Default Attributes

Lytics offers a wide range of pre-packaged user attributes, including automatically generated and customizable ones. Additionally, Lytics employs predictive modeling and machine learning algorithms to provide insights and scores, allowing users to gain a deeper understanding of their audience. The guide below provides an overview of all available attributes and examples to enhance your profiling efforts.

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If you haven't already reviewed our documentation on collecting events via our JavaScript SDK, we highly recommend doing so first. This will give you a better understanding of how data collection works at a high level before delving into the specifics of what can be collected.

Available Attributes

The following attributes are all available out of the box with no customization necessary in all Lytics pricing tiers. Do note that any attributes flagged as Computed can not be edited directly but are computed based on various factors, including other non-computed attributes.

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For a more comprehensive example of how any of the following attributes can be collected and used for your visitors click the name of the identifier to access the code examples below.

Identifiers

Default attributes that are used to stitch profiles together. For instance, if you pass an email along with the _uid, all events that have only been associated with either identifier will be merged into a single comprehensive profile.

NameSlugDescriptionComputedExample
Lytics ID_idA unique ID that represents the materialized profile in Lytics.Yes4fafb5b3-b199-58f2-a68b-4b266b363dd1
Current Lytics Cookie_uidThe current cookie id for the user.No50b772f5-a0be-42f2-8828-84b8db5d5a23
All Lytics Cookies_uidsAll cookies that are associated with the user.No["50b772f5-a0be-42f2-8828-84b8db5d5a23"]
EmailemailThe email address of the user.No[email protected]
Unique User IDuuidA UUID for the user.No4fafb5b3-b199-58f2-a68b-4b266b363dd1

Details

Details encompass all default attributes related to user demographics and general information, including name, phone number, status, etc. It serves as a catch-all for attributes not specifically tied to interactions or behaviors.

NameSlugDescriptionComputedExample
NamenameThe full name of the user.NoJohn Doe
First Namefirst_nameThe first name of the user.NoJohn
Last Namelast_nameThe last name of the user.NoDoe
TitletitleThe title of the user.NoPresident
PhonephoneThe phone number of the user.No555-555-5555
CellcellThe cell phone number of the user.No555-555-5555
OriginoriginThe origin of the user.Noloyalty_2022
LanguagelanguageThe language of the user.Noen-us
AgeageThe age of the user.No25
CompaniescompaniesThe companies the user is associated with.No["Lytics", "Pantheon"]
GendergenderThe gender of the user.NoM
StatusstatusThe status of the user.Noactive
User Attributesuser_attributesA map of custom attributes associated with the user.No{"role": "member", "bonus": "active"}
TimezonetimezoneThe timezone of the user.No-7
CitycityThe city of the user.NoDenver
CountrycountryThe country of the user.NoUS
StatestateThe state of the user.NoCO

Meta

Meta encompasses all system-level information that provides insights into the health and breadth of the profile. This includes data such as creation date, last update timestamp, source information, and other metadata associated with the profile's management and maintenance. Metadata offers a behind-the-scenes view of the profile's overall status and administration.

NameSlugDescriptionComputedExample
Created_createdThe date the user was created.Yes2023-12-12T21:09:11.625960142Z
Last Scored_last_scoredThe date the user was last scored.Yes2024-02-28T02:45:51.377423153Z
Modified_modifiedThe date the user was last modified.Yes2024-02-28T02:45:51.377423473Z
Number of Aliases_num_aliasesThe number of aliases for the user.Yes1
Number of Days_num_daysThe number of days the profile has existed.Yes38
Number of Events_num_eventsThe number of events the user has been associated with.Yes2425
Number of Streams_num_streamsThe number of streams the user has been associated with.Yes2
Stream Names_streamnamesThe names of the streams the user has been associated with.Yes["default", "ios"]
User is Botis_botWhether the user has been flagged as a bot or not.Yesf

Behavior

Behavioral attributes typically cannot be directly managed but represent a set of insights derived from a user's behavior over time. These insights are invaluable when personalizing experiences based on changes in behavior or behaviors indicative of high likelihood. For instance, you might want to present a premium offer to users exhibiting higher momentum than usual. Behavioral attributes enable targeted and timely interventions tailored to user actions and patterns.

NameSlugDescriptionComputedExample
Consistencyscore_consistencyScore representing how consistent their activity patterns are.Yes99
Frequencyscore_frequencyA score representing how frequently the user is active.Yes63
Intensityscore_intensityA score representing how intense the user's activity is.Yes94
Maturityscore_maturityA score representing how mature the user's activity is.Yes34
Momentumscore_momentumA score representing how much momentum the user currently has.Yes54
Propensityscore_propensityA score representing how likely the user is to engage again.Yes1
Quantityscore_quantityA score representing how much activity the user has.Yes99
Recencyscore_recencyA score representing how recent the user's activity is.Yes99
Volatilityscore_volatilityA score representing the degree of variability in behavior.Yes99

Interests

Interests entail understanding the topics a user is interested in based on their interactions, cross-referenced by deep programmatic analysis of their online activities. This allows for tailored content recommendations and targeted messaging aligned with the user's preferences and engagement history.

NameSlugDescriptionComputedExample
Lytics Contentlytics_contentA map of topic-level interests for the user.Yes{"Baking": 0.26418695138978837}

Intelligence

Attributes classified as intelligence encompass diverse, highly valuable information to facilitate relevant and high-value personalized experiences. Within this category, you'll discover real-time segment membership, values crucial for split testing and experimentation, and direct correlation to our real-time machine learning modeling. These attributes empower dynamic and data-driven decision-making, enhancing the efficacy of personalized marketing strategies.

NameSlugDescriptionComputedExample
Segment Membership_segmentsThe segments the user is associated with.Yes["all", "anonymous_profiles", "smt_power"]
Split_splitA random value that is evenly distributed across users.Yes74
Split 2_split2A random secondary value that is evenly distributed across users.Yes58
Needs Messageneeds_messageStream-specific score that represents the relative distance between now and the next predicted event.Yes{"default": 0.05758899316182292}
Next Eventnext_eventStream-specific prediction for the next expected event.Yes{"default": "2024-03-01T03:00:00Z"}
Lookalike Model Predictionssegment_predictionScores from Lytics Lookalike and SegmentML models.Yes{"likely_to_churn": 0.26418695138978837}
Lookalike Model Percentilessegment_prediction_percentilePercentiles from Lytics Lookalike and SegmentML models.Yes{"likely_to_churn": 0.26418695138978837}

Activity

Activity encompasses the user's engagement across different channels and campaigns, including clicks and conversions. It provides valuable insights into recent interactions, aiding campaign optimization and channel effectiveness assessment.

General

NameSlugDescriptionComputedExample
First Seenevent_first_seenThe first time the user was seen for a specific event.No{"click": "2023-12-12T21:09:11.625Z"}
Last Seenevent_last_seenThe last time the user was seen for a specific event.No{"click": "2024-02-28T02:45:49.776Z"}
ChannelschannelsThe channels the user has been active on.No["web", "email"]
DevicesdevicesThe devices the user has been active on.No{"desktop": 123}
HourlyhourlyThe number of events per hour for the user.Yes{"0": 17, "1": 69, "2": 262, "3": 97}
Hour of WeekhourofweekThe number of events per hour of the week for the user.Yes{"3": 2, "4": 2, "5": 1, "11": 3}
Last Activelast_active_tsThe last time the user was active.No2024-02-28T02:45:50.784Z
Last Channel Activitieslast_channel_activitiesThe last time the user was active on a specific channel.No{"web": "2024-02-28T02:45:50.784Z"}

Web

NameSlugDescriptionComputedExample
DomainsdomainsThe domains the user has been active on.No["umami.lytics.com"]
First Visit Timestampfirstvisit_tsThe first time the user visited the site.No2023-12-12T21:09:11.625Z
Last Visit Timestamplastvisit_tsThe last time the user visited the site.No2024-02-28T02:45:50.784Z
Pageview CountpageviewctThe number of pageviews the user has had.Yes234
Referring DomainrefdomainThe referring domain for the user.No["umami.lytics.com"]
User Agentuser_agentThe user agent for the user.YesChrome
Visit CountvisitctThe number of visits the user has had.Yes145
Visit Cityvisit_cityThe city the user visited from.YesDenver
Visit Countryvisit_countryThe country the user visited from.YesUS
Visit Regionvisit_regionThe region the user visited from.YesCO
Form Dataform_dataThe form data the user has submitted.No{"first_name": "John"}
Forms Submittedforms_submittedThe forms the user has submitted.No["newsletter", "contact"]
UTM Campaign Lastutm_campaign_lastThe last UTM campaign referred from.Noholiday
UTM Campaignsutm_campaignsThe UTM campaigns the user has interacted with.No["holiday", "summer"]
UTM Content Lastutm_content_lastThe last UTM content referred from.Norecipe-1
UTM Contentsutm_contentsThe UTM contents the user has interacted with.No["recipe-1", "recipe-2"]
UTM Medium Lastutm_medium_lastThe last UTM medium referred from.Noarticle
UTM Mediumsutm_mediumsThe UTM mediums the user has interacted with.No["article", "recipe"]
UTM Source Lastutm_source_lastThe last UTM source referred from.Nogoogle_ads
UTM Sourcesutm_sourcesThe UTM sources the user has interacted with.No["google_ads", "meta_ads"]
UTM Term Lastutm_term_lastThe last UTM term referred from.Noexample
UTM Termsutm_termsThe UTM terms the user has interacted with.No["example"]

Campaign

NameSlugDescriptionComputedExample
Hoverly_hoverThe number of times the user hovered over a specific campaign.No{"content-rec-modal": 5}
Impressionsly_impressionsThe number of times the user saw a specific campaign.No{"content-rec-modal": 1}
Closesly_closesThe number of times the user closed a specific campaign.No{"content-rec-modal": 10}
Conversionsly_conversionsThe number of times the user converted on a specific campaign.No{"content-rec-modal": 2}
Milestonesly_milestonesThe number of times the user reached a milestone on a campaign.No{"engaged-donation-page": 1}
Goalsly_goalsThe number of times the user reached a goal on a campaign.No{"made-donation": 1}

Examples

Identifiers

Lytics ID (_id)

This is an automatically generated canonical ID managed by Lytics. It refers to the materialized profile and cannot be customized or overridden.

Current Lytics Cookie (_uid) and All Lytics Cookies (_uids)

_uid represents the Lytics anonymous 1st party cookie. This value is automatically captured with every jstag.send() call from the JavaScript tag. The only way to customize this value is to explicitly set the value of _uid, which we do not recommend.

jstag.setid("somecustomvalue");
jstag.send();

Email (email)

jstag.send({
  email:"[email protected]"
});

Unique User ID (uuid)

jstag.send({
  uuid:"someuniqueuserid"
});

Details

First Name (first_name)

jstag.send({
  first_name:"John",
});

Last Name (last_name)

jstag.send({
  last_name:"Doe",
});

Title (title)

jstag.send({
  title:"President",
});

Phone (phone)

jstag.send({
  phone:"555-555-5555",
});

Cell (cell)

jstag.send({
  cell:"555-555-5555",
});

Origin (origin)

jstag.send({
  origin:"loyalty_2022",
});

Language (language)

By default, the Lytics JavaScript SDK will collect language information based on the browser, but this can be overridden.

jstag.send({
  _ul:"en-us",
});

Age (age)

jstag.send({
  age:25,
});

Companies (companies)

jstag.send({
  companies:["Lytics", "Pantheon"],
});

Gender (gender)

jstag.send({
  gender: "N/A",
})

Meta

Created (_created)

Lytics automatically generate this and represents the oldest event associated with the user.

Modified (_modified)

This is automatically generated by Lytics and represents the last time the user was modified.

Last Scored (_last_scored)

This is automatically generated by Lytics and represents the last time the users scores were updated.

Number of Aliases (_num_aliases)

This is automatically generated by Lytics and represents the number of aliases associated with the user.

Number of Days (_num_days)

This is automatically generated by Lytics and represents the number of days the user has existed.

Number of Events (_num_events)

This is automatically generated by Lytics and represents the number of events associated with the user.

Number of Streams (_num_streams)

This is automatically generated by Lytics and represents the number of streams associated with the user.

Stream Names (_streamnames)

This is automatically generated by Lytics and represents the names of the streams associated with the user.

User is Bot (is_bot)

This is automatically generated by Lytics and represents whether the user has been flagged as a bot or not.

Behavior

The following attributes are all computed in real-time as the profile evolves. Each of the behavioral attributes are surfaced as a score between 0 and 100. These scores represent an aggregate summary of the user's behavior across various dimensions: Consistency, Frequency, Intensity, Maturity, Momentum, Propensity, Quantity, Recency, and Volatility.

Interests

Lytics Content (lytics_content)

The interest attributes are computed in real-time and represent the user's interest in various topics. These topics are generated as a result of the analysis done by the Lytics Interest Engine and then associated with the user based upon their interaction with content on your site.

Intelligence

Segment Membership (_segments)

This attribute displays an array of all segments the user is currently a member of. It updates in real-time based on various audience definitions. Lytics offers a range of useful segments out of the box, requiring no additional setup. For detailed information on these audiences, refer to our Developer Tier > Audiences documentation.

Split & Split2 (_split & _split2)

These attributes are automatically generated by Lytics and represent a random value evenly distributed across users. They are useful for split testing and experimentation.

Needs Message (needs_message)

This attribute is computed in real-time and represents the relative distance between now and the next predicted event. It is stream specific and is useful for understanding when a user is likely to engage again.

Next Event (next_event)

This attribute is computed in real-time and represents the next expected event. It is stream specific and is useful for understanding when a user is likely to engage again.

Lookalike Model Predictions & Lookalike Model Percentiles (segment_prediction & segment_prediction_percentile)

This attribute is computed in real-time and represents the scores resulting from Lytics Lookalike and SegmentML models. Out-of-the-box, Lytics offers a range of useful models, requiring no additional setup. For detailed information on these models, refer to our Developer Tier > Models documentation.

Activity

General

First Seen & Last Seen (event_first_seen & event_last_seen)

Both of these attributes are automatically populated based upon the _e value in the jstag.send payload. By default Lytics will collect a pv event for each page view and this will automatically populate the first_seen and last_seen attributes. Below is an example of collecting a custom event that would populate these attributes as well.

jstag.send({
  _e:"custom_event"
});```
 

Channels (channels) [needs update]

jstag.send({
  _channel:"web",
});

Devices (devices)

jstag.send({
  _device:"desktop",
});

Hourly (hourly)

This attribute is automatically populated with a count of events per hour for the user.

Hour of Week (hourofweek)

This attribute is automatically populated with a count of events per hour of the week for the user.

Last Active Timestamp (last_active_ts)

This attribute is automatically populated with the last time an event was received in any stream for the user.

Last Channel Activities (last_channel_activities) [needs update]

jstag.send({
  _channel:"web",
});

Web

Domains (domains)

This attribute is automatically populated with the domains the user has been active on.

First Visit Timestamp (firstvisit_ts)

This attribute is automatically populated with the first time the user visited the site and sends data to the default stream.

Last Visit Timestamp (lastvisit_ts)

This attribute is automatically populated with the last time the user visited the site and sends data to the default stream.

Pageview Count (pageviewct)

This attribute is automatically populated with the number of _pv events recieved for the user.

jstag.send({
  _e:"pv"
});

Referring Domain (refdomain)

This attribute is automatically populated with the referring domain for the user.

jstag.send({
  _ref:"umami.lytics.com",
});

User Agent (user_agent)

This attribute is automatically populated based on the user agent of the browser. This attribute must be turned on in your Lytics account to be collected.

Visit Count (visitct)

This attribute is automatically populated with the number of visits the user has had based on presence of the _sesstart key in an event.

jstag.send({
  _sesstart:1
});

Visit City (visit_city)

This attribute is automatically populated with the city the user visited from based upon GeoIP.

Visit Country (visit_country)

This attribute is automatically populated with the country the user visited from based upon GeoIP.

Visit Region (visit_region)

This attribute is automatically populated with the region the user visited from based upon GeoIP.

Form Data (form_data)

Form data is a wildcard attribute that allows you to pass a number of key value pairs that all get stored under the form_data attribute. This is useful for capturing form submissions.

jstag.send({
  formdata_fn:"John",
  formdata_ln:"Doe",
  formdata_someotherkey:"somevalue"
});

Forms Submitted (forms_submitted)

jstag.send({
  form_name:"newsletter"
});

UTM Campaign Last (utm_campaign_last)

jstag.send({
  utm_campaign:"holiday"
});

UTM Campaigns (utm_campaigns)

jstag.send({
  utm_campaign:"holiday"
});

UTM Content Last (utm_content_last)

jstag.send({
  utm_content:"recipe-1"
});

UTM Contents (utm_contents)

jstag.send({
  utm_content:"recipe-1"
});

UTM Medium Last (utm_medium_last)

jstag.send({
  utm_medium:"article"
});

UTM Mediums (utm_mediums)

jstag.send({
  utm_medium:"article"
});

UTM Source Last (utm_source_last)

jstag.send({
  utm_source:"google_ads"
});

UTM Sources (utm_sources)

jstag.send({
  utm_source:"google_ads"
});

UTM Term Last (utm_term_last)

jstag.send({
  utm_term:"example"
});

UTM Terms (utm_terms)

jstag.send({
  utm_term:"example"
});

Campaign

Hover (ly_hover) [needs update]

jstag.send({
  pf_widget_id: "content-rec-modal",
  pf-widget-event: "hover"
});

Impressions (ly_impressions) [needs update]

jstag.send({
  pf_widget_id: "content-rec-modal",
  pf-widget-event: "show"
});

Closes (ly_closes) [needs update]

jstag.send({
  pf_widget_id: "content-rec-modal",
  pf-widget-event: "close"
});

Conversions (ly_conversions) [needs update]

jstag.send({
  pf_widget_id: "content-rec-modal",
  pf-widget-event: "conversion"
});

Milestones (ly_milestones) [needs update]

jstag.send({
  pf_widget_id: "engaged-donation-page",
  pf-widget-event: "milestone"
});

Goals (ly_goals) [needs update]

jstag.send({
  pf_widget_id: "made-donation",
  pf-widget-event: "goal"
});

What’s Next