Please read Users and roles page on User guides.

Please read Communities and organisations page on User guides.

All user data captured or calculated by the mediarithmics platform ends in a special database named datamart. ‌

A mediarithmics datamart is more than a traditional database. This new generation database leverages a database engine specially designed to outperform in the domain of data-driven marketing applications. This database engine is the outcome of more than seven years of R&D from mediarithmics engineering teams. Datamart is the first database engine to provide both real-time query processing and scalability on large data volumes.

A datamart is a multi-model database. Datamart displays information it manages in an object graph, where nodes can either be persisted objects or objects computed on the fly. Datamart then generates column-oriented tables specialized for analytics scenarios.

Welcome to our documentation web site, whether you are Ad / Digital Ops, Data Engineer, Data Analyst or Data Scientist, you will find here all the information you need to make the most of mediarithmics.

Our vision

Our vision is to provide a data-first marketing cloud to solve the need for organisations to serve as best-in-class in a digital economy.

The mediarithmics platform can be seen both as a set of digital marketing applications that can be used directly by marketers, or as a highly customizable data marketing cloud infrastructure that data engineers and data scientists can tailor to the needs of their organisation.

Even if this 'Get started' section is mainly targeted at a technical audience, it is written in plain English, without reference to technical implementation details, and should be readable by anyone interested in the way mediarithmics structures the world of modern marketing. It is a good introduction to the mediarithmics platform.

From collection to activation

The purpose of a data marketing platform is to enable use of all available information to make better decisions in all areas of marketing.

The mediarithmics platform can collect all data sources. Whether the data comes from the users' online activity or from backoffice management systems such as CMS', ERPs, and CRMs, all the data is stored for each user in a graph model as shown below. This universal data model allows you to connect all the information of a user, the collected information such as account identifiers, terminal identifiers, profile information, activities, and purchases, as well as data calculated on the fly, such as appetence score, age and gender predictions, and recommendations of articles or other content.

These centralized data allow us to conduct multiple analyzes (visit analytics, uplift analytics, segment insights, data discovery ...) and to act when the time comes:

• Either by activating audience segments via a rich ecosystem of connectors to advertising networks, communication tools (notifications, emails) or personalization tools (on-site display, A/B testing on user journey),

• Either by defining orchestrations that will be triggered automatically, by aligning themselves with the highlights of the user experience (e.g., first visit, creation of an account, drop in the frequency of visits, etc.).

For all stakeholders

In many companies, the recent history of data marketing has been built by the accumulation of different solutions that stack or overlap. Whether it is for the acquisition of new customers, the improvement of the conversion rate, the retention of existing customers, or the construction of predictive models, each application in isolation makes sense and provides a service to a team.

But after a while, this structuring in silos no longer makes it possible to be reactive and to innovate. The data is multiplied in multiple systems and these systems cannot speak to each other because of the "heaviness" of these huge amounts of data.

The mediarithmics platform has been designed from the start to overcome these structural difficulties and introduce a new model: the data-first marketing cloud.

In this model, the data gathered in a single datamart are shared by all applications and users. Both mediarithmics and partner applications access the same source of truth for analytical queries that mobilize large amounts of data, but also for all real-time personalisation micro-queries that are interested in the characteristics of a single user.

Multi-model database technology

All the user data captured or calculated by the mediarithmics platform ends up in a special database named Datamart.

A mediarithmics datamart is more than a traditional database. This new generation database is leveraging a database engine specially designed to outperform in the domain of data-driven marketing applications. This database engine is the outcome of more than seven years of R&D from mediarithmics engineering teams. It is the first database engine to provide both real-time query processing and scalability on large data volumes (see focus on Datamart).

The mediarithmics datamart is a key enabler of all the use cases that can be imagined in one-to-one marketing … (real-time user experience customization, audience segmentation, funnel analytics, and campaign analytics …).

User 360 view

The information inserted in a datamart is primarily stored following a graph structure, where nodes containing pieces of information are connected through a link to represent a relationship between those pieces of information.

Let's take an example. A user visits a website which implements a tracker connected to the mediarithmics platform. At the end of the visit, three nodes will be created in the datamart.

• One node to represent the user

• One node to represent the visit (e.g., piece of information: the web site, the date, the visit duration, pageviews, products in the basket, etc.)

• One node to represent the device which was used.

Notice that the node representing the user looks like a pinpoint. This node is called a UserPoint and it plays the role of a central pinpoint connecting all pieces of information which are collected about the user.

Any kind of user identifiers

In the User 360 View, it is important to distinguish the nodes representing a user identifier and the nodes representing a describing content on the user.

There are three different types of :

• The UserAccount Id : It represents the identifier of a registered user. The registration system can be a CRM system, a loyalty program system, or an authentication system. This registration system is external to the mediarithmics platform. The UserAccount Id is composed of a character string and a compartment id, which represents the registration system.

• The UserEmail: It represents an identifier based on the user email. It is usually derived from the user email by using a hash function (MD5, SHA-256, …)

• The UserDeviceTechnicalId: it represents the identifier of a UserDevice.

Many different pieces of information attached to a user

The describing content of a user 360 view is composed of different data elements corresponding to different point of view on the user:

• A UserActivity represents an interaction with the user

• A UserProfile represents a summary of timeless information about the user (e.g. firstname, lastname, birthdate, address, sex, age, etc.)

• A UserSegment is here to capture that the user belongs to a particular group of users, such as an audience segment.

UserActivity

A UserActivity represents any interaction with the user either online or offline. It can represent the summary of an online visit, the detailed content of an in-store purchase, the summary of a call to a call-center, a campaign interaction like an opened email or a click on a banner.

A UserActivity can contain a list of events. An event represents the most granular level of interaction with a user. It is defined by a name and a set of custom properties.

For each user, the UserActivity are ordered in a timeline.

For more detailed information on UserActivity and UserEvent, please follow the link below.

UserProfile

A UserProfile provides a summary of information on the user and can contain any kind of information. The UserProfile is usually imported from an existing information system like a CRM or login database, and collect information such as: contact details, the user preferences, the status in a loyalty program, the subscription to newsletters.

UserSegment

A UserSegment node represents a user who belongs to an audience segment. An Audience Segment is a group of users who share some common characteristics in the eyes of the marketer. There are several ways to define an audience segment in the platform.

Whatever the type of Audience Segment, a User 360 view contains a UserSegment node for each audience segment the user belongs to.

For more detailed information on audience segments and their life-cycles, please follow the link below.

UserChoice

A UserChoice node represents the choice of a user regarding data privacy and data processing declared by the organisation. This information is used to automatically adapt the behavior of the applications to consider the user choices about Data Privacy.

UserScore

A UserScore is a set of data calculated dynamically and providing predictive information on a user. A UserScore is connected to a Machine Learning Function (ML Function). A UserScore can, for example, predict the expected lifetime value, churn risk, age, or gender of a user.

Plug-able Custom Logic

All platform services are directly manageable and consumable through APIs.

Beyond these standard capabilities, the platform is also a computing infrastructure which enables the hosting of custom algorithms packaged in plugins. A plugin is a bundle of code and data which defines the behavior of a live function hosted in the mediarithmics cloud. It can be developed in any programming language. Due to latency requirements, some languages may be more adaptable than others.

The mediarithmics platform can integrate these plugins:

• Activity Analyzer: to filter and enrich activities data collected through a tag or the API

• Audience Feed: to push audience segments to third-party systems

• Machine Learning Function: to add live predictive data to a user graph

A large ecosystem of connectors

The mediarithmics platform operates at the heart of an open ecosystem by offering connections with many partners.

Our platform covers your needs, including (but not limited to): analytics tools, messaging solutions, personalization solutions, prediction tools, advertising networks, and audience monetization.

We categorise Network IDs into two groups: Device-based Network IDs and User-based Network IDs. Each category allows you to add custom identifiers or subscribe to existing ones.

To integrate a Device-based or User-based Network ID, the steps are generally:

1. Create a new / Subscribe to a Device Registry or Compartment

2. Activate this Device Registry or Compartment on the desired Datamarts (only required where there is multiple datamarts in your environment)

3. Activate the Device Registry or Compartment ingestion on your Channels

Don't hesitate to check the documentation of a particular Network ID below for more information and detailed procedures.

Managing device registries in the mediarithmics platform

Device-based Network IDs refer to Device Registries because each device is assigned a "registry" where its identifiers are stored.

How to active a Device Registry on a datamart

Here are common steps to activate a Device registry on a specific datamart:

• If you have a single datamart, the Device Registry is automatically activated, and you can go to the next step.

• If you have multiple datamarts, you must manually activate the Device Registry:

  1. Go to Navigator > Settings > Organisation > Device Registries.

  2. Select your Device Registry.

This is the current list of supported data warehouses cloud providers :

• Google BigQuery

To set up your own Big Query data warehouse as a data store for mediarithmics please follow these steps in this order :

For First ID, you need to :

1. Subscribe to First ID

2. Activate First ID data ingestion from Websites

Subscribe to First ID

1. Go to Navigator > Settings > Organisation > Device Registries.

2. In the Shared device registries section, click Manage subscriptions.

3. Subscribe to First ID.

If you have multiple datamarts, don't forget to activate First ID on all datamarts.

Activate First ID ingestion from websites

First ID requires a modification of the user-event-tag to ingest the identifier. Follow these steps:

1. Head to Navigator > Settings > Datamart > Channels.

2. Select the site where you want to activate automated First ID capture.

3. Go to JS Tag Configuration > Device Identification.

4. Enable First ID.

For ID5, you need to :

1. Subscribe to ID5

2. Activate ID5 data ingestion from websites

Subscribe to ID5

1. Go to Navigator > Settings > Organisation > Device Registries.

2. In the Shared device registries section, click on Manage subscriptions

3. Subscribe to ID5.

If you have multiple datamarts, activate the ID5 subscription on all datamarts.

Activate ID5 ingestion for your websites

ID5 requires a specific technical configuration to be properly ingested from your website.

Please contact your Account Manager to complete the ID5 configuration for your channel (an activity analyzer will be added to your channel to handle ID5 IDs).

Follow the steps related to your data warehouse cloud provider :

• BigQuery

• Snowflake

A UserPoint could have multiple user accounts and user profiles on the same datamart. For example, having a profile on one of your sites and another profile on another site.

Compartments are a notion created to represent those different places where you have accounts and profiles. There is always one default compartment on a datamart, and you can create more of them. Compartments are associated with user account IDs to create a user identifier.

Understand UTIQ identifiers

UTIQ proposes two types of identifiers:

• UTIQ martechpass (mobile): refers to identifiers coming from mobile network connections

• UTIQ martechpass (fixed): refers to identifiers coming from internet box connections

Subscribe to UTIQ

To subscribe:

1. Go to Navigator > Settings > Organisation > Compartments.

2. In the Shared user account compartments section, click on Manage subscriptions.

3. Subscribe to:

   • UTIQ martechpass (mobile)

Activate UTIQ ingestion from your websites

UTIQ IDs can be ingested natively via the user-event-tag when users browse your websites.

To enable automatic ingestion:

1. Go to Navigator > Settings > Datamart > Channels.

2. Select the site where you want to activate UTIQ capture.

3. Under JS Tag Configuration > Device Identification, enable:

   • UTIQ martechpass (mobile)

Creation steps

Here are our creation steps for making a new audience segment feed for you :

1. Validation process to ensure we need the connector.

2. Audience segment feed specifications:, studying how we connect to the partner and which parameters should be made available to the user.

3. Code and validation.

4. Cookie matching set up. Some partners need a cookie-matching mechanic so that we can send them identifiers that have meaning for them.

A UserPoint is a 360° vision of a unique user on the platform.

It is composed of different sets of data to cover all the user aspects providing a 360° view (online and offline):

• represent an interaction with the user

• represent a summary of timeless information about the user, like first name, last name, birth date...

Custom User IDs are used to identify your users with your systems such as CRMs, loyalty programs, or authentication systems.

Create a Custom User ID

1. Go to Navigator > Settings > Organisation > Compartments.

This API gives you programmatic access to user activities as a data cube. You get metrics with dimensions, filters, and within date ranges leveraging the part of our multi-model database.

With the activities analytics API, you can create reports to answer questions like:

• Number of active users. The metric is users and it has no dimensions or filter.

Audience segments represents a group of users and are central to most marketing actions. Users can be grouped by common profile characteristics, or similar behavior in their online browsing or in their purchases. Users can be grouped either using the mediarithmics platform, or externally and then imported.

Here is a list of audience segment types that differ from each other by the method used to group users:

• Audience segments calculated from a query (type USER QUERY). Several tools are provided to define this type of segment. Occasional platform users will find it easier to use the Audience Builder. More advanced users such as data admin or data analyst will find a greater wealth of expression in the Segment Builder. Finally, technical users, data engineers or integrators, can directly use a textual query language (see OTQL).

• Audience segments imported from an external system (type USER LIST)

It is important to distinguish the nodes representing a user identifier and the nodes describing content on the user.

Every activity or import that'll have one of those properties set will automatically be related to the correct UserPoint.

Any activity using an identifier that doesn't already exist in the datamart will trigger the creation of a new UserPoint.

There are three different types of user identifiers:

To declare a new data warehouse go to the Computing console, under the Data Store section you will find the Data warehouse menu.

Define the following properties for your datawarehouse :

• A name

• A description

Then upload or drag and drop your credentials file. If the credentials file contains the correct set of permissions (cf ), the data warehouse will be created. If not, an error will be raised.

A is composed of sections and cards.

• Each section has a title and cards disposed on a grid.

• Each card is a white block organizing horizontally or vertically.

The size and position of each card is defined by a 12 column grid, with as many rows as needed. Cards size and position are set with {h,w,x,y} properties :

How it works

mediarithmics contextual targeting module:

1. Identifies the URL browsed by an anonymous user

2. Determines which segments (for panel-based) / targeting lists (for semantic) are bound to the URL

How it works

You can compute segments browser-side using our Edge technology and share them with Google Ad Manager. This allows you to react instantly to user behavior and trigger campaigns as users navigate your website.

To do so, the mediarithmics tag:

• Collects and stores identifiers and navigation data in local storage. When an event is pushed by the tag, it is sent both to the activity processing pipeline and saved locally.

Technical concept

Computed fields are defined in the schema at the UserPoint level.

The plugin behind it performs computations on a regular basis of a function's result, based on the user activities and profile updates.

This document import allows you to mark device points for deletion.

Each line in the document is a user agent identifier that is linked to a device point (or is a device point id directly). For each of those identifiers, the job will find its associated device point and delete it. Note that a device point may be linked to multiple user agent identifiers, all of which will be deleted once their device point is (including user agent identifiers that may not appear in your document).

You have three different ways to query your data in mediarithmics depending on what you want to achieve.

GraphQL

The gives you the ability to query all kinds of UserPoint data, as collected by the platform (profile, activities, segments, identifiers, etc.) with a single request.

Hyper point

A hyper point is a preventive way to flag a UserPoint whenever there are too many identifiers linked to it. Concretely, a UserPoint becomes a hyper point when mediarithmics tries to and especially when at least one of the following conditions is met:

• the frequency of creation in this UserPoint reaches:

Description

This client-side connexion allows you to synchronize a targeting list with Xandr Monetize and therefore run your media campaigns on this targeting list.

Prerequesite

This document import allows you to mark UserPoint for deletion.

Each line in the document is a user identifier that is linked to a UserPoint (or is a UserPoint ID directly). For each of those identifiers, the job will find its associated UserPoint and delete it, along with all of its identifiers, segments, scenario and its profile .

With data visualisation, you can create dashboards :

• In your datamart's home page

Each dashboard is represented by a object. It has a title, scopes, and a . Its content is composed of .

Dashboards can be displayed on:

• Datamart's home page with the home scope.

• Segments page with the segments scope.

Managing compartments in the mediarithmics platform

User-based Network IDs refer to Compartments because each user network ID is assigned a compartment where its identifiers are stored.

Online tracking aims at giving the ability to track unique users across digital properties. We support the following integrations in real time:

• Website tracking

• Mobile tracking

• API tracking

User activities and events

With real-time user tracking, you send user events to mediarithmics that are aggregated and encapsulated into . Some user activities only have one user event, while others can have multiple events.

For example, when you send hits from the JS Tag on a web site, mediarithmics aggregates all the events into sessions, creating one user activity per session.

Session aggregation

If registering a visit as IN_SESSION, the visit will go through the session aggregation step. We aggregate visits in sessions based on the provided identifiers. We close sessions after 30 minutes of inactivity or if a new event is recorded with a referrer from a different domain than the previously recorded. Visits registered as LIVE don't create sessions.

The processing pipeline

When using the real-time tracking capability of mediarithmics, you enter what is called the User Activity Processing Pipeline. It allows mediarithmics to do some processing on your behalf.

Here are the steps of processing, in order :

1. At session closing or every minute if events are live, creation of a user activity.

2. Execution of . They allow you to edit the events in the activity and add new ones based on their shapes.

3. Application of the It is a plugin allowing you to execute code to transform each activity.

The activity is finally stored.

To measure and optimize the performance (i.e., cost per action) of your marketing campaigns, you need to track user conversions driven by your Marketing Campaign.

On the mediarithmics platform, you create a Goal to define a trigger when a user should convert.

There are two ways of configuring a Goal:

1. Creating a Goal and using the associated Pixel. Each user that sees the Pixel in a page will convert for this goal. This Pixel should be present on the confirmation page or dynamically loaded when the user converts (by using a Tag Manager or by including the 'pixel load' in the website logic)

2. Writing a rule that will be run on the Datamart data. It will generate a user conversion each time a user matches the rule.

From experience option #1 is the simplest solution to configure and should be used for each 'non-recurrent' User Conversion tracking.

The option #2 offers more flexibility but with the cost of a longer & more complex configuration. It should be used either when the:

• datamart tracking JS TAGs / Pixels are already present on the website, including the page where the conversion happens

• Goal to track will be used repeatedly

• Goal definition requires a complex rule that can't be achieved by displaying a JS TAG / Pixel

Tracking User Conversions by using Pixels

When creating a goal in the navigator (Campaigns tab > Goals sub-tab):

1. Define the name of the goal.

2. Select Trigger via Pixel.

3. Copy/paste the generated HTML code to your technical team so that they can integrate it.

4. Don't forget to associate the correct attribution model for the attribution of the conversion to your Marketing Campaigns.

Tracking User Conversions by using Datamart Queries

When creating a goal in the navigator (Campaigns tab > Goals sub-tab) :

1. Define the name of the goal.

2. Select Trigger via Query.

3. Define the query that should be used to tell if a User has converted or not.

4. Don't forget to associate the correct attribution model for the attribution of the conversion to your Marketing Campaigns

If you have issues when defining the proper Query to define your Goals, please try Option #1 or reach out to your Account Manager.

Creating a connection to Snowflake requires to set up key-pair authentication

Steps

1. Generate a private and a public key

2. Assign the public key to a Snowflake user

3. Create and format a credentials JSON file

1. Private and public key generation

Use the following command to generate a private key (called rsa_key.p8)

Then use the following command to generate a public key (called rsa_key.pub linked to the private key called rsa_key.pub stored in the current directory)

2. Assigning public key to Snowflake user

From the Snowflake interface or the Snowflake CLI run the following SQL query to assign the public key generated at step 1 (rsa_key.pub) to the snowflake user :

Also make sure the user also has a default warehouse, else run this query :

3. Edit a credentials file

Now you need to generate a JSON file with the following format :

• "user" : the user you associated the public key with

• "account" : the "account" property is what is called "Account identifier" in Snowflake. It's usually the organization name and the account name hyphenated

• "database" : the database containing the data you want to make available to mediarithmics

Bulk import user activities to insert user activities that happened outside of real-time tracking. Those activities usually are offline activities, like store visits or store purchases, but you can adapt it to your use cases.

How-to

Use the endpoints to create a with theUSER_ACTIVITYdocument type and APPLICATION_X_NDJSON mime type. Only ndjson data is supported for user activities.

Then, create anwith your user activities formatted in ndjson .

Example

An audience feed is a plugin acting like a connector that allows mediarithmics customers to push their segments to a third-party platform.

It is generic: once a connector to a partner has been created, every customer can use it. Each audience feed has a specific set of options to adapt to each customer.

Key concepts

An audience external feed is a mediarithmics specific to a partner, but shared across customers. In the UI, it is called Server-side plugin: go to a specific segment, click Add a feed, and you will see a feed type called server-side. It is marketed as connectors or server-to-server connectors.

It has:

• Plugin definition

  • group_id: com.mediarithmics.audience.externalfeed

  • artifact_id: [[partner]]-connector

A feed is an instance of an audience feed. It is specific to a segment in an organisation. It has:

• Feed ID

• Instance properties specified by users in the UI when adding a feed to a segment.

A feed session is initiated whenever an external feed is activated. A new session will also be created if a feed is paused and then reactivated. This session is not visible in the UI.

A feed preset is a template allowing users to easily create feed instances with pre-configured properties. You can, for example, create a Facebook feed preset containing your key for your organisation, and you won't have to remember it every time you set up a new feed.

The UserPoint API allows to retrieve, create, update or delete all data related to a single UserPoint.

General description

The endpoint is formatted as follows:

/v1/datamarts/<DATAMART_ID>/user_points/<USER_POINT_SELECTOR>/<DOCUMENT_TYPE>/<DOCUMENT_SELECTOR>

where

• <USER_POINT_SELECTOR> allows to select a UserPoint based on any of its identifiers

Possible values are:

• <DOCUMENT_TYPE> designates the type of data that will be addressed

Possible values are:

• <DOCUMENT_SELECTOR> allows to select a particular document on the UserPoint

As each document is identified by a different identifier, possible values depend on the type of document previously selected. It is also optional: use it if you want to get, update or delete a particular document.

Description

This client-side connexion allows you to synchronize a targeting list with Google Ad Manager (GAM) and therefore run your media campaigns on this targeting list.

Prerequesite

To synchronize your targeting lists with Google, you will need to :

• Setup on your website

• Have an active account on Google Ad Manager

Setup

You'll need to follow the following steps:

• Configure your Ad Manager network (Admin > Global settings > General settings > Api access > Enabled)

• Add mediarithmics contextual targeting service account user for API access (Admin > Global settings > Network settings > Add a service account user) and specify KeyValue Management role.

A UserPoint can be associated with multiple UserDevicePoint. Each user device point can include:

• Multiple device technical identifiers

• One device information record

Create a new registry

1. Go to Navigator > Settings > Organisation > Device Registries.

2. In the First-party device registries section, click on New Registry.

3. Specify the following details:

Subscribe to an existing registry

1. Go to Navigator > Settings > Organisation > Device Registries.

2. In the Shared device registries section, click on Manage subscriptions.

3. Select Subscribe to the desired registry.

If you have multiple datamarts, don't forget to activate your Device Registry on all datamarts.

For the following integration to work, you must add the on the page environment.

Here is an example of the AMP tag:

Arguments

Description

This client-side connexion allows you to synchronize a targeting list with Equativ’s Monetization Platform (EMP) and therefore run your media campaigns on this targeting list.

Prerequesite

Context

Check for more context on this feature.

How it works

1. Create your computed field function (plugin)

Step 1: Create your computed field function (plugin)

Check the . Bellow are some precisions about specific Computed fields Function implementation.

Develop the plugin with the SDK and create a build from it

OnUpdate functions

These 3 functions are triggered during the initialization of the Computed Field Function and whenever a new activity, profile update, or computed field modification occurs, updating the State accordingly.

.

BuildResult

This function returns the result of the computed field function for a specific State. The Result will be stored for a defined duration, and querying the field will return the stored value during that period.

.

Create a plugin and a version

Follow the standard procedure to with the type COMPUTED_FIELD_FUNCTION. Once the plugin is created, you can from the build.

Step 2: Create your computed field within the platform

1. In the Navigator > Settings > Datamart, navigate to Computed Fields.

2. Add a new computed field.

3. Select the plugin and version of the new instance you want to use.

4. Complete the necessary fields:

This will set up the computed field linked to your computedfield function for use in the schema.

Step 3: Declare your computed field in your schema

After creating the computed field, you need to connect it to your schema:

1. Use the directive @ComputedField(technical_name = "...") at the appropriate level of your schema.

2. Ensure the technical name matches the one used when creating the computed field.

This will integrate the computed field into your schema and make it available for use in queries and operations.

A schema validation will be triggered when you click Save. It will ensure that your Datamart has access to all the declared computed fields and verify their integration with the schema.

Step 4: Wait until the end of the initial loading

Once your schema is validated, our service will run a job to initialize your computed field and set the state to match the current status of the timeline. For each UserPoint in your Datamart, we will process their timeline and execute the OnUpdateXXX() function.

To monitor the progress:

1. Go to the Computing Console > Computed Fields.

2. Check if your computed field is ready or if the initial loading is still in progress.

Please note, this process may take some time to complete (up to 72h) as it computes the timelines for all UserPoint.

Step 5: Use your computed field

Once the initial loading is complete, you can start using your computed field just like any other standard field in mediarithmics features.

For example, in query tools or any OTQL query:

If your computed field is indexed, you can also perform queries like:

This allows you to incorporate the computed field into your data analysis and queries once it's fully initialized.

Setup summary

In order to track activities on your mobile app, you'll need to keep in mind the following :

• All mobile user activities need to be sent to mediarithmics using the Tracking API

• The should be used to authenticate any requests between your app and mediarithmics. Don't hesitate to contact your Account manager to have more information about this.

• The activity and events in the payload need to comply with mobile app specificities described in the

• The user agent identifier ($user_agent_id) needs to be particularly formatted using guidelines

Mobile app integration options

The first option is to integrate a small piece of code (approx. 100 lines) into the mobile application to execute calls to the mediarithmics tracking API. Sample code for iOS and Android is available in the section (see illustration below).

The second option is to re-use an existing analytics tool. It is then possible to transfer events from the analytics solution's server to the mediarithmics API.

Mobile app specificities

Predefined event names

In the context of Mobile App Tracking, predefined event names are available out-of-the-box to simplify and automate event processing ():

• app open event ($app_open) corresponds to the opening of the app and app resume event (when the app becomes active again)

• app install event ($app_install)

• app update event ($app_update)

The install and update events are automatically calculated on server side and you don't have to send them :

• The install event ($app_install) is triggered the first time an app open event is received for a user, regardless if the user is new or existing.

• The update event ($app_update) is triggered when the SDK version, app version or OS version changes from one open to the next.

User activities for mobile apps

In the context of Mobile App Tracking, some fields of the User Activity objects have a limited set of possible values. Details below:

A UserProfile is usually imported from an existing information system, for example CRM, and login database. We usually see our customers using the profile to collect:

• Contact details

• User preferences

• Status in a loyalty program

• Subscription to a newsletter

• CRM information

• Scoring calculated outside the platform

• Aggregated Values

• other details

Each UserProfile is associated with a UserPoint by a . It can also be linked to a UserAccount.

User profile object

Example:

By uploading a specific CSV file, you can "shallow-rename" fields (change their display label) or hide specific properties from users without altering the underlying technical schema.

Decorator CSV Format

Schema decorators are defined using a CSV file. Below are the specifications for the file format.

CSV Dialect Rules

• Separator: Comma (,)

• Quotes: Double quotes (") must be used for strings that contain commas.

Columns Specification

The CSV must contain the following headers.

Example

Code snippet

Managing Decorators

You can manage schema decorators either through the mediarithmics user interface (Navigator) or programmatically via the API.

Via User Interface

In the Datamart > Object View Configuration section of the Navigator Settings, you can use the buttons under the Schema to manage your CSV files directly.

The available actions are:

• Upload new Decorators: Allows you to upload a prepared CSV file to apply new labels and visibility rules. This will overwrite existing decorators for this schema.

• Download Template: Downloads a blank CSV file containing the required headers (OBJECT_NAME, FIELD_NAME, etc.) to help you get started.

• Download Decorators: Downloads the current active decorator CSV file. This is useful if you want to make edits to the existing configuration.

Via API

Prerequisites

You will need the following values:

• DATAMART_ID: The ID of your Datamart.

• SCHEMA_ID: The ID of the Schema you wish to decorate.

• MICS_API_TOKEN: Your API authentication token.

Retrieve Current Decorators

To fetch the existing decorator file for a specific schema:

Bash

Update Decorators

To upload a new decorator CSV file (replacing the existing configuration):

Bash

List of Methods

OnUpdate methods

These functions are triggered during the initialization of the Datamart Function and whenever a new activity, profile update, or computed field modification occurs, updating the State accordingly.

onUpdateActivity

onUpdateActivity(state: State, userActivity: UserActivity): State

• Inputs:

  • state: Current state object.

  • userActivity: New user activity data.

onUpdateUserProfile

onUpdateUserProfile(state: State, userProfile: UserProfile, operation: core.Operation): State

• Inputs:

  • state: Current state object.

  • userProfile: Updated userProfile data.

onUpdateComputedFields

onUpdateComputedFields(state: State | null): Result

• Inputs: state: Current state object or null.

• Outputs: Result object.

• What It Does: Computes the result from the current state.

• Example:

buildResult

buildResult(state: State | null): Result

• Inputs: state: Current state object or null.

• Outputs: Result object.

• What It Does: Computes the result from the current state.

• Example:

Test

Before deploying the plugin, create tests to ensure the function behaves as expected.

Test cases might include:

• Adding new activities, and update profiles and verifying the state is updated correctly. Check that the order we send profile updates and activities has no impact.

• Checking that activities which are no more relevant to compute the result are removed from the state.

• Validating that the correct values are returned in the result.

Best Practices

• How to decide if we create 1 or several plugin(s)? (example with 2 scores)

  • If the required data in the state is the same for both scores → 1 plugin

  • If the required data in the state is different (state is polymorph) → 2 plugins

• Always return the same state in case of error, or null. Otherwise, we delete the state, and so reset the result.

Context for the following example

To demonstrate how the computed field works, consider this use case:

Goal: Calculate the sales over 12 months for items in the IT category per user.

For simplicity, this example focuses on the IT category. However, the computed field can be scaled to compute the amount for all categories, not just IT.

In this scenario, the computed field will:

The user activities endpoint gives you the ability to retrieve & delete any activities (and attached events) on a given UserPoint.

Retrieve activities & events from a UserPoint

GET https://api.mediarithmics.com/v1/datamarts/:datamartId/user_timelines/:userpointId/user_activities?filters=:filters

Accessing the feed

1. Go to the Segment: Navigate to the segment where your feed is configured.

2. Open Feed Options: Click the menu button and choose either Stats or

Use this feature to UPSERT or DELETE in your datamart.

How-to

Use the endpoints to create a with theUSER_CHOICEdocument type and APPLICATION_X_NDJSON mime type. Only ndjson data is supported for user choices.

Dashboards can have filters in the top action bar.

You can set up filters only in advanced mode using the available_filters property of your .

The user can select a value and all the queries in the dashboard adapt to the selected value.

{
    // Using technical names of compartments, segments or channels 
    // will result in IDs being automatically replaced by names in the UI
    "technical_name": String, 
    "title": String,
    "values_retrieve_method": 'Query', // Only available value at the moment
    // OTQL query to retrieve list of selectable values
    // Use a query string, not the ID of a query
    "values_query": String, 
    // How to adapt queries in the dashboard to the selected value(s)
    "query_fragments": [QueryFragment], 
    "multi_select": Boolean, // If the user can select multiple values
}

A query fragment tells the dashboard how to adapt each query to the value(s) selected by the user.

{
    // Any available data source such as 'activities_analytics' or 'OTQL'
    "type": String, 
    // Only for OTQL type, chooses which queries should be transformed
    // Select 'ActivityEvent' to transform queries FROM ActivityEvent
    "starting_object_type": String,
    // The query part to add 
    "fragment": String,
}

Here is a sample with a filter that enables the selection of compartments and an other for channels

This document import allows you to merge UserPoint by associating their . Each line in the document represents a different user identifiers association

This document import allows you to mark for deletion. Each line in the document represents a different object to remove from the platform.

The integration batch is a plugin type used for customers' integrations. It can be periodic or non-periodic plugin. You can choose to pause a recurring plugin so that all the coming executions are canceled.

Imagine you want to create a script that imports data every night for a customer :

• You declare a new integration batch plugin called