage Data?
Life stage data should possess the following attributes to be considered a valid means of reliable information. To properly help businesses retain customers, gain new ones or upsell across segments, a life stage dataset should include:
Taste of age group: a typical life stage dataset includes information on the preferences and brand affinity of an age group and understanding what makes them tick. The taste and brand loyalty of that age group is used for targeted marketing essential, as it determines what exactly they like at the moment, and why.
Interests: every age group has generic interests. A life stage dataset should explain what an age group likes to do or where they like to go.
Demographics: factors like ethnicity, income level, gender, and location also matter in the creation of a life stage data-based buyer persona.
What is Life Stage Data used for?
One of the key goals as a marketer is to find out just what motivates consumers to taking the actions they do. This is closely linked to what kind of personality they have, and their age. Life stage data helps to determine or project these personalities based on a market segment’s age.
Additionally, consumer needs are changing all the time. Life stage data helps to gain insight and foresight on individuals as they grow, in order to help create products that they will prefer. It aids the innovation process of production, so that there are no wasted resources.
How can a user assess the quality of Life Stage Data?
The assessment on quality of a life stage dataset rests on a number of factors. A life stage dataset needs to possess:
Timeliness: because life stages only last for a couple of years, and social trends may change along with time, life stage data must be up to date. That way it won’t provide outdated intelligence.
Accuracy: because decisions will ultimately be made from a life stage dataset, the data must be accurate. It is important because any statistical error could mean a misinterpretation of a life stage’s interests and needs.
Reliability: life stage data must come with the guarantee that it can be relied on, which is linked to the customer service a data provider offers.
Popular Life Stage Data Use Cases
Find out the most common applications of Life Stage Data.