Please read 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.

The object graph nature of a datamart is particularly adapted to capture the 'user graph' where each piece of information associated to a user is connected with the others in a local graph structure.

Please read page on User guides.

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

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:

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.

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.

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.

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:

• User accounts

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):

• UserActivity represent an interaction with the user

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

• are here to capture that the user belongs to a particular group of users

• capture that the user has given his consent for a specific type of data processing

• UserTrait represent a calculated attribute used to describe the user.

Imagine you are going to a site with firefox, and you go back to that site later on safari. At the moment, there is no way for the platform to know that all those actions are from the same user. You'll have two UserPoints: one with the UserDeviceTechnicalId from firefox and the other with the UserDeviceTechnicalId from safari. If later you log into the site with your UserAccount on both navigators, the two UserPoint will be associated with a common UserAccount ID identifier. The platform will automatically merge the two UserPoint and their activities to represent the reality that we now know those two users were the same.

UserPoint merges can only be triggered by :

• updates

The merge will result in a UserPoint survivor, to which we migrate all the data related to the previous two UserPoint.

For IP address, you have 2 options :

Option 1 : Activate auto IP address capture for a CTV channel

Option 2 : Create dedicated registries then activate auto capture

Option #1

If you wish to have 2 dedicated registries for each CTV channel

1. Go to Navigator > Settings >Channels

2. Select an existing CTV Channel or create a new one

3. In the IP addresses capture section, toggle "Active"

4. Save

This will automatically create dedicated registries to store IPv4 and IPv6 addresses. All captured IP addresses will then be stored in one of these two registries.

If you have multiple CTV channels don't forget to do this for each one.

If you wish to use the same 2 registries for all your CTV channels

1. Create 2 devices registries Navigator>Settings>Organisation>Device Registries

   1. One of type IP_V4_ADDRESS_ID

   2. One of type IP_V6_ADDRESS_ID

Users can have multiple UserEmail registered on the platform. They have the following properties:

The hash property is mandatory and is the property used to identify a user (provided in $hash property in User activities request).

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.

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).

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

Here are general steps to follow in order to activate a Compartment on a specific datamart:

• If you have a single datamart, the Compartment is automatically activated.

• If you have multiple datamarts, you must activate it on every datamart

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

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

2. In the First-party user account compartments section, click on New Compartment.

If you wish to leverage data stored in your data warehouse, you can declare you own data warehouse in mediarithmics as a data source.

We currently support the following warehouses :

Learn more :

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.

For ID5, you need to :

1. Subscribe to ID5

2. Activate ID5 data ingestion from websites

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

With data visualisation, you can create dashboards :

• In your datamart's home page

• In your segments

• In the standard segment builder

Those dashboards will answer questions like :

• What is ingested in the platform?

• Do we have moments where we ingest less data than on other days?

• Who are my users?

Create your first dashboard with our guide.

Speed up your learning curve with useful examples in our .

Definition

A computed field is a dynamic field defined in the schema, linked to a script that is triggered for each new user activity or profile update and performs a computation of a function's result on a regular basis. It is used when the desired outcome cannot be achieved with existing directives or to simplify an OTQL query by replacing multiple directives with a single field.

Those fields can be used in segment definition to improve analysis and sharing, or in the Experiment feature to enhance control groups creation.

Examples

Those examples are handled by computed fields:

• Retrieve the most recent event (e.g. last visit, last transaction, etc.)

• Get the total amount for a specific type of event (e.g. sum of transactions in the last 30 days, total expenditure on a particular category of product, etc.).

• Weighted sum (Affinity total, etc.)

• Compare multiple channels (e.g. to find the best channel for transactions).

This example should be handled by a standard directive:

• Get all users with at least a specific amount for one category

This example can't be handled by a computed field:

• Identify the top X% of a specific user group (e.g. the top 10% of buyers).

Once live, a computed field behaves like a regular field, making it transparent to the user. Here's how you can use it in practice:

Use Case: Identify Users Who Have Spent More Than 100€ on IT Products in the Last 3 Months

With Standard Directives:

This query uses standard directives to sum the basket amounts for IT products over the last 3 months.

With a Computed Field:

In this version, the computed field IT_amount_3months is used directly in the query. The field is calculated on a regular basis, meaning no computation is performed during query execution. This results gives faster query response times, as the value is precomputed and readily available.

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 .

How-to

1. Use the bulk import endpoints to create a document import with the USER_POINTS_DELETION document type and APPLICATION_X_NDJSON mime type. Only ndjson data is supported.

2. Create anwith your commands formatted in ndjson. Each line can represent either a user agent, a user email, a user account or a UserPoint ID. Their respective syntax is detailed in .

Please note that the uploaded data is in ndjson and not json. That means the different deletions are not separated by commas, but by a line separator.

A list of possible user_agent_id values can be found at

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 merge UserPoint and especially when at least one of the following conditions is met:

• the frequency of user identifiers creation in this UserPoint reaches:

  • more than 10 identifiers by Registry ID in 2 days,

  • more than 4 Account ID in 7 days,

  • more than 4 Email hash in 7 days,

• more than 8 UserPoint were merged to that UserPoint in the last 14 days,

• more than 100 user identifiers are attached to this UserPoint.

This system is a preventive action of flagging a UserPoint so that the UserPoint is still actionable in mediarithimics.

The quarantine job is a monthly process that flags any UserPoint which has too many objects attached to it. This happens especially when at least one of the following conditions is met:

• more than 10.000 ,

• more than 5.000 .

A UserPoint in quarantine can still be looked up in Navigator but no more additions will happen to it.A UserPoint can be freed by the quarantine job if it gets below the above-mentionned limits. This particularly happens by deleting activities (through API or when cleaning rules are applied) on a quarantined UserPoint.

Pre-requisite

You first need to declare your data warehouse as a data source in mediarithmics as detailed in the data warehouse management section.

Synchronization strategies

When trying to ingest data from a data warehouse to mediarithmics, you will need to configure a service account with specific rights on the data you want to expose : the service account will need to at least have read access on the data.

Working dataset/schema

If you want to use synchronization strategies that can handle updates and deletes on existing data, you will need to create a working dataset/schema. On this dataset/schema, the service account should have editing rights : this will be used to create temporary tables and track changes on your tables.

Available strategies

• Profile data

  • Timestamp based : all updates in the source table (insert, update, delete) are impacted in mediarithmics. This requires a timestamp column tracking the last update of each line of the table.

  • Hash table : all updates in the source table (insert, update, delete) are impacted in mediarithmics. A temporary table is created based on a hash of columns of the source table.

  • Full import : every synchronization will import the full table. It does not handle deletes or updates, this is a append-only strategy.

• Event data

  • Timestamp based : import all events that occurred since the last import. This requires a timestamp column tracking when each event of the table has occurred. It does not handle deletes or updates, this is a append-only strategy.

Without a working dataset/schema only append-only strategies are available : timestamp based for event data, and full import for profile data.

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

GraphQL

The GraphQL API 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.

OTQL

The OTQL Language is an extension to GraphQL. It is based on the same schema and adds unique features to query a graph of millions/billions of objects.

Analytics

We provide an analytics data cube to query user activities.

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.

A DashboardContent is composed of sections and cards.

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

• Each card is a white block organizing charts 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 :

• h is the number of rows that the card takes.

• w is the number of columns that the card takes

• {x,y}

Here is a sample grid with five cards and their corresponding properties :

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 plugin 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

• Plugin versions with

  • Deployed code

  • An external service referencing partner’s API

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.

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:

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.

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

Dashboards can be displayed on:

• Datamart's home page with the home scope.

• Segments page with the segments scope.

• Standard segment builders with the builders scope.

• A specific set of segments with the segments scope and segment IDs in the segment_ids property.

• A specific set of standard segment builders with the builders scope and builder IDs in the builder_ids property.

You can have multiple dashboards at the same scope.

If you have multiple dashboards to be displayed at a given scope, tabs will be created to switch between them. If you don't have multiple dashboards, it is displayed without any tab.

See for managing dashboards by API.

If you want to start from an existing dashboard, you can

1. Open the dashboard you want to clone on the computing console

2. Go to the Advanced tab and copy the whole JSON

3. Create a new dashboard

Context

Check User guides documentation to learn more about this feature.

Prerequisites

URLs & hits must be captured into mediarithmics in order to perform the contextual extraction and semantic analysis. This can be done either through:

• User event tag (user tracking)

• Contextual targeting tag (no user tracking)

• (for mobile app only. url field needs to be populated in that case)

To configure the Contextual targeting feature, follow the setup steps below depending on your needs:

1. - MANDATORY. This initial configuration is required for all customers using the Contextual targeting feature

2. - OPTIONAL. Complete this step only if you plan to activate your contextual targeting lists. You do not need this step if your goal is solely to use contextual extraction for audience building within the platform

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 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.

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

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.

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

• Separator: Comma (,)

• Quotes: Double quotes (

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.

A computed field is an instance of a Datamart function applied within a specific context. To use it, it must be declared in your schema.

A Computed Field Function is a of type COMPUTED_FIELD_FUNCTION, which can be created via the API following the standard or through the computing console interface.

A computed field is defined by these elements:

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

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 .

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.

• Computes browser-side which segments the user enters or leaves.

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)

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.

There are few ways to export data depending on your usage.

Use if you need to export audience segments data to your partners' solutions and keep it recurring and synchronized.

If you need a feed to deliver its data to an external system, first set up a destination file to define where and how those files are delivered.

Create to create ndjson files corresponding to results of .

by sending all the ingested data to Google Cloud Platform (Pub/Sub) or Microsoft Azure (Event Hubs - Alpha).

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).

1. Use the endpoints to create a with the USER_DEVICE_POINTS_DELETION document type and APPLICATION_X_NDJSON mime type. Only ndjson data is supported.

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.

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

• Website tracking

• Application tracking

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.

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.

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.

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)

   • UTIQ martechpass (fixed)

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:

The storage method for UTIQ identifiers depends on their type, which directly impacts how UserPoint are merged:

• UTIQ martechpass (mobile): stored as UserAccount identifiers in compartment 20. The identifier value is provided in the user_account_id property. These act as reconciliation keys, triggering a when the same identifier is seen across different sessions.

• UTIQ martechpass (fixed): stored as UserProfile in compartment 21. The identifier value is provided in the $utiq_martechpass_fixed property. These are anonymous profiles (no user_account_id and therefore not linked to any userAccount) and do not trigger any UserPoint merge.

For the following integration to work, you must add the amp-analytics module on the page environment.

Here is an example of the AMP tag:

<amp-analytics type="mediarithmics">
  <script type="application/json">
    {
      "vars": {
        "site_token": <SITE_TOKEN>,
        "event_name": "amp-test-pageview"
      },
      "extraUrlParams": {
        "prop1": "value1",
        "prop2": "value2"
      }
    }
  </script>
</amp-analytics>

Arguments

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

• All user activities need to be sent to mediarithmics using the

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

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.

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

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

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(state: State, userActivity: UserActivity): State

• Inputs:

1. Make sure the BigQuery project has the correct API enabled

2. Create dedicated datasets

3. Create a dedicated service account with the right set of permissions

UserAccount identify a user registered on your different systems, like your CRM, a loyalty program or any authentication system. They have the following properties:

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:

1. Aggregate the order amounts over the last 12 months.

2. Filter the data based on the IT category (or any other category, depending on the use case).

By scaling this approach, you can calculate the sales amount for multiple categories, ensuring flexibility and extensibility in your calculations.

In this use case, we need to declare the State, Result, and UserActivity for computing the sales amount over the last 12 months for items in the IT category.

The State stores the order amounts for each day, categorized by IT items, for the last 12 months. Here's how the state structure looks :

The activities_for_the_last_12_months keeps the amount data for each day, where the date is represented by a numeric value (e.g., timestamp), and the amount is the total order value for that day.

The Result represents the total sales amount over the last 12 months for the IT category.

The result will return the computed IT_amount_for_the_last_12_months after summing up the values stored in the state.

The UserActivity defines the structure of the activity that triggers the update. In this example, we focus on the items bought, particularly in the IT category.

The items_bought array contains details about each item, such as category (IT, for example) and price.

With the context declared, you can implement your computed field logic. Here's how the MyComputedField class looks:

• Goal: Update the state with new IT category purchases and remove activities older than 12 months.

  • It filters the UserActivity to ensure only IT items are included.

  • Removes old activities beyond the 12-month period.

• Goal: Sum all the basket amounts stored in the state and return the total.

  • It checks each stored activity date to ensure it is within the last 12 months and sums the amount for each IT purchase.

Before deploying the plugin, create tests to ensure the function behaves as expected. Test cases might include:

• Adding new activities and verifying the state is updated correctly.

• Checking that old activities are purged.

• Validating that the correct basket amounts are returned in the result.

Once the computed field is implemented, you need to declare it in your schema as follows:

This will link the computed field (IT_amount_for_the_last_12_months) to your schema, making it available for querying.

When working with profile and activity information:

Be mindful that the computed field needs to be commutative during the initial loading. For cases where UserProfile and UserActivity need to be aggregated, it might be necessary to store more information in the State to ensure consistency and accuracy during this phase.

Example use-case: Basket amount by fidelity card

During initial loading, if you need to track the basket amount for current fidelity cards, you may need to store all activities grouped by fidelity card in the state. This is because, until the initial loading completes, you may not know which fidelity cards are currently active for the user.

Use this feature to UPSERT or DELETE user_choices in your datamart.

How-to

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

Then, create an execution with your user choice import commands formatted in ndjson .

User choice import command

Each line in the uploaded file can have the following properties:

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 Edit.

Feed details overview

The feed details are divided into three sections to enhance understanding:

1. :

   • Provides a high-level summary of the feed.

   • Shows the status of the feed instance.

   • Displays the identifiers sent to the partner for addition or deletion.

2. :

   • Lists all plugin properties configured for the current instance.

3. :

   • Offers an in-depth view of processing steps, aiding in the investigation of anomalies.

The Stats tab is tailored for non-technical users to easily assess feed activity. It shows whether the feed is successfully transmitting identifiers to the destination platform.

1. Creation:

   • Waiting for Activation: The feed card is created on the segment but remains inactive.

   • Feed Activated: The feed creates the segment on the destination platform (triggers the onExternalSegmentCreation function).

• Successful Identifier Transactions: number of upserts or deletions of identifiers successfully sent during the selected period.

• Identifier Coverage: percentage of UserPoint without any identifiers sent to the destination platform.

• Daily Graph: visual representation of upserts or deletions sent daily.

Client-side feeds have simpler functionality but use a similar interface.

• PAUSED: the feed card is created but not activated.

• ACTIVE: the feed is activated on the segment and the feed is downloaded by the browser.

• Daily Downloads: number of times the feed was downloaded by the browser during the selected period.

The Configuration tab displays the plugin properties for the feed instance:

• Plugin properties layout is determined during the creation of the plugin version.

• These values can only be modified when the feed is not activated. Once activated, the configuration becomes read-only.

The Troubleshooting tab is designed for technical users, providing detailed insights into:

• Successful and failed operations (e.g., upserts and deletions).

• Errors and processing steps for investigating anomalies.

• Server-Side and Client-Side feeds:

  • Resource details, plugin information, and plugin version information.

• Server-Side Feeds only:

1. API Calls to the Audience Feed Plugin:

   • Displays response status for /user_segment_update calls.

2. Identifiers Sent to the Destination Platform:

• Push: Adding a UserPoint to the segment or an identifier to a UserPoint.

• Remove: Removing a UserPoint from the segment or an identifier from a UserPoint.

• Statuses:

• API Calls to the Audience Feed Plugin (/batch_update route):

  • Number and status of batches created.

• Records Sent to the Destination Platform:

• Files Sent by File Delivery Service:

  • Number of files sent and response statuses.

• Records Sent to the Destination Platform:

• Displays a daily graph of browser downloads, as shown in the Stats tab.

Steps

1. Service account and permissions setup

2. Create dedicated schemas

3. Configure key-pair authentication

4. Create and format a credentials JSON file

For the initial set up, start with the SQL script template below. The script will :

• Create a dedicated service account, role, and warehouse for mediarithmics to use

• Give the service account read only access on a database of your choice and all its content

We recommend to create two dedicated schemas for mediarithmics' usage.

• mics_workspace schema that holds all temporary tables created while syncing with your warehouse

• mics_output schema that holds all output tables — the clean results of daily processing, ready for consumption

The service account you created should have full access (read/write) on both schemas. Use this code to do so :

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)

Then, 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 service account created for mediarithmics to use :

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

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

Select your Cloud Data warehouse provider and define the following properties :

• A name

• A description

• A location (BigQuery only)

• A working dataset/schema ()

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

Use this feature to add or remove from .

1. Use the endpoints to create a with theUSER_SEGMENTdocument type and APPLICATION_X_NDJSON or TEXT_CSV mime type.

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

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

Number of active users

Number of activities per day

Values of a dimension

Active users and the revenue per day per device form factor on a specific channel

More use cases

You can create any use case you want using the list of dimensions and metrics that are available.