**There is some debate about the effectiveness and future potential of Customer Data Platforms (CDPs). One aspect is the increasing competition from cloud data (warehouse) platforms. Overlap seems to be growing as it became much easier to build (some) of the functionality that existing CDPs are offering. Referencing this approach, a term I hear more often is the *Headless* (Customer) Data Platform. What is this *headless* approach all about and how does it compare to existing, stand-alone tools and suites?**

The Headless Customer Data Platform (CDP) or Data Management Platform (DMP) refers to an increasingly popular solution (as there is no exact definition and probably never will be): 

- A cloud data warehouse acts as the foundation (think of GCP / AWS / Azure). Different data sources are ingested, transformed and joined. 
- Create insights and actions on all kinds of levels (**not limited to customer data**). Think of product or campaign level data.
- Activate data in channels, platforms and channels **directly from the data warehouse environment**
- Without a stand-alone CDP tool or suite, but a combination of cloud (data) services and specialized tools or frameworks.

## Headless?

*Headless* architecture gained popularity in the Content Management (CMS) world and is widely used these days. The concept: decoupling the back-end (content management) and the front-end (website). The CMS will act as a central content hub. Through an API, content can be shared with all sorts of applications, such as websites, apps or even dynamically generated emails . The front-end is no longer intertwined with the content management system. The result: all content centralized in one system and front-end applications can be developed independently from the CMS.

The headless CDP or DMP acts as a **(marketing) data hub** where insights and actions can be created on all kinds of levels **and** are activated / integrated with channels, tools or platforms (think of audiences, events or triggers). This is done directly from the cloud data warehouse, without the need for a stand-alone CDP tool or suite. However, there is no centralized user interface. The solution consists of a combination of services within the cloud environment (building blocks) and other (specialized) tools that fulfill a specific need (e.g. connectivity or decision making). 

![headless-cdp-architecture_white.PNG](https://static.stacktonic.com/medium_headless_cdp_architecture_white_cb9e8e79bc.PNG)


## Not limited to customer data only

I'm comparing the headless approach to existing stand-alone CDP solutions, which are mainly build around customer data. However, a headless data management system is not limited to customer data only. Some examples:

- Identity management, building a single (360°) customer view and (customer) audiences.
- Enriching product data / product feeds. Gaining insights into product performance.
- Create models, such as product recommenders, purchase of churn prediction models or forecasting sales (both in batch as real-time).
- Creating business rules or “triggers” (decisioning) for follow-up / next-best actions.
- Sales and engagement reports, based on multiple data sources. Creating notifications and alerts based on deviations within these reports.

The headless approach has a lot of overlap with the idea behind [The Modern Datastack](https://snowplowanalytics.com/blog/2021/05/12/modern-data-stack/). However, I will try to make the comparison with existing CDP tools, so limiting myself to functionalities these platforms generally offer.

## A data warehouse 'with benefits'

Many organizations recently invested in a data warehouse in one of the well-known cloud providers such as AWS, Azure or GCP. Data can be retrieved from various sources and joined together. Usually this is based on periodic batches (once per day or hour), but real-time data ingestion is also perfectly possible (due to the rise of real-time architecture). Think of (live) orders or clickstream data from websites / apps.

Working with such a data warehouse and all the different services within these platforms became much easier. Services within the cloud platforms can be stacked on top of each other (**building blocks**), making it easier to import, transform and analyze large amounts of data. Skills that are important for working with such a data environment, such as SQL, R or Python, are also increasingly present within organizations (but yes, in most cases not nearly enough).

*Activating* this data in channels and/or tools directly from the data warehouse is what increases the overlap with existing CDPs, thus becoming an alternative. 

## The headless approach vs. CDP tools: overlap in functionality

There are many CDP 'flavors'. For example, one platform is focusing more on activation, personalization and AI. The other on creating insights, identity resolution and a 360° customer view. However, almost all of the systems require you to to collect and ingest source data. Often (Javascript) trackers have to be implemented on-site and (mostly) CSVs uploaded to join online data with order or CRM data. 

Lately, when talking about a CDP when a cloud data warehouse environment is already present (or will be), generally the following comes up:

1. The data sources that already flow into your data warehouse, also have to be integrated with the CDP.
2. Capturing and collecting real-time (on-site) behavioral data most of the times is already done by systems such as Google Analytics or Snowplow (and more recently leveraging *server-side* tag management solutions). So why is it necessary to create another online behavioral database (in which measurement methodology certainly will differ)?
3. Duplicating business rules already present in your warehouse environment. Think of identity resolution, creation of a 360 customer view or audience selections.

If the above applies to your organization depends on your specific situation, but in most cases there will be overlap. And most CDP and data management tools do not (yet) work together in such a way that they can "piggyback" on everything that is already collected and calculated in your data warehouse.

## Disadvantages of a headless CDP

Of course, stand-alone CDPs have their advantages. Mainly this is time and cost. First off all if you don't have a data warehouse and/or resources are limited, a stand-alone CDP suite could fit your needs and get you started quickly, seen the out-of-the box functionality. The cost factor heavily depends type of system, volume and integrations. When zooming in, the headless CDP has the following disadvantages: 

1. No central user interface & ease of use: a headless system does not have an integrated user interface where settings can be easily adjusted or insight can be given into all processes. It is certainly possible to gain insight into the processes of a headless system, but those insights have to be created yourself or is available in multiple systems. So there is some technical / SQL knowledge required to set up audiences or journeys for example.
2. (Real-time) decision engine / flow builder: making decisions based on real-time data flows (e.g. web or app data). Creating such a real-time decision engine (e.g. online personalization use cases) within the cloud is possible, but is much more complex than clicking some rules together in a user interface. Also, creating flows / journeys is easily done within most CDP solutions.
3. Connectivity to channels and tools is maintained by the platform. In a headless CDP, you have to build the integrations yourself (mostly trough API connections), although there are currently several (open-source) frameworks available, such as [Google Tentacles](https://stacktonic.com/article/build-your-own-cdp-activating-audiences-in-google-ads-customer-match-using-google-tentacles). Interesting development is the rise of "reverse ETL" tools.

## Reverse ETL

This last one deserves some some explanation. There are already a lot of tools that ingest data *into* your warehouse, but less tools that can activate that data to your marketing channels or other platforms. *Reverse ETL* tries to solve this problem. Ultimately, a tool like this should enable you to focus on building use cases and not to worry about connector development and maintenance (e.g. the Facebook API). Next to that, the tools is providing insights in the process and data that flows out of your cloud platform. It also seems that some of these tools are developing a user interface where non-technical users can create audiences or flow on top of your data warehouse data.

*An example setup*

![headless-cdp-architecture_white_example_setup.PNG](https://static.stacktonic.com/medium_headless_cdp_architecture_white_example_setup_0c4b10c778.PNG)

## When to choose for a headless approach?

When your organization already has a cloud data warehouse or planning to set one up, the headless approach can be interesting if you:

1. Don't want to purchase and implement an (expensive) CDP tool, for example if you just want to share audiences, triggers or other data directly from the data warehouse with your (marketing) channels or (external) platforms;
2. Want to realize advanced and more complex use cases based on different sources and combinations, which are not possible or hard to achieve in stand-alone tools (by clicking stuff together) or without moving the data out and in the CDP platform to perform the necessary actions.
3. Do not want to duplicate data and recreate business rules in different environments (single source of thruth).
4. Unlock the potential and activate non-customer data, such as product or campaign data.

Using various cloud services and dedicated / specialized tools (such as reverse ETL), it's possible to connect data and tools relatively easily. Keep in mind that certain skills must be present in the team that will work with the headless CDP on a daily basis and at scale. Think of data engineers, tech-savvy marketers and analysts. This is especially important as use cases become more complex. 

## Start with your use cases

But most importantly, when choosing between a headless or a stand-alone CDP (or whatever solution), start with **defining clear use cases** and determine the roles / persons that will work with such a system. *“The ability to create audiences in a drag-and-drop interface and share these with our marketing channels”* is not a solid use case. Creating 100 audiences, in which the same customer is in 80 of these 100 audiences, and is targeted 80 times with a different message, does not work. Try to work out the details.

Insight and ease of use are certainly advantages of a stand-alone CDP, although the promise that every marketer can work with a CDP is often not fulfilled. Whether that depends on the marketer or the tool will differ per situation and person. The fact is that in reality, the more technical marketers and/or data engineers become the main users of such systems when the "low-hanging-fruit" use cases are live and CDP needs to be build out.

So, the best tip I can give is to involve the right people, work out clear requirements, detailed use cases and then pick a solution.

There is some debate about the effectiveness and future potential of Customer Data Platforms (CDPs). One aspect is the increasing competition from cloud data (warehouse) platforms. Overlap seems to be growing as it became much easier to build (some) of the functionality that existing CDPs are offering. Referencing this approach, a term I hear more often is the *Headless* (Customer) Data Platform. What is this *headless* approach all about and how does it compare to existing, stand-alone tools and suites?

large_headless-cdp-architecture.PNG

medium_headless-cdp-architecture.PNG

small_headless-cdp-architecture.PNG

thumbnail_headless-cdp-architecture.PNG

headless-cdp-architecture.PNG

The Future of CDPs – The Headless approach

Learn how to serve user level data from BigQuery to your website or app, using Redis (low-latency), Cloud Functions and Google Tag Manager Server. The user-level profile data can be used for on-site personalization or data enrichments within GTM Server.

medium_gtm-server-profile-store.PNG

small_gtm-server-profile-store.PNG

thumbnail_gtm-server-profile-store.PNG

gtm-server-profile-store.PNG

Create a User Profile and Recommender service using BigQuery, Redis and GTM Server

**In this tutorial we will show you how to activate audiences in Google Ads Customer Match lists using the Google Tentacles framework. The framework can be used activate your (first-party) data in marketing and advertising platforms. Basically building a headless Customer Data Platform (CDP) within an (existing) cloud / data environment without an expensive CDP tool or suite**

- Automate the audience push to Google Ads (Customer Match lists), based on (hashed) email address or other available identifiers.
- Google Tentacles runs "serverless" within the Google Cloud Platform and starts running when a new file is uploaded in a specific Storage bucket.
- These files are new line delimited JSONs, containing the create and remove to audience operations. These files can be generated by your data warehouse (e.g. Google BigQuery or Dataflow).
- Can be used for a lot of common use cases (pushing audiences or conversion events to external platforms).
- The framework itself is focused on Google products (what a suprise!), but connectors for non-Google products can be developed easily (e.g. Facebook Conversion API or Custom Audience connector).

## What is Tentacles?

[Tentacles](https://github.com/GoogleCloudPlatform/cloud-for-marketing/blob/main/marketing-analytics/activation/gmp-googleads-connector/README.md) is an (unofficial) Google framework, operating **serverless** using different services within the Google Cloud Platform, like Cloud Functions & PubSub. 
The **integration framework** is able to handle large amounts of data to be send to other, mostly marketing related Google products (like Google Analytics data upload, Google Campaign conversions or Google Ads Custom Audiences). 
Next to all the Google related connectors, the framework is handling tasks like `queueing`, `batching`, `rate-limits` and `(error) logging` and therefore can be used perfectly for building *custom connectors*, like a Facebook Custom Audience connector we've build.

More background information on the framework [in this article](https://cloud.google.com/architecture/serverless-integration-solution-for-google-marketing-platform).

![google-tentacles-serverless-framework-headless-cdp.png](https://static.stacktonic.com/google_tentacles_serverless_framework_headless_cdp_1ed8fd795c.png)

*Architectural layout of the serverless integration framework. Source: [Google](https://cloud.google.com/architecture/serverless-integration-solution-for-google-marketing-platform)*

## 1. Preparation

If not present already, create a Google Cloud project ([instructions](https://cloud.google.com/resource-manager/docs/creating-managing-projects)) where you can run Tentacles. Tentacles provides you with a bash install script, that will check and activate all services / APIs needed. It assumes you're an project administrator, so if it's not possible to grant administrator rights, at least you need the following permissions / roles;

- Service Account User
- Cloud Functions Developer
- Pub/Sub Editor
- Storage Admin
- Service Usage Admin
- Cloud Datastore User
- Service Management Administrator

The following services / APIs are used by Tentacles, if not activated already, the install script will enable it for you;

> In new GCP projects, you have to select a Datastore / Firestore operation mode. For Tentacles, **choose the Firestore Native mode**. You can't change this setting later on, so make sure you set it to Firestore Native mode.

- **Google Ads API** (you have to enable this manually in your project)
- Google Cloud Storage
- Firestore (Native mode)
- Google Cloud Functions 
- Google Pub/Sub

**Create an OAuth Client**

1. In GCP navigate to Google APIs & Services > Credentials
2. If not present already, configure the consent screen and fill out the required fields.
3. Create new OAuth 2.0 Client credentials. When creating, select the **Desktop app**
Create )Desktop app
4. Write down the `Client-ID` and `Client-Secret`, you will need it during the installation.

** Google Ads API basic access**

In Google Ads, **create an API developer token** and apply for API basic access. This will take some days. [See documention](https://developers.google.com/google-ads/api/docs/access-levels)

## 2. Installation / deployment

Let's move on the deployment of Tentacles.

1. In your Google Cloud project, open up the Cloud shell (top right).

2. Clone the repository to your Cloud shell instance;

```bash
git clone https://github.com/GoogleCloudPlatform/cloud-for-marketing.git
```

3. Run the install script and follow the instructions.

```bash
cd cloud-for-marketing/marketing-analytics/activation/gmp-googleads-connector
bash deploy.sh
```

- The namespace chosen will be the prefix of all services created by Tentacles
- When asked for the APIs (connectors) to install, choose "Google Ads API for Customer Match Upload" (option 5).
- Enter the Client-ID and Cliend-Secret you've created in the earlier steps, this will generate a verification link that results in a response code you need to paste back into the install script.

The install script will enable all the services if not already, creating the Pub/Sub topics, Cloud Functions, Cloud bucket and triggers. Next step is to configure the Google Ads Customer Match connector.

## 3. Configure the Google Ads connector

The Google Ads connector uses the Google Ads API (not the old Google Adwords API). It expects;

- Two separate JSON files per audience, co (per audience),  with one of the available customer identifiers, like (hashed) email or telephone. See [this page](https://developers.google.com/google-ads/api/reference/rpc/v7/UserIdentifier) for all available identifiers.
- One file containing the `create` (add) to audience operations
- One file containining the `remove` from audience operations

> Be aware that you're uploading customer data to Google, so make sure you have the correct consent / permissions from those users.

**Connector configuration**

Every Google Ads audience needs to be defined in the `config_api.json` file. For the same audience you have to create 2 configurations, one for `create` operators and one for `remove` operators. 

> As I'm told, this is because Tentacles mimics the behavior of the Google Ads API. The Google Ads API separates create and delete operations. Next to that, using multiple identifiers (like hashed email and phone) also results in additional configurations, because the Google Ads API also separates these operations.

| Setting | Description |
|---|---|
| developerToken | Google Ads developer token. Make sure you have Basic Access to the Google Ads API |
| customer_id | Google Ads account-ID where you want the audiences to be created |
| login_customer_id | Google Ads MCC account-ID, when accessing the account through an MCC. |
| list_id| Google Ads Customer Match list ID > You will have to create the audience first in the interface and fetch the ID. Hopefully, automatic audience creation will be added in the future  |
| list_type | Type of identifiers you wish to upload to this audience, for example `hashed_email` or `hashed_phone_number`. See [this page](https://developers.google.com/google-ads/api/reference/rpc/v7/UserIdentifier). |
|operation| `create` or `remove`, so you have to create two separate configurations for the same audience |


**Example `config_api.json` file. **

*ACM: Ads Customer Match configurations (the config_api.json can also contain configurations for the other connectors*

``` json
{
  "ACM": {
    "audience_1_add": {
        "developerToken": "<GOOGLE-ADS-API-DEVELOPER TOKEN>",
        "customerMatchConfig": {
            "customer_id": "<ADS-CUSTOMER-ID>",
            "login_customer_id": "<ADS-MCC-CUSTOMER-ID>",
            "list_id": "<CUSTOM-AUDIENCE-ID>",
            "list_type": "hashed_email",
            "operation": "create"
        }
    },
    "audience_1_remove": {
        "developerToken": "<GOOGLE-ADS-API-DEVELOPER TOKEN>",
        "customerMatchConfig": {
            "customer_id": "<ADS-CUSTOMER-ID>",
            "login_customer_id": "<ADS-MCC-CUSTOMER-ID>",
            "list_id": "<CUSTOM-AUDIENCE-ID>",
            "list_type": "hashed_email",
            "operation": "remove"
        }
    }
  }
}
```

1. Go back to the Google Cloud shell. 
2. You can use the file editor (Open Editor button), navigate to `cloud-for-marketing/marketing-analytics/activation/gmp-googleads-connector` 
3. Open `config_api.json` and edit the contents / paste your Google Ads Custom Audience configuration.

Then, run the following command to upload or update the config into Tentacles (the bash script will load or update the configuration into the Firestore database).

```
bash deploy.sh update_api_config
```

> The install script doesn't remove entries  in the Firestore database, only adds or updates.

Now, Tentacles is all set to receive the JSON files containing the create and remove operations for Google Ads audiences.

## 4. Automate the Google Ads import files

The last step is to automate the generation of the newline delimited JSON files. The naming of these files should like like `API[ACM]_config[audience_1_add]_<YYYYMMDD>.json`, where the connector key (ACM) and config key (audience_1_add) should match the entries in `config_api.json`

The content of the newline delimited JSON files should look like;

``` json
{ "hashed_email": "f660ab912ec121d1b1e928a0bb4bc61b15f5ad44d5efdc4e1c92a25e99b8e44a" }
{ "hashed_email": "bceede42d53175d644efb79514d3967d3f9b92eb810b2c30bcddfae571b8b377" }
{ "hashed_email": "8d1dc4e440a9965c3388c8714d3dd25ce99d855fd6828396f0fab07f57fba820" }

```

Most likely, you will have to execute the following tasks;

- Create a selection (audience) based on available data in your data warehouse (e.g. "all high-value customers")
- Calculate delta's / incremental tables, containing the `create` and `remove` audience operations
- When using BigQuery, you could create **date sharded** (partition) tables (audience_1_YYYMMMDD), holding all the the **SHA256** hashed emails of that date for the specific audience. 
- Based on these date sharded tables, you can calculate the differences between the most recent audience state and audience state of the previous date. 
- With these differences/deltas you can **create two intermediate tables**, one containing the **SHA256** hashed emails for the `create` operations (new users in audience), the other containing the `delete` operations (users need to be deleted).
- Export the 2 intermediate tables to JSON files (newline delimited), using the correct filename > `API[ACM]_config[audience_1_add]_<YYYYMMDD>.json` (can be exported directly from BigQuery)
- Move file to the Google Cloud bucket where the Tentacles Cloud Function is listening to (in the `/outbound` folder)

You could use the following SQL to calculate differences based on two date sharded tables (current date vs. previous date):

``` sql
#standardSQL
SELECT
  operator AS operator,
  JSON_EXTRACT_SCALAR(record, '$.hashed_email') AS hashed_email
FROM (
  -- Get new / adjusted records compared to previous table -> CREATE operations
  SELECT 
    *,
    'create' AS operator, 
  FROM (
    SELECT TO_JSON_STRING(a) AS record FROM `{table_name}_*` a WHERE _TABLE_SUFFIX = '{current_date}' -- New table
    EXCEPT DISTINCT
    SELECT TO_JSON_STRING(b) AS record FROM `{table_name}_*` b WHERE _TABLE_SUFFIX = '{prev_date}' -- Old table
  )
  UNION ALL
  -- Get missing records in current table, compared to previous table -> REMOVE operations
  SELECT
    *,
    'remove' AS operator, 
  FROM (
    SELECT TO_JSON_STRING(b) AS record FROM `{table_name}_*` b WHERE _TABLE_SUFFIX = '{prev_date}' -- Old table
    EXCEPT DISTINCT
    SELECT TO_JSON_STRING(a) AS record FROM `{table_name}_*` a WHERE _TABLE_SUFFIX = '{current_date}' -- New table
  )
)
```

There are a lot of solutions possible automating these tasks, but you could use one of the following setups;

1. **Apache Airflow**

Apache Airflow has all the connectors available to automate these tasks (create audience tables, create intermediate / delta tables) and export it in JSON format to the Tentacles Cloud bucket. Using Apache Airflow, you can create a scalable solution that can handle a lot of audiences.

2. **Scheduled Queries + Cloud Scheduler + Cloud Functions**

- Use BigQuery Scheduled Queries to generate the daily full audience tables and the delta tables (see example SQL above).
- Use Cloud Functions to run a function that starts the BigQuery export job to a JSON file in the Tentacles bucket. See [this code](https://cloud.google.com/bigquery/docs/samples/bigquery-extract-table-json).
- Let Cloud Scheduler run the Cloud Function after the scheduled queries ran.

## Summary

Tentacles will enable you to activate your data directly out of your cloud environment pretty easily. It could save some money compared to expensive CDP tools / suites, but keep in mind

- It doesn't provide you with a nice user interface
- Only has some basic (error) logging
- Heavily Google focused, but customer connectors can added / developed easily

Some last tips;

- Google Ads Connector; when you using multiple identifiers (like email and telephone), you need to split up files according to the number of different identifiers used + add/ remove operations. Resulting in 4 files per audience when using hashed_email and hash_phone_number for example. Not that efficient, but you can adjust the Google Ads connector (nodeJs) without much effort, for example combining create and add operations in 1 file.
- You can debug Tentacles, using the logs provided in the Google Cloud Functions interface or the Cloud Logging / Stackdriver interface.

## Useful links & documentation

- [Tentacles GitHub repository](https://github.com/GoogleCloudPlatform/cloud-for-marketing/blob/main/marketing-analytics/activation/gmp-googleads-connector/README.md)
- [Tentacles overview & architecture by Google](https://cloud.google.com/architecture/serverless-integration-solution-for-google-marketing-platform)
- [Tentacles tutorial by Google](https://cloud.google.com/architecture/deploying-a-serverless-integration-solution-cloud-functions-cloud-pubsub)


In this tutorial we will show you how to activate audiences in Google Ads Customer Match lists, using the Google Tentacles framework. It will enable you to automate the audiences push to Customer Match lists, based on (hashed) email address or other available identifiers.

medium_tentacles.jpg

small_tentacles.jpg

thumbnail_tentacles.jpg

tentacles.jpg

Build your own CDP: Activating audiences in Google Ads Customer Match using Google Tentacles

**Use (real-time) triggers like (sports) events or weather conditions to automate your display and/or video campaigns in Google Display & Video 360 (DV360). This tutorial will show you how to automate these operations using Python and the DV360 API.**

- **Activate** and **pause** DV360 **line items**, making use of the DV360 API and Python
- Activate **manual triggers** to quickly activate a line item, that will automatically pause after a configurable amount of time.
- The script can be scheduled using a serverless function like AWS Lamba / Google Cloud Functions or a tool like Apache Airflow.
- Go directly [to the code](#changing-the-line-item-status)

## Practical use cases & business value

The new DV360 API (mid-2020) unlocked some interesting new features and actions became much more simpler to automate. 

![google-display-video-360-enable-line-item.PNG](https://static.stacktonic.com/google_display_video_360_enable_line_item_324ff943bd.PNG)

To give a few practical examples, using this functionality;

- (De)activate line items based on **weather conditions**
- (De)activate line items based on **events** like company / national events or sports matches (e.g. campaigns based on match outcomes)
- (De)activate line items based on **sales or stock levels**

Another interesting, related, feature are the "[manual triggers](https://support.google.com/displayvideo/answer/9265340?hl=en)"; a line-item can be activated quickly and it will automatically deactivate after a desired amount of time has passed (so no need to deactivate the line item trough the API or interface). 

Think of;

- Activate line items when an event has started (and automatically deactivate after X time)
- Activate line items when a commercial has aired (and automatically deactivate after X time)

Line items with manual triggers will also work using our code example (two use cases provided), but you have to attach a manual trigger on the line item within the [DV360 interface](https://support.google.com/displayvideo/answer/9265340?hl=en) first.

## Preparation & configuration

Before running the script, a few things need to be configured:

- [Create a Google Cloud project](https://cloud.google.com/resource-manager/docs/creating-managing-projects) when you don't have one already
- Activate the Google Display & Video 360 API within the Google Cloud Console

![google-display-video-360-enable-api.PNG](https://static.stacktonic.com/google_display_video_360_enable_api_272a0c8370.PNG)
- [Create a service account](https://cloud.google.com/iam/docs/creating-managing-service-accounts) in the Google Cloud console to programmatically authenticate with the different Google services (without using a user account)
- Add the service account to your Google DV360 account and grant access rights to the correct advertiser accounts (I've used the `"Standard" role` but probably can be configured more accurate).

## Changing the line item status

The Python script below will;
- Authenticate using a service account (change GOOGLE_JSON_KEYFILE to the location of your `keyfile.json`)
- Update a line-item based on `advertiser-id`, `line-item-id` and desired `line-item status`.
- Or activate a manual trigger based on `advertiser-id` and `trigger-id` (can be obtained in the DV360 interface when a trigger is created)

> Keep your JSON key file secure!

*View code [on GitHub](https://gist.github.com/krisjan-oldekamp/044945ef754a766bd7dd7390ac41e3cb)*

``` python
###################################################
# Author    Krisjan Oldekamp / Stacktonic.com
# Email     krisjan@stacktonic.com
# Article   https://stacktonic.com/article/how-to-activate-and-pause-line-items-in-google-dv-360-using-python
####################################################

import os
from urllib.error import HTTPError
from googleapiclient import discovery
from oauth2client.service_account import ServiceAccountCredentials

####################################################
# Settings
####################################################

GOOGLE_JSON_KEYFILE = "<your-keyfile>.json" # Google Cloud Platform Service Account JSON keyfile
GOOGLE_DV360_API_VERSION = "v1"
GOOGLE_DV360_API_SCOPES = ["https://www.googleapis.com/auth/display-video"]

DV360_ADVERTISER_ID = "<your-dv360-advertiser-id>"

####################################################

# Google D&V360 API service
def get_dv360_service():
    credentials = ServiceAccountCredentials.from_json_keyfile_name(
        GOOGLE_JSON_KEYFILE,
        scopes=GOOGLE_DV360_API_SCOPES)

    return discovery.build('displayvideo', GOOGLE_DV360_API_VERSION, credentials=credentials, cache_discovery=False)

# Change DV360 Line Item status
def update_lineitem_status(advertiser_id, line_item_id, status_new):

    service = get_dv360_service()

    status_dv360 = 'ENTITY_STATUS_ACTIVE' if status_new == 'ACTIVE' else 'ENTITY_STATUS_PAUSED'

    line_item_obj = {
        'entityStatus': status_dv360,
    }

    # Update the line item.
    update_line_item = service.advertisers().lineItems().patch(
        advertiserId=advertiser_id,
        lineItemId=line_item_id,
        updateMask="entityStatus",
        body=line_item_obj
    ).execute()

    print("Lineitem " + line_item_id + " updated to " + status_dv360)

# Activate a manual trigger
def manual_trigger(advertiser_id, trigger_id):

    service = get_dv360_service()

    # Activate the trigger
    update_line_item = service.advertisers().manualTriggers().activate(
        advertiserId=advertiser_id,
        triggerId=trigger_id
    ).execute()

    print("TriggerId " + trigger_id + " activated.")


# Usecase 1: Update lineitem status

line_item_id = "123" # DV360 Line-Item-ID
line_item_status_new = "ACTIVE" # New status -> ACTIVE or PAUSED

update_lineitem_status(DV360_ADVERTISER_ID, line_item_id, line_item_status_new)

# Usecase 2: Activate a manual trigger (create and attach a manual trigger in the DV360 interface first)

trigger_id = "123"

manual_trigger(DV360_ADVERTISER_ID, trigger_id)
```

## How to automate

There are endless ways to automate the script. The methods I've used;

- Wrapping the script in an Apache Airflow DAG
- Using a serverless function, like AWS Lambda or Google Cloud Functions. In our case, the Cloud function was triggered using Cloud Scheduler and the function fetched line-items and activation status from a Google Sheet.

Use (real-time) triggers like (sports) events or weather conditions to automate your display and/or video campaigns in Google Display & Video 360 (DV360). This tutorial will show you how to automate these operations using Python and the DV360 API.

large_traffic-light.jpg

medium_traffic-light.jpg

small_traffic-light.jpg

thumbnail_traffic-light.jpg

traffic-light.jpg

How to activate and pause line items in Google DV360 using Python

Cloud (data) platforms such as AWS, Google Cloud or Azure enable you to build a marketing data platform that doesn't only ingest and join data, but can also produce actionable insights and activate data in other tools and platforms. 

Building a cloud Marketing Data Platform

Google Analytics 4 is more than just a measurement and reporting tool. The event data exports to Google BigQuery (available in the free version) and the GA4 API brings a ton of new possibilities. Think of advanced analysis, joining data with other sources, building prediction models or integrate with other systems.

Google Analytics 4 Advanced

Increased privacy concerns, legislation and browser updates affecting (third-) party cookies is resulting in a new era where users regain control over their data and organizations should rethink their customer and marketing strategy. This will impact a lot of area's such as data collection, advertising, first-party data strategy, legal and UX. 