Introduction to Personalized Newsletters

In today's digital landscape, the demand for tailored content has prompted an exploration of the OpenAI API's capabilities to generate business value. Among various applications, the creation of automated, personalized newsletters emerged as a significant opportunity. This aligns seamlessly with my current project, NewsFoldr, a SaaS tool designed to aggregate and simplify news from multiple sources. For a deeper understanding of NewsFoldr's design and architecture, you may refer to these articles: Startup Stash Article 1, Article 2, Article 3.

Example of Data Structure

{

"_id": {

"$oid": "6571449452fa5fdc75d6abc2"

},

"status": "subscribed",

"first_name": "Sedem Quame",

"email_address": "",

"__v": {

"$numberInt": "0"

},

"country": "GH",

"interests": [

"Business",

"Economy",

"science technology",

"science",

"sports",

"world",

"Health"

],

"personalities": [

"elon musk",

"hollywood",

"sarkodie",

"putin",

"trump"

],

"subscription_type": "paid"

}

Process of Newsletter Generation

The suggested script operates on a weekly or custom schedule to automatically create and send personalized emails. The associated costs vary based on the frequency of these emails, which is a critical consideration for the business model. The following sections will outline the steps involved in this process, covering how the script consolidates data, formats emails, and utilizes transactional email services for distribution.

#### Breakdown of Script Functionality

The script serves as the engine for the automated newsletters, performing essential tasks to convert user data into a polished, personalized email. Here’s a brief overview:

• Data Summary and Formatting:

  The script starts by summarizing user data, including interests and preferred news figures, setting the groundwork for the newsletter.

• Transactional Email Service Integration:

  Once formatted, the newsletter is sent through a transactional email provider. Although I faced challenges with Mailchimp's policies, alternatives like SendGrid or Mailgun provide excellent functionality.

Step-by-Step Process

The subsequent sections will walk you through the key components of the script:

#### Retrieving Subscriber Data

The script extracts subscriber information, including emails, interests, and personal details, which is crucial for tailoring content to individual preferences.

import requests

class Subscribers:

def __init__(self, url) -> None:

self.newsletter_api_url = url

def return_all_subscribers(self):

"""Returns a list of all newsletter subscribers."""

endpoint = "get-all-subscribers"

url = f"{self.newsletter_api_url}/{endpoint}"

payload = headers = {}

response = requests.request("GET", url, headers=headers, data=payload)

return response.json()["subscriber"]

#### Generating Reusable Content

Standardized parts of the newsletter, such as the introduction and conclusion, are reused to enhance efficiency.

newsletter_introduction = GLOBAL_SUMMARIZER.generate_newsletter_introduction()

GLOBAL_KEYWORD_CACHE.insert_into_cache(

"newsletter", "intro", newsletter_introduction

)

newsletter_closing_remarks = GLOBAL_SUMMARIZER.generate_newsletter_closing_remarks()

GLOBAL_KEYWORD_CACHE.insert_into_cache(

"newsletter", "closing_remarks", newsletter_closing_remarks

)

#### Creating Personalized Content

The script crafts unique content based on subscriber data, including news relevant to their interests and key figures.

newletter_subscribers = GLOBAL_SUBSCRIBER_SERVICE.return_all_subscribers()

for newletter_subscriber in newletter_subscribers:

newsletter_content = ""

newsletter_content += f"{newsletter_introduction}n"

for interest in newletter_subscriber.get("interests", []):

try:

category_articles = GLOBAL_NEW_SERVICE.get_articles_by_category(

interest.lower()

)

if len(category_articles) > 0:

category_summary = GLOBAL_SUMMARIZER.generate_category_summaries(

"interests", interest, category_articles

)

newsletter_content += f"{category_summary}nn"

except Exception:

pass

#### Caching for Efficiency

To optimize costs and performance, frequently used keywords and terms are cached, reducing the necessity for repeated API calls.

from collections import defaultdict

class KeywordCache:

def __init__(self) -> None:

self.cache = defaultdict(lambda: {})

def search_cache(self, keyword_type, keyword):

return keyword_type in self.cache, self.cache[keyword_type].get(keyword, "")

def insert_into_cache(self, keyword_type, keyword, value):

self.cache[keyword_type][keyword] = value

def delete_from_cache(self, keyword_type, keyword=""):

if keyword_type in self.cache:

if keyword != "":

del self.cache[keyword_type][keyword]

#### Assembling the Newsletter

The final newsletter is compiled by merging both reusable and personalized components, ensuring that each email resonates with the subscriber's preferences.

newsletter_content = ""

newsletter_content += f"{newsletter_introduction}n"

# Additional code that appends to newsletter_content

newsletter_content += f"{newsletter_closing_remarks}n"

#### Email Dispatch

Ultimately, the completed newsletter is sent using a transactional email service to guarantee reliable delivery to subscribers.

class EmailDispatcher:

def __init__(self, _api_key: str) -> None:

self.email_client = Client(_api_key)

self.newsfoldr_sender_email = "" # Insert sender email address

def send_personalized_transaction_email(

self, personalised_email_text, recipient_email_address

):

try:

recipient_email_address = self.newsfoldr_sender_email

message = {

"from_email": self.newsfoldr_sender_email,

"subject": "NewsFoldr Weekly Recap - Key Stories and Insights You May Have Missed",

"text": personalised_email_text,

"to": [{"email": recipient_email_address, "type": "to"}],

}

response = self.email_client.messages.send({"message": message})

logger.info("API called successfully: {}".format(response))

except ApiClientError as error:

logger.error("An exception occurred: {}".format(error.text))

Cost Considerations and Strategy

The remarkable efficiency of automated systems often comes with the challenge of managing expenses, particularly with frequent use. In the context of my project, NewsFoldr, this is evident in the costs associated with generating personalized newsletters using the GPT-3 API. In this section, I will detail the costs involved and suggest strategies to balance frequency and sustainability.

Cost Breakdown

The expense of generating each newsletter is primarily determined by the average character count and distribution frequency. For example:

• Average Character Length: 13,800 characters
• Cost per Email: $0.14 (using GPT-3 API)

Annual Cost Calculation for 100 Free Subscribers:

• Weekly Cost: $0.14 per email × 100 subscribers = $14.00
• Monthly Cost: $14.00 × 4 weeks = $56.00
• Yearly Cost: $56.00 per month × 12 months = $672.00
• Additional Costs: Transactional Email Service: Approximately $20.00 per year
• Total Yearly Expense: $672.00 (Newsletter) + $20.00 (Email Service) = $692.00

This cost can escalate significantly with an increase in subscribers, making it crucial to implement cost-effective strategies.

Strategies to Reduce Costs

1. Two-Tier Newsletter System:

   Establishing both a free and a paid version of the newsletter. The free version would have restrictions on character length and content depth, while the paid version would provide comprehensive features.

2. Content Limitation for Free Version:

   Imposing limits on the character count, tokens, and stories in the free version. Premium features could include deeper story insights, reserved for paid subscribers.

3. Pricing Strategy for Paid Version:

   The paid newsletter could be priced at a premium, for instance, $30.00, with variations based on delivery frequency and content value. For example, newsletters that include financial insights could be priced higher than those focused on entertainment news.

4. Alternative Deployment Models:

   Investigating other models that can be deployed privately and accessed via a microservice. While this may incur costs for server space and technical expertise, it could offer a more economical solution in the long run.

Conclusion

After delving into the intricacies of creating an automated, personalized newsletter system using the OpenAI API, I believe many of you will find value in this endeavor. Whether you aim to launch your own newsletter service or experiment with AI-driven content curation, a solid and efficient codebase is essential.

Sale Offer:

I am thrilled to announce that the code I developed for this project is now available for purchase. Hosted on Gumroad, this codebase serves as a comprehensive toolkit for initiating your automated newsletter service, incorporating all necessary features and functionalities refined through my experience with NewsFoldr.

What You Will Receive:

Acquiring this code provides you with a head start in the realm of AI-powered newsletters. The package includes:

• Complete Codebase: A fully-developed codebase that integrates seamlessly with the OpenAI API and is tailored for creating personalized newsletters.
• Flexibility and Customization: The ability to adjust the service according to your requirements, whether modifying content parameters or incorporating unique features.
• Ease of Integration: The code is crafted for straightforward integration with any transactional email vendor's API, allowing for smooth deployment and scalability.

Requirements:

To get started, you will need:

• Transactional Email Vendor: This is essential for sending newsletters to your subscribers.
• Access to the OpenAI API: Crucial for harnessing the power of AI in content generation and personalization.

Accessing the Code:

The code is readily available for purchase on Gumroad. Here’s the link to access it: Access the Automated Newsletter Code on Gumroad.

By comprehensively understanding and strategically planning the frequency of distributions, we can ensure the project's financial sustainability while still delivering value to our users. Stay tuned for an in-depth cost breakdown and strategies in future sections.