Lifestyle Insights

Learn real-world behaviors, habits, and lifestyles to power hyper-personalized experiences.

This feature is currently in Early Access and is still under active development

Introduction to Lifestyle Insights

With Lifestyle insights you will gain a deeper understanding of your users by identifying the places they frequently visit such as home, work, and favorite venue types. These insights reveal daily routines, habits, and preferences.

We also provide Semantic Time, which categorizes activities based on a user’s personal timeline (e.g., morning, afternoon, evening). This helps you understand not just where users go, but when and how they structure their day.

Together, these insights enable personalized and relevant user engagement. The insights can be grouped in the topics below

Features Overview

1

Lifestyle Profiling

Lifestyle Profiling segments users into different lifestyle profiles. Segments are actionable labels assigned to users based on long-term behavioral patterns.

They describe lifestyle characteristics such as:

• Commuting behavior

• Geographic living area

• Driving style

• Social activity level

These segments help you tailor experiences and build more relevant and better user experience. The segments can be divided into these sub-categories:

Profiles

Profiles are a special type of Segment that apply to every user. Unlike regular segments (which a user may or may not belong to), every user always has a Profile. Each Profile has a level that indicates the degree to which it applies to that user:

• LIMITED

• MODERATE

• HIGH

Segment	Description
Physical Activity	evaluates how much the user is involved in physical activities
Mobility	evaluates how much the user is on the move
Social Activity	evaluates how much the user is involved in social engagement

Leisure

Segment	Description
Bar goer	enjoys evenings out at a pub or bar
Fresh food enthousiast	shops for food often
Healthy biker	frequently bikes for long distances
Healthy walker	frequently walks for long distances
Nature lover	likes to go out to a park, public garden, zoo or nature reserve
Resto lover	likes eating out
Shopaholic	shops a lot
Sportive	sports regularly

Mobility

Segment	Description
Die hard driver	uses the car for almost every trip
Easy commuter	has an easy commute to/from work
Frequent flyer	frequently flies
Green commuter	mostly sticks to walking and biking for commutes
Heavy commuter	has a heavy commute to/from work
Long commuter	lives far from their work location
Normal commuter	has an average commute time and distance
Public transports user	often travels with public transports
Public transports commuter	often commutes with public transports
Short commuter	lives close to their work location

Work life

Segment	Description
Early bird	whose first morning activity is earlier than average
Fulltime worker	works full-time
Home bound	does not leave the house very often or travels very far
Homebody	prefers to stay at home on weekends and outside business hours
Homeworker	works from home or who is unemployed
Late worker	works until late
Night owl	whose last evening activity is later than average
Nightworker	works at night
Parttime worker	works part-time
Sleep deprived	sleeps very little
Student or teacher	a student or teacher
Uber parent	a parent who drives his/her kids to school, kindergarten or day care
Work life balancce	has a good balance between work and home life
Work traveller	works a lot remotely (travelling or in remote environments)
Workaholic	works more than average

2

Venue Type Detection

Detects the type of place that the user has visited (e.g., drinks, sports, education, shopping) based on location context, time of day and users mobility behavior. Below is a list of the venue types that can be detected.

Venues

UNKNOWN	Unknown venue
DRINK_DAY	Cafes, coffee bars, tea rooms, etc
DRINK_EVENING	Bars, pubs and in general places where one goes for drinks in evenings.
EDUCATION_INDEPENDENT	Educational institutions visited by the user on his own for their own studies. High schools, universities, colleges, etc.
EDUCATION_PARENTS	Schools and kindergartens visited by parents.
HEALTH	Hospitals, clinics, emergency rooms.
INDUSTRIAL	Buildings tagged as “industrial” on OSM, built for some manufacturing process.
LEISURE_BEACH	Beaches, resorts and swimming areas.
LEISURE_DAY	Bowling, billiards and other entertainment places.
LEISURE_EVENING	Cinemas, theatres and music halls.
LEISURE_MUSEUM	Museums
LEISURE_NATURE	Forests, lakes, national parks, etc.
LEISURE_PARK	City parks, gardens, zoos.
OFFICE	Office buildings. For example, of private lawyers, notaries or company representatives.
RELIGION	Churches, mosques and other religion related buildings.
RESIDENTIAL	Apartment blocks, houses.
RESTO_MID	Food courts, restaurants, snack bars.
RESTO_SHORT	Ice cream, fast food, donut stores.
SHOP_LONG	Supermarkets, malls, wholesales, shopping centres.
SHOP_SHORT	Small grocery stores, butchers, bakers.
SPORT	Gyms, sport centres. Venues visited to exercise.
SPORT_ATTEND	Stadiums. Venues visited to attend a sport event.
TRAVEL_BUS	Bus stops.
TRAVEL_CONFERENCE	Conference, convention, exhibition centres.
TRAVEL_FILL	Gas stations.
TRAVEL_HOTEL	travel_hotel- Hotels, motels, guest rooms, etc.
TRAVEL_LONG	Airports
TRAVEL_SHORT	Public transport stations, railway stations.

3

Home & Work Detection

Automatically identifies home and work locations based on the user’s behavioral patterns. These locations are typically detected during the first week of using Sentiance.

The detected location will seamlessly addapt when a user moves from the original home or changes jobs

This includdes the detection of when the user enters and exists these locations.

4

Physical Activity

Physical Activity segments users into activity levels based on detected behaviors such as walking, cycling, and gym visits.

Users are categorized into one of the following activity levels:

• Low

• Moderate

• High

These levels represent the user’s overall physical activity based on their detected behavior patterns.

Privacy by Design

We do not expose exact locations. Instead, we provide venue types to preserve user privacy. This approach is often more accurate for example, confidently identifying that a user is at a bar without pinpointing which specific one.