Automating Ad Campaigns with Open AI and Python-Django

Automating Ad Campaigns with Open AI and Python-Django


  1. Introduction

    • Purpose of the blog post

    • Importance of integrating OpenAI API into Ad Tech products

    • Brief overview of what will be covered

  2. Understanding the Integration of OpenAI API in Ad Tech

    • Overview of OpenAI API

    • Benefits of using OpenAI for ad campaigns

    • Case studies and examples of successful integrations

  3. Streamlining and Automating Ad Campaigns

    • How OpenAI API can help streamline ad campaigns

    • Automating ad creation and optimization

    • Ensuring character limit compliance for Google and Facebook

  4. Sample Best Performing Ad Titles and Descriptions in Different Tones

    • List of tones: Persuasive, Witty, Professional, Friendly, Urgent

    • Examples of ad titles and descriptions for each tone

  5. Technical Implementation: Using Python, Django, DRF, and PostgreSQL

    • Setting up the development environment

    • Creating the Django project and app

    • Configuring Django REST Framework (DRF)

    • Setting up PostgreSQL

  6. Fetching Best Performing Ad Campaigns

    • Integrating with Google BigQuery, Google Ads, and Facebook Ads

    • Creating an API to fetch ad data

    • Storing and managing ad data in PostgreSQL

  7. Using OpenAI GPT-3.5 Turbo for Ad Content Generation

    • Creating a prompt to fetch ad titles and descriptions

    • Integrating OpenAI API with the Django app

    • Parsing the OpenAI API response in Python

  8. Building the API to Return Ad Content

    • Designing the API endpoint

    • Implementing the API to fetch and return ad content

    • Testing the API

  9. Conclusion

    • Recap of the integration process

    • Benefits of using OpenAI in ad tech

    • Future possibilities and enhancements

  10. Appendix

    • Resources and references

1. Introduction

Integrating advanced AI technologies into ad tech products is revolutionizing how we approach digital marketing. By leveraging the power of OpenAI's API, businesses can streamline, automate, and enhance their ad campaigns across platforms like Google and Facebook. This blog post will guide you through a detailed tutorial on how to integrate OpenAI API into your ad tech product, ensuring your ads are compelling, effective, and within the character limits of each platform.

2. Understanding the Integration of OpenAI API in Ad Tech

OpenAI's API offers robust natural language processing capabilities that can generate high-quality text content. Integrating this API into ad tech products can significantly enhance ad copy creation, ensuring it is tailored to the target audience and optimized for performance. This integration not only saves time but also improves the effectiveness of ad campaigns by automating repetitive tasks and providing data-driven insights.

Benefits of using OpenAI API:

  • Automation: Automatically generate ad copy that adheres to platform guidelines.

  • Optimization: Continuously refine ad content based on performance metrics.

  • Personalization: Create personalized ad experiences at scale.

3. Streamlining and Automating Ad Campaigns

OpenAI's API can help streamline ad campaigns by automating the generation of ad titles and descriptions. It ensures that the content complies with the character limits for platforms like Google and Facebook, making the ad creation process more efficient and less error-prone.

Key advantages include:

  • Time Efficiency: Reduce the time spent on creating and revising ad copy.

  • Consistency: Maintain a consistent tone and style across all ads.

  • Scalability: Easily scale ad creation to match campaign needs.

4. Sample Best Performing Ad Titles and Descriptions in Different Tones

To demonstrate the power of OpenAI, here are examples of ad titles and descriptions tailored to different tones:


  • Title: "Unlock Your Potential with Our Premium Courses"

  • Description: "Join thousands of learners who have transformed their careers. Enroll now and get a 20% discount!"


  • Title: "Boost Your Brainpower – No Magic Wand Needed!"

  • Description: "Our courses are spellbindingly good! Enroll today and start your magical learning journey."


  • Title: "Advance Your Career with Expert-Led Courses"

  • Description: "Gain in-demand skills from industry leaders. Enroll now to elevate your professional profile."


  • Title: "Learn Something New Today – We've Got Your Back!"

  • Description: "Join our community of learners and enjoy fun, interactive courses. Sign up now for a special offer!"


  • Title: "Last Chance! Enroll Now and Save Big"

  • Description: "Don't miss out on our limited-time discount. Sign up today and start learning immediately!"

5. Technical Implementation: Using Python, Django, DRF, and PostgreSQL

Setting up the Development Environment:

  1. Install Python and virtualenv:

     sudo apt update
     sudo apt install python3 python3-venv python3-pip
  2. Create and activate a virtual environment:

     python3 -m venv myenv
     source myenv/bin/activate
  3. Install Django and Django REST Framework:

     pip install django djangorestframework psycopg2-binary
  4. Create a new Django project and app:

     django-admin startproject adtech_project
     cd adtech_project
     django-admin startapp ads

Configuring Django REST Framework (DRF): Add rest_framework to your INSTALLED_APPS in


Setting up PostgreSQL:

  1. Install PostgreSQL:

     sudo apt install postgresql postgresql-contrib
  2. Create a database and user:

     CREATE DATABASE adtech_db;
     CREATE USER adtech_user WITH PASSWORD 'yourpassword';
     ALTER ROLE adtech_user SET client_encoding TO 'utf8';
     ALTER ROLE adtech_user SET default_transaction_isolation TO 'read committed';
     ALTER ROLE adtech_user SET timezone TO 'UTC';
     GRANT ALL PRIVILEGES ON DATABASE adtech_db TO adtech_user;
  3. Configure Django to use PostgreSQL: In, update the DATABASES setting:

     DATABASES = {
         'default': {
             'ENGINE': 'django.db.backends.postgresql',
             'NAME': 'adtech_db',
             'USER': 'adtech_user',
             'PASSWORD': 'yourpassword',
             'HOST': 'localhost',
             'PORT': '',

6. Fetching Best Performing Ad Campaigns

Integrating with Google BigQuery, Google Ads, and Facebook Ads: To fetch ad performance data, you will need to set up API connections with Google BigQuery, Google Ads, and Facebook Ads.

Google BigQuery:

  1. Install Google Cloud library:

     pip install google-cloud-bigquery
  2. Authenticate and fetch data:

     from import bigquery
     client = bigquery.Client()
     query = """
     SELECT ad_title, ad_description, performance_metric
     FROM `your_project.your_dataset.your_table`
     ORDER BY performance_metric DESC
     LIMIT 1
     query_job = client.query(query)
     results = query_job.result()
     for row in results:
         print(row.ad_title, row.ad_description, row.performance_metric)

Google Ads and Facebook Ads:

  • Follow similar steps to authenticate and fetch data using their respective APIs.

Storing and Managing Ad Data in PostgreSQL:

  1. Create models in Django for storing ad data:

     from django.db import models
     class Ad(models.Model):
         title = models.CharField(max_length=255)
         description = models.TextField()
         platform = models.CharField(max_length=50)
         performance_metric = models.FloatField()
         created_at = models.DateTimeField(auto_now_add=True)
  2. Run migrations to create the database schema:

     python makemigrations
     python migrate

7. Using OpenAI GPT-3.5 Turbo for Ad Content Generation

Creating a Prompt to Fetch Ad Titles and Descriptions: To generate ad content, create a prompt that specifies the required tone and character limits.

Integrating OpenAI API with Django:

  1. Install OpenAI library:

     pip install openai
  2. Generate ad content using OpenAI:

     import openai
     openai.api_key = 'your_openai_api_key'
     def generate_ad_content(tone, character_limit):
         response = openai.Completion.create(
             prompt=f"Generate an ad title and description with a {tone} tone within {character_limit} characters.",
         return response.choices[0].text.strip()

Parsing the OpenAI API Response in Python: The response from OpenAI will be in JSON format. Parse it to extract the ad title and description.

import json

def parse_openai_response(response):
    data = json.loads(response)
    title = data['title']
    description = data['description']
    return title, description

8. Building the API to Return Ad Content

Designing the API Endpoint: Create an endpoint to fetch and return ad content.

Implementing the API:

  1. Define a view in Django:

     from rest_framework.views import APIView
     from rest_framework.response import Response
     from rest_framework import status
     class AdContentView(APIView):
         def get(self, request, tone, character_limit):
             ad_content = generate_ad_content(tone, character_limit)
             title, description = parse_openai_response(ad_content)
             return Response({"title": title, "description": description}, status=status.HTTP_200_OK)
  2. Add the URL pattern:

     from django.urls import path
     from .views import AdContentView
     urlpatterns = [
         path('ad-content/<str:tone>/<int:character_limit>/', AdContentView.as_view(), name='ad_content'),
  3. Test the API: Use tools like Postman or curl to test the API endpoint.

9. Conclusion

By integrating OpenAI's API into your ad tech product, you can streamline and enhance your ad campaigns, ensuring they are effective and compliant with platform guidelines. This tutorial has covered the technical implementation in detail, demonstrating how to create a powerful tool for automated ad content generation.

10. Appendix

By following this guide, you'll be well-equipped to harness the power of AI in your ad tech product, driving better results and maximizing efficiency.

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