Building a Custom Automation Workflow for Ad Copy Analysis and CRM Integration using Python and Machine Learning
Awesome Automation Workflow
12/30/20234 min read
As businesses increasingly rely on digital marketing and customer relationship management (CRM) tools, automation has become a critical component of success. By automating repetitive tasks and integrating disparate systems, businesses can improve efficiency, reduce errors, and gain valuable insights into customer behavior. In this article, we'll explore how Mountaineer (a small AI automations startup) built an effective custom automation workflow for ad copy analysis and CRM integration using Python, machine learning, and other tools.
Step 1: Custom Vision API GPT Integration
The first step in building our automation workflow is to train a machine learning model to recognize and classify ad copies as good or bad based on specific criteria. We'll use the Custom Vision API from Microsoft Azure to train and deploy the model. Here's how:
1. Train the model using Python and the Custom Vision SDK. You can use labeled data to train the model to recognize patterns and make predictions.
2. Create a Python script that takes an ad copy as input, sends it to the Custom Vision API for analysis, and returns the classification result.
3. Integrate the model with the client's ad platform by creating a REST API using Flask or another web framework. The API should accept ad copies as input, send them to the Custom Vision API for analysis, and return the classification result.
4. Continuously monitor and improve the model's accuracy by collecting user feedback and performance metrics. You can use Python and a database like SQLite or PostgreSQL to store and analyze the data.
Step 2: CRM Automation
Next, we'll integrate the client's CRM system with their email platform using Python and the APIs provided by both systems. Here's how:
1. Use the CRM's API to create new leads when an email reply is received, and update the lead's status based on their interactions with the client's emails and ads.
2. Create a custom field in the CRM to track the likelihood of a lead becoming a paying customer. You can use Python and the CRM's API to create a new custom field and update it based on the lead's engagement history.
3. Set up rules and triggers to automatically send follow-up emails based on the lead's behavior and interactions with the client's emails and ads.
Step 3: Email Automation
To set up automated email campaigns, we'll use Python and the email platform's API to create and send personalized email templates based on the lead's interests and engagement history. Here's how:
1. Use the email platform's API to create and send personalized email templates.
2. Set up rules and triggers to automatically send follow-up emails based on the lead's behavior and interactions with the client's emails and ads.
Step 4: Custom Dashboard
To build a custom dashboard using Bubble, we'll use the Bubble visual editor to create the user interface and the Bubble API to pull in data from the client's ad platform and CRM system. Here's how:
1. Use the Bubble visual editor to create the user interface.
2. Use the Bubble API to pull in data from the client's ad platform and CRM system.
3. Use a library like Plotly or Matplotlib to generate custom charts and graphs and embed them in the dashboard.
Step 5: Automated Custom Process
Finally, we'll identify a custom process that can be automated and design and build a custom automation solution using Python and appropriate libraries and frameworks. Here's how:
1. Work with the client to understand their business processes and identify areas where automation can improve efficiency and reduce errors.
2. Use Python and appropriate libraries and frameworks to design and build a custom automation solution. For example, you can use libraries like PyAutoGUI or Selenium to automate desktop applications, or libraries like Requests or BeautifulSoup to automate web scraping.
3. Use a continuous integration and deployment (CI/CD) tool like Jenkins or Travis CI to automate the build, test, and deployment process.
Building an API App and a Custom Cold Caller using Python and Streamlit
In addition to the above workflow, we can also use Python and Streamlit to build an API app and a custom cold caller. Here's how:
1. To build the API app using Python and Streamlit, use Streamlit's API functionality to create REST endpoints that accept and respond to HTTP requests. Use Flask or another web framework to handle the HTTP requests and responses, and use Python to perform the necessary processing and analysis.
2. To build a custom cold caller using Python, use libraries like Twilio or Nexmo to send automated text messages or make automated phone calls. Use Python to generate the messages or scripts for the calls, and use the library's API to send or make the calls. You can also use machine learning techniques to analyze the responses and optimize the messaging strategy.
Conclusion
By following the steps outlined in this article as pioneered by Mountaineer Agency, you can build a custom automation workflow for ad copy analysis and CRM integration using Python, machine learning, and other tools. By automating repetitive tasks and integrating disparate systems, you can improve efficiency, reduce errors, and gain valuable insights into customer behavior. With the addition of an API app and a custom cold caller, you can further streamline your workflow and improve your outreach efforts.
Edited and written by David J Ritchie