Native Consultation

The mrt_native__consultation table provides detailed insights into user consultations within the native application. It captures user identifiers, session details, traffic origin and discovery scores, enabling analysis of user interactions and engagement with offers and venues. This table is essential for understanding user behavior and optimizing the user experience by analyzing the pathways through which users interact with the application.

Table description

Main focus of the table : - It computes the discovery score linked to each consultation, which represents the ability of each user to consult diversified offers. Discovery score is the addition of 3 increments (0 or 1) : item (=1 if the user consults a new offer),category (=1 is the user consults an offer from a new category, which reflects a cultural sector. Exemple : book, cinema, live show..), subcategory (=1 is the user consults an offer from a new subcategory, which is more granular than categories. Exemple : audio book, cinema subscription, festival..) - It provides more information on consultation origin : marketing campaigns linked, home datas (name, audience type..), consultation macro origin (main canal : search, home, similar_offer, deeplink..) and consultation micro origin (more granular information on the canal : type of home, type of research, origin of venue consultation which stems from offer consultation...)

name	data_type	description
consultation_id		The unique identifier for each consultation event.
consultation_date		The date on which the consultation took place.
origin		The origin of the event, indicating where it was triggered from.
offer_id		Unique identifier for the offer.
user_id		Unique identifier for a user.
unique_session_id		A unique identifier for the session, ensuring no duplicates.
item_discovery_score		The discovery score increment related to the discovery of a new item_id. =1 if the user consults a new offer, 0 if not.
subcategory_discovery_score		The discovery score increment related to the discovery of a new subcategory. =1 if the user consults an offer from a new subcategory, which is more granular than categories. Exemple : audio book, cinema subscription, festival..
category_discovery_score		The discovery score increment related to the discovery of a new category. =1 if the user consults an offer from a new category, which reflects a cultural sector. Exemple : book, cinema, live show..)
discovery_score		The total discovery score. It is the addition of item_discovery_score, subcategory_discovery_score and category_discovery_score
is_category_discovered		A boolean indicating if a category was discovered during the consultation.
is_subcategory_discovered		A boolean indicating if a subcategory was discovered during the consultation.
item_id		Identifier for the item associated with the offer used internally by the data science team.
offer_subcategory_id		Identifier for the subcategory of the offer.
offer_category_id		Identifier for the category of the offer.
offer_name		Name of the offer as it appears in the application.
venue_id		Unique identifier for the venue.
venue_name		Name of the venue.
venue_type_label		Type of the venue ('Musée', 'Cinéma','Librairie', etc). Selected by the partner in a drop-down list.
partner_id		Unique identifier of the partner.
offerer_id		Unique identifier of the offerer.
user_region_name		Region name of the user's registered address.
user_department_code		Department code associated with the user's registered address.
user_activity		User's registered activity (student, apprentice, unemployed etc). Registered at first grant deposit and updated when the user applies for its GRANT_18.
user_is_priority_public		Boolean. Indicates if the user considered as a pass Culture priority public (users that are either residing in a rural area, in a QPV or are not in education).
user_is_unemployed		Boolean. Indicates if the user is unemployed as per its registered activity.
user_is_in_education		Boolean. Indicates if the user is in education, based on their registered activity. According to the INSEE, a user is considered to be in education if they fall under one of the following categories: Middle school student (Collégien), High school student (Lycéen), University student (Étudiant), Apprentice (Apprenti), Work-study student (Alternant).
user_is_in_qpv		Boolean. Indicates if the user's registered address is in a priority neighborhood (QPV).
user_macro_density_label		Macro density label of the user's registered address.
traffic_medium		The medium of the marketing campaign (email, push notification ...)that generated the session.
traffic_campaign		The name of the marketing campaign that generated the session.
traffic_source		The source of the marketing campaign (Instagram, Snapchat ...) that generated the session.
module_id		The identifier for the module associated with the consultation.
entry_id		The identifier for the entry associated with the consultation.
home_name		The name of the home associated with the consultation.
home_audience		The audience type for the home.
user_lifecycle_home		The lifecycle stage of the user in relation to the home.
consultation_macro_origin		The macro origin of the consultation, indicating the broader context of its initiation : search, home, similar_offer, deeplink..
consultation_micro_origin		The micro origin of the consultation, which provides more granular information on the canal : type of home, type of research, origin of venue consultation which stems from offer consultation...