> For the complete documentation index, see [llms.txt](https://docs.sentiance.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.sentiance.com/getting-started/features-catalog/lifestyle-insights.md).

# Lifestyle Insights

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This feature is currently in **Early Access** and is still under active development
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## Introduction to Lifestyle Insights

<div><figure><img src="/files/7JegvcKINJvLJGergCfx" alt=""><figcaption></figcaption></figure> <figure><img src="/files/zO3j68j4IB8PhyDcLPp5" alt=""><figcaption></figcaption></figure> <figure><img src="/files/iQilRwSbCDuOu7oD4ict" alt=""><figcaption></figcaption></figure></div>

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

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### 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:

<details>

<summary><strong>Profiles</strong></summary>

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

<table><thead><tr><th width="165.098876953125">Segment</th><th>Description</th></tr></thead><tbody><tr><td>Physical Activity</td><td>evaluates how much the user is involved in physical activities</td></tr><tr><td>Mobility</td><td>evaluates how much the user is on the move</td></tr><tr><td>Social Activity</td><td>evaluates how much the user is involved in social engagement</td></tr></tbody></table>

</details>

<details>

<summary><strong>Leisure</strong></summary>

<table><thead><tr><th width="213.171875">Segment</th><th>Description</th></tr></thead><tbody><tr><td>Bar goer</td><td>enjoys evenings out at a pub or bar</td></tr><tr><td>Fresh food enthousiast</td><td>shops for food often</td></tr><tr><td>Healthy biker</td><td>frequently bikes for long distances</td></tr><tr><td>Healthy walker</td><td>frequently walks for long distances</td></tr><tr><td>Nature lover</td><td>likes to go out to a park, public garden, zoo or nature reserve</td></tr><tr><td>Resto lover</td><td>likes eating out</td></tr><tr><td>Shopaholic</td><td>shops a lot</td></tr><tr><td>Sportive</td><td>sports regularly</td></tr></tbody></table>

</details>

<details>

<summary><strong>Mobility</strong></summary>

<table><thead><tr><th width="253.2196044921875">Segment</th><th>Description</th></tr></thead><tbody><tr><td>Die hard driver</td><td>uses the car for almost every trip</td></tr><tr><td>Easy commuter</td><td>has an easy commute to/from work</td></tr><tr><td>Frequent flyer</td><td>frequently flies</td></tr><tr><td>Green commuter</td><td>mostly sticks to walking and biking for commutes</td></tr><tr><td>Heavy commuter</td><td>has a heavy commute to/from work</td></tr><tr><td>Long commuter</td><td>lives far from their work location</td></tr><tr><td>Normal commuter</td><td>has an average commute time and distance</td></tr><tr><td>Public transports user</td><td>often travels with public transports</td></tr><tr><td>Public transports commuter</td><td>often commutes with public transports</td></tr><tr><td>Short commuter</td><td>lives close to their work location</td></tr></tbody></table>

</details>

<details>

<summary><strong>Work life</strong></summary>

<table><thead><tr><th width="189.421875">Segment</th><th>Description</th></tr></thead><tbody><tr><td>Early bird</td><td>whose first morning activity is earlier than average</td></tr><tr><td>Fulltime worker</td><td>works full-time</td></tr><tr><td>Home bound</td><td>does not leave the house very often or travels very far</td></tr><tr><td>Homebody</td><td>prefers to stay at home on weekends and outside business hours</td></tr><tr><td>Homeworker</td><td>works from home or who is unemployed</td></tr><tr><td>Late worker</td><td>works until late</td></tr><tr><td>Night owl</td><td>whose last evening activity is later than average</td></tr><tr><td>Nightworker</td><td>works at night</td></tr><tr><td>Parttime worker</td><td>works part-time</td></tr><tr><td>Sleep deprived</td><td>sleeps very little</td></tr><tr><td>Student or teacher</td><td>a student or teacher</td></tr><tr><td>Uber parent</td><td>a parent who drives his/her kids to school, kindergarten or day care</td></tr><tr><td>Work life balancce</td><td>has a good balance between work and home life</td></tr><tr><td>Work traveller</td><td>works a lot remotely (travelling or in remote environments)</td></tr><tr><td>Workaholic</td><td>works more than average</td></tr></tbody></table>

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### 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.

<details>

<summary>Venues</summary>

<table><thead><tr><th width="239.1934814453125"></th><th></th></tr></thead><tbody><tr><td>UNKNOWN</td><td>Unknown venue</td></tr><tr><td>DRINK_DAY</td><td>Cafes, coffee bars, tea rooms, etc</td></tr><tr><td>DRINK_EVENING</td><td>Bars, pubs and in general places where one goes for drinks in evenings.</td></tr><tr><td>EDUCATION_INDEPENDENT</td><td>Educational institutions visited by the user on his own for their own studies. High schools, universities, colleges, etc.</td></tr><tr><td>EDUCATION_PARENTS</td><td>Schools and kindergartens visited by parents. </td></tr><tr><td>HEALTH</td><td>Hospitals, clinics, emergency rooms.</td></tr><tr><td>INDUSTRIAL</td><td>Buildings tagged as “industrial” on OSM, built for some manufacturing process.</td></tr><tr><td>LEISURE_BEACH</td><td>Beaches, resorts and swimming areas.</td></tr><tr><td>LEISURE_DAY</td><td>Bowling, billiards and other entertainment places. </td></tr><tr><td>LEISURE_EVENING</td><td>Cinemas, theatres and music halls. </td></tr><tr><td>LEISURE_MUSEUM</td><td>Museums</td></tr><tr><td>LEISURE_NATURE</td><td>Forests, lakes, national parks, etc. </td></tr><tr><td>LEISURE_PARK</td><td>City parks, gardens, zoos.</td></tr><tr><td>OFFICE</td><td>Office buildings. For example, of private lawyers, notaries or company representatives.</td></tr><tr><td>RELIGION</td><td>Churches, mosques and other religion related buildings.</td></tr><tr><td>RESIDENTIAL</td><td>Apartment blocks, houses. </td></tr><tr><td>RESTO_MID</td><td>Food courts, restaurants, snack bars. </td></tr><tr><td>RESTO_SHORT</td><td>Ice cream, fast food, donut stores.</td></tr><tr><td>SHOP_LONG</td><td>Supermarkets, malls, wholesales, shopping centres.</td></tr><tr><td>SHOP_SHORT</td><td>Small grocery stores, butchers, bakers. </td></tr><tr><td>SPORT</td><td>Gyms, sport centres. Venues visited to exercise.</td></tr><tr><td>SPORT_ATTEND</td><td>Stadiums. Venues visited to attend a sport event.</td></tr><tr><td>TRAVEL_BUS</td><td>Bus stops.</td></tr><tr><td>TRAVEL_CONFERENCE</td><td>Conference, convention, exhibition centres.</td></tr><tr><td>TRAVEL_FILL</td><td>Gas stations.</td></tr><tr><td>TRAVEL_HOTEL</td><td>travel_hotel- Hotels, motels, guest rooms, etc.</td></tr><tr><td>TRAVEL_LONG</td><td>Airports</td></tr><tr><td>TRAVEL_SHORT</td><td>Public transport stations, railway stations.</td></tr></tbody></table>

</details>

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### 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.

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### 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.
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**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.
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