A limitative list of segments can be found below. Not all segments are calculated yet or require further improvements. A more exhaustive list will be prioritized and further updated towards the future.
Socio-demographic: language, income estimate (social class), age estimate, family (has kids), is pregnant, urban/suburban/rural, …
Location: region, city, district for both home, work and current location; previously visited or nearby specific (set of) POI(s)
Category behaviour: users and addicts for 20+ categories (supermarkets, automotive, DIY, beauty, fashion, books, toys, kitchen, living, shoes, sports, underwear, electronics, (fast)food, restaurants, tourism, jobs, experiences …), loyal/mixer/switcher behaviour based on frequency. Given additional POIs are added to the database, extra segments are continuously added.
Shopping behaviour: convenience buyer, mall / high street / periferic or retail parc preference, premium vs discount shopper, local shopper, ...
Brand loyalty: loyalty levels for 600+ retail brands (occasional - loyal - churning - …)
Leisure: culturist, dog walker, sportive, runner, cyclist, museum visitor, festival goer, movie goer, holiday preference, winter sporter, …
Commuting behaviour: usage levels for car, bike, public transport, length of commutes, traffic jammer, ...
Professional: white/blue collar worker, fixed vs flexible, 4/5th, business traveller, changed jobs, looking for a job, …
Intent: buying a car, buying a kitchen, buying a bathroom, just moved, …
Behaviour based: at home, at work, in free time, going home, looking for lunch, heading to the gym, in daily commute, …
Technology: device information like browser (iOS, Android), phone brand...
App activity: registered, time spent in-app, notification received / opened, …
On top of all pre-calculated segments, we offer a segment builder in which interactions and segments can be combined to create new and unique segments.
