Dinamize is an email marketing platform that provides comprehensive tools for managing email campaigns, contact lists, and marketing automation. It enables businesses to create, send, and track email campaigns, manage subscriber lists, segment contacts, and analyze marketing performance through detailed transaction and engagement data.

Configuring Dinamize as a Source

In the Sources tab, click on the “Add source” button located on the top right of your screen. Then, select the Dinamize option from the list of connectors. Click Next and you’ll be prompted to add your access.

1. Add account access

You’ll need to provide your Dinamize credentials to access your data. The following configurations are required:

User: Your Dinamize username (Nome do usuário)
Password: Your Dinamize password (Senha do usuário)
Client Code: Your Dinamize client code (Código do cliente)

The following configuration is optional:

Start Date: (Optional) The earliest date from which records will be synced. If not provided, all available data will be synced. This is particularly useful for streams with replication keys to limit the initial sync window.

Once you’re done, click Next.

2. Select streams

Choose which data streams you want to sync. For faster extractions, select only the streams that are relevant to your analysis. You can select entire groups of streams or pick specific ones.

Tip: The stream can be found more easily by typing its name.

Select the streams and click Next.

3. Configure data streams

Customize how you want your data to appear in your catalog. Select the desired layer where the data will be placed, a folder to organize it inside the layer, a name for each table (which will effectively contain the fetched data) and the type of sync.

Layer: choose between the existing layers on your catalog. This is where you will find your new extracted tables as the extraction runs successfully.
Folder: a folder can be created inside the selected layer to group all tables being created from this new data source.
Table name: we suggest a name, but feel free to customize it. You have the option to add a prefix to all tables at once and make this process faster!
Sync Type: you can choose between INCREMENTAL and FULL_TABLE.
- Incremental: every time the extraction happens, we’ll get only the new data - which is good if, for example, you want to keep every record ever fetched.
- Full table: every time the extraction happens, we’ll get the current state of the data - which is good if, for example, you don’t want to have deleted data in your catalog.

Once you are done configuring, click Next.

4. Configure data source

Describe your data source for easy identification within your organization, not exceeding 140 characters. To define your Trigger, consider how often you want data to be extracted from this source. This decision usually depends on how frequently you need the new table data updated (every day, once a week, or only at specific times). Optionally, you can define some additional settings:

Configure Delta Log Retention and determine for how long we should store old states of this table as it gets updated. Read more about this resource here.
Determine when to execute an Additional Full Sync. This will complement the incremental data extractions, ensuring that your data is completely synchronized with your source every once in a while.

Once you are ready, click Next to finalize the setup.

5. Check your new source

You can view your new source on the Sources page. If needed, manually trigger the source extraction by clicking on the arrow button. Once executed, your data will appear in your Catalog.

For you to be able to see it on your Catalog, you need at least one successful source run.

Streams and Fields

Below you’ll find all available data streams from Dinamize and their corresponding fields:

Contacts

Stream for managing contact information, including email addresses, names, custom fields, and contact preferences.Identifiers:

code - Unique identifier for the contact (primary key)
external_code - External code of the contact

Contact Information:

email - Contact email address
name - Contact name
custom_fields - Custom fields object (cmp4, cmp5, cmp6…)

Dates:

insert_date - Registration date (replication key)
rest_date - Rest date (contacts will not receive emails until the end of the specified day)

Contact Lists

Stream for managing contact lists, which group contacts for campaign targeting and segmentation.Identifiers:

code - Unique identifier for the contact list (primary key)
user_code - User code of the contact list owner

List Information:

title - Contact list title
type - Key field for duplicate verification - EM (email) | CE (external code)
status - Contact list status - LI (released) | EA (under review) | BL (blocked)

Metrics:

total_contacts - Total number of contacts
total_valid - Total number of valid contacts

Dates:

insert_date - Registration date (replication key)
rest_date - Rest date (contacts in the list will not receive emails until the end of the specified day)

Actions

Stream for managing email sends (envios), including campaign dispatches, scheduling, and send statistics.Identifiers:

code - Unique identifier for the send (primary key)
campaign_code - Campaign code
contact-list_code - Contact list code
contact-list_title - Contact list title
filter_code - Filter code
message_code - Message code

Send Information:

title - Send title
subject - Email subject
sender_name - Sender name
sender_email - Sender email
reply_to - Reply-to email
message_type - Message type (CAD, URL)
url - Message URL (if send type is ‘URL’)
action_mark - Verification code to confirm ‘URL’
action_type - Send type (PT - Point, IC - Incremental)
status - Send status (PD - Pending, AG - Scheduled, EN - Sending, EP - Partially sending, FN - Finished)

Metrics:

total_contacts - Total contacts separated for the send
total_sent - Total sent

Dates:

date_create - Registration date (replication key)
date_start - Start date
date_end - End date

Campaigns

Stream for managing email marketing campaigns.Identifiers:

code - Unique identifier for the campaign (primary key)

Campaign Information:

title - Campaign title

Dates:

insert_date - Registration date (replication key)

Messages

Stream for managing email message templates and content.Identifiers:

code - Unique identifier for the message (primary key)

Message Information:

title - Message title

Dates:

insert_date - Registration date (replication key)

Transactions

Stream for tracking consumption/usage transactions, providing billing and usage data for email sends and platform usage.Identifiers:

action_code - Send code (part of composite primary key)
dt_transaction - Transaction date (part of composite primary key, replication key)

Transaction Information:

description - Consumption description
value - Transaction value

Fields

Stream for managing custom fields that can be associated with contacts for segmentation and personalization.Identifiers:

code - Unique identifier for the field (primary key)
code_name - Internal name for reference in other API calls
field-group_code - Field group code
field-group_name - Field group title

Field Information:

title - Field title
type - Field type (LV - List of Values, DH - Date/time, DT - Date, VC - Short text, TXT - Long text, INT - Integer, FLT - Decimal, PHN - Phone number, DVC - Application token)
is_required - Whether the field is required
is_uniquevalue - For ‘List of Values’ type fields, whether it’s unique (true) or multiple (false)
is_searchable - Field applicability

Dates:

insert_date - Registration date (replication key)

Field Groups

Stream for managing field groups that organize custom fields into logical categories.Identifiers:

code - Unique identifier for the field group (primary key)

Group Information:

title - Field group title

Dates:

insert_date - Registration date (replication key)

Filters

Stream for managing segmentation filters used to create dynamic contact lists and target specific contact groups.Identifiers:

code - Unique identifier for the filter (primary key)

Filter Information:

title - Filter title
type - Operator between rules (AND, OR)

Dates:

insert_date - Registration date (replication key)

List of Values

Stream for managing predefined value lists used in fields of type “List of Values”.Identifiers:

code - Unique identifier for the value in the list of values (primary key)

Value Information:

value - The value

Optout Emails

Stream for tracking email opt-outs and unsubscribes, including the reason and origin of the opt-out request.Identifiers:

email - Contact email (primary key)

Optout Information:

reason_description - Opt-out reason
origin - Opt-out origin (C - Manually included, P - Requested by email owner, D - Spam complaint)

Dates:

request_date - Opt-out request date (replication key)

Users

Stream for managing user accounts and user information.Identifiers:

code - Unique identifier for the user (primary key)

User Information:

email - User email
name - User name
language - Language
timezone - Timezone

Dates:

insert_date - Registration date (replication key)

Teams

Stream for managing teams and organizational hierarchy.Identifiers:

code - Unique identifier for the team (primary key)
top_team - Parent team code

Team Information:

name - Team name

Use Cases for Data Analysis

This guide outlines valuable business intelligence use cases when consolidating Dinamize data, along with ready-to-use SQL queries that you can run on Explorer.

1. Campaign Performance Overview

Analyze email campaign performance by tracking send statistics, delivery rates, and engagement metrics across all campaigns. Business Value:

Identify which campaigns have the highest delivery rates
Track send volumes over time
Monitor campaign status distribution
Analyze send patterns by campaign type

SQL query

SELECT
   a.campaign_code,
   a.title AS action_title,
   a.action_type,
   a.status,
   a.contact-list_title,
   CAST(a.total_contacts AS INTEGER) AS total_contacts,
   CAST(a.total_sent AS INTEGER) AS total_sent,
   CASE 
      WHEN CAST(a.total_contacts AS INTEGER) > 0 
      THEN CAST(a.total_sent AS INTEGER) * 100.0 / CAST(a.total_contacts AS INTEGER)
      ELSE 0 
   END AS send_rate,
   a.date_create,
   a.date_start,
   a.date_end
FROM
   nekt_raw.dinamize_actions a
WHERE
   a.date_create >= CURRENT_DATE - INTERVAL '30' DAY
ORDER BY
   a.date_create DESC

SELECT
   a.campaign_code,
   a.title AS action_title,
   a.action_type,
   a.status,
   a.contact-list_title,
   CAST(a.total_contacts AS INT64) AS total_contacts,
   CAST(a.total_sent AS INT64) AS total_sent,
   SAFE_DIVIDE(
      CAST(a.total_sent AS INT64),
      CAST(a.total_contacts AS INT64)
   ) * 100 AS send_rate,
   a.date_create,
   a.date_start,
   a.date_end
FROM
   `nekt_raw.dinamize_actions` a
WHERE
   DATE(a.date_create) >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
ORDER BY
   a.date_create DESC

Sample Result

campaign_code	action_title	action_type	status	contact-list_title	total_contacts	total_sent	send_rate	date_create	date_start	date_end
CAM001	Welcome Campaign	PT	FN	Newsletter Subscribers	1000	985	98.5	2024-11-15 10:00:00	2024-11-15 10:00:00	2024-11-15 12:00:00
CAM002	Monthly Newsletter	IC	EN	Customer Base	5000	3200	64.0	2024-11-20 08:00:00	2024-11-20 08:00:00	NULL

2. Contact List Analysis

Analyze contact list health, growth, and composition to understand your subscriber base. Business Value:

Track contact list growth over time
Monitor list status and health
Identify lists with high valid contact rates
Understand list ownership and distribution

SQL query

SELECT
   cl.code,
   cl.title,
   cl.type,
   cl.status,
   cl.user_code,
   CAST(cl.total_contacts AS INTEGER) AS total_contacts,
   CAST(cl.total_valid AS INTEGER) AS total_valid,
   CASE 
      WHEN CAST(cl.total_contacts AS INTEGER) > 0 
      THEN CAST(cl.total_valid AS INTEGER) * 100.0 / CAST(cl.total_contacts AS INTEGER)
      ELSE 0 
   END AS validity_rate,
   cl.insert_date,
   u.name AS owner_name
FROM
   nekt_raw.dinamize_contact_lists cl
   LEFT JOIN nekt_raw.dinamize_users u ON cl.user_code = u.code
WHERE
   cl.status = 'LI'
ORDER BY
   CAST(cl.total_contacts AS INTEGER) DESC

SELECT
   cl.code,
   cl.title,
   cl.type,
   cl.status,
   cl.user_code,
   CAST(cl.total_contacts AS INT64) AS total_contacts,
   CAST(cl.total_valid AS INT64) AS total_valid,
   SAFE_DIVIDE(
      CAST(cl.total_valid AS INT64),
      CAST(cl.total_contacts AS INT64)
   ) * 100 AS validity_rate,
   cl.insert_date,
   u.name AS owner_name
FROM
   `nekt_raw.dinamize_contact_lists` cl
   LEFT JOIN `nekt_raw.dinamize_users` u ON cl.user_code = u.code
WHERE
   cl.status = 'LI'
ORDER BY
   CAST(cl.total_contacts AS INT64) DESC

Sample Result

code	title	type	status	user_code	total_contacts	total_valid	validity_rate	insert_date	owner_name
LIST001	Newsletter Subscribers	EM	LI	USR001	5000	4850	97.0	2024-01-15 10:00:00	João Silva
LIST002	Customer Base	CE	LI	USR002	3200	3100	96.9	2024-02-20 14:30:00	Maria Santos

3. Transaction and Consumption Analysis

Analyze platform usage and consumption patterns to understand costs and usage trends. Business Value:

Track platform usage and consumption over time
Identify high-consumption periods
Monitor transaction values and trends
Correlate usage with campaign activity

SQL query

SELECT
   DATE(t.dt_transaction) AS transaction_date,
   COUNT(*) AS transaction_count,
   SUM(CAST(t.value AS DECIMAL(10,2))) AS total_value,
   AVG(CAST(t.value AS DECIMAL(10,2))) AS avg_value,
   COUNT(DISTINCT t.action_code) AS unique_actions
FROM
   nekt_raw.dinamize_transactions t
WHERE
   DATE(t.dt_transaction) >= CURRENT_DATE - INTERVAL '30' DAY
GROUP BY
   DATE(t.dt_transaction)
ORDER BY
   transaction_date DESC

SELECT
   DATE(t.dt_transaction) AS transaction_date,
   COUNT(*) AS transaction_count,
   SUM(CAST(t.value AS FLOAT64)) AS total_value,
   AVG(CAST(t.value AS FLOAT64)) AS avg_value,
   COUNT(DISTINCT t.action_code) AS unique_actions
FROM
   `nekt_raw.dinamize_transactions` t
WHERE
   DATE(t.dt_transaction) >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
GROUP BY
   DATE(t.dt_transaction)
ORDER BY
   transaction_date DESC

Sample Result

transaction_date	transaction_count	total_value	avg_value	unique_actions
2024-11-27	45	1250.50	27.79	12
2024-11-26	38	980.00	25.79	10
2024-11-25	52	1450.75	27.90	15

4. Opt-out Analysis

Track and analyze email opt-outs to understand subscriber churn and identify patterns in unsubscribes. Business Value:

Monitor opt-out trends over time
Identify common opt-out reasons
Track opt-out origins (manual, user request, spam complaint)
Correlate opt-outs with campaign sends

SQL query

SELECT
   DATE(o.request_date) AS optout_date,
   o.origin,
   o.reason_description,
   COUNT(*) AS optout_count,
   COUNT(DISTINCT o.email) AS unique_emails
FROM
   nekt_raw.dinamize_optout_emails o
WHERE
   DATE(o.request_date) >= CURRENT_DATE - INTERVAL '30' DAY
GROUP BY
   DATE(o.request_date),
   o.origin,
   o.reason_description
ORDER BY
   optout_date DESC,
   optout_count DESC

SELECT
   DATE(o.request_date) AS optout_date,
   o.origin,
   o.reason_description,
   COUNT(*) AS optout_count,
   COUNT(DISTINCT o.email) AS unique_emails
FROM
   `nekt_raw.dinamize_optout_emails` o
WHERE
   DATE(o.request_date) >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
GROUP BY
   DATE(o.request_date),
   o.origin,
   o.reason_description
ORDER BY
   optout_date DESC,
   optout_count DESC

Implementation Notes

Data Quality Considerations

Most streams use incremental replication based on date fields (insert_date, date_create, dt_transaction, request_date)
The start_date configuration parameter can be used to limit the initial sync window for streams with replication keys
Some fields like total_contacts, total_sent, and value are stored as strings and may need casting to numeric types in queries
The custom_fields field in contacts is an object type and may contain dynamic fields (cmp4, cmp5, cmp6, etc.)
Contact lists and contacts support a rest_date field that prevents emails from being sent until the end of the specified day

API Limits & Performance

The API uses POST requests with pagination
Rate limiting is handled automatically with retry logic (error code 240024)
For faster extractions, select only the streams necessary for your analysis
Streams with replication keys (incremental sync) will only fetch new records since the last sync or the configured start date
The teams and list_of_values streams are full table syncs (no replication key)

Introduction

Get started

Using Nekt

Workspace and access

MCP

SDK

Dinamize as a data source

Configuring Dinamize as a Source

1. Add account access

2. Select streams

3. Configure data streams

4. Configure data source

5. Check your new source

Streams and Fields

Use Cases for Data Analysis

1. Campaign Performance Overview

2. Contact List Analysis

3. Transaction and Consumption Analysis

4. Opt-out Analysis

Implementation Notes

Data Quality Considerations

API Limits & Performance

Introduction

Get started

Using Nekt

Workspace and access

MCP

SDK

​Configuring Dinamize as a Source

​1. Add account access

​2. Select streams

​3. Configure data streams

​4. Configure data source

​5. Check your new source

​Streams and Fields

​Use Cases for Data Analysis

​1. Campaign Performance Overview

​2. Contact List Analysis

​3. Transaction and Consumption Analysis

​4. Opt-out Analysis

​Implementation Notes

​Data Quality Considerations

​API Limits & Performance

Configuring Dinamize as a Source

1. Add account access

2. Select streams

3. Configure data streams

4. Configure data source

5. Check your new source

Streams and Fields

Use Cases for Data Analysis

1. Campaign Performance Overview

2. Contact List Analysis

3. Transaction and Consumption Analysis

4. Opt-out Analysis

Implementation Notes

Data Quality Considerations

API Limits & Performance