Life Stage Data: Best Life Stage Datasets & Databases
Life stage data is intelligence on an individual’s age and career status,
which is a branch of psychographic data. It’s used by marketers for market
analytics and demographic targeting. Datarade helps you find the best life
stage datasets, providers and APIs.
Datonics Web Browsing Data for USA & Canada (Interest, Intent, Life Stage, Behavioral Audiences)
Individuals looking for life Insurance
Clean & Verified Platform & API B2B Contact Data, Lead Data, Business Contact Data, Contact Email Data, Business Email Data, Phone & Direct Dial Data.
Acxiom Audience Data Enhancement - Validate & Enhance Customer Data (Germany Covered)
KIA Biz's customised B2B data GDPR compliant European Data
220+ Countries Global Trade Data, Customs Data, Bill of Lading Data, Import and Export Shipments Records
datazeit Walmart Retail, Brand & Product Data / USA / Live data coverage
B2B Contact Data: C-Level, Executives and Professionals Contact Data & Email List
B2B Email Data - Reach 100M+ Executives Globally With Our B2B Email Marketing Data
TRAK Data - United States Political Data - Republicans, Democrats, Independents, Donors, Congressional Districts & More
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.