In my previous blog post, I touched upon the potential of using ChatGpt and GPT-4 for sales and marketing tasks, with a focus on quality and accuracy. In this blog, I am going to delve deeper into the topic and show you how to scale the use of AI models like ChatGpt/GPT-4 while maintaining true accuracy of personalization. By integrating with APIs and internal apps, it is possible to reach thousands of buyers with highly personalized emails that are tailored to their specific needs and interests.
The rise of Generative Pre-trained Transformer 4 (GPT-4) has created an exciting new landscape for businesses looking to generate high-quality content at lightning speeds. But as promising as this technology is, one of the biggest challenges businesses face is accuracy. The models are only as accurate as the data they were trained on, which can sometimes be limited. This makes it difficult to generate content that is relevant, up-to-date, and accurate.
To address this accuracy challenge, our team has developed a framework for the effective utilization of GPT-4 and ChatGpt to generate accurate content at scale. This framework involves integrating GPT-4 models with external APIs to feed them with the latest real-world data, and integrating these models with existing business tools to utilize real-time data.
To demonstrate the practical applications of our framework, we will use the example of a salesperson in a recruiting firm.
Analysis of Salesperson Tasks in a Recruiting Firm
Writing a single personalized email can take anywhere from 10-20 minutes for a salesperson. A salesperson in a recruiting firm performs tasks like the following to craft a personalized email to a buyer:
Check job portals regularly
Identify interesting job postings
Learn more about the companies that are hiring
Understand what they are hiring for
Qualify and decide
Find key contacts in each company
Write the email
Personalize various parts of the email
By breaking down these tasks into smaller components, we were able to identify the areas where GPT-4 and external APIs could help streamline the process, resulting in highly personalized emails in a fraction of the time it would normally take.
Leveraging GPT-4 and APIs to automate prospecting tasks
Building our solution, we envisioned a hybrid network of APIs and GPT-4 models that together will automate many to all of the above salesperson tasks in a recruiting firm.
Monitoring Job Portals: The 3 APIs were used to monitor multiple job portals (covering Angellist, Lever, Taleo, Jobvite, Greenhouse, LinkedIn Jobs, and Google Jobs) for new job postings, which saved the salesperson from having to manually check each portal regularly.
Finding Key Contacts: The 2 APIs were used to find key contacts in the hiring accounts and enrich their details, allowing the salesperson to quickly identify the right person to reach out to. The reason for using 2 APIs was that some of the APIs we used in the previous step returned a different unique identifier that one API does not support.
Understanding Hiring Requirements: The GPT-4 model was used to read job descriptions and identify required skills and qualifications, giving the salesperson a better understanding of the company's hiring needs.
Gathering Company Information: Another GPT-4 model was used to scrape company websites and generate a summary based on the content found, providing the salesperson with a quick overview of the company.
Tailoring the Pitch: A GPT-4 model was used to write the pitch tailored to each account based on their hiring requirements, ensuring that the salesperson's email was highly relevant to each recipient.
Crafting the Email: The final GPT-4 model was used to craft a highly personalized email to each key contact in a given account using all the information gathered from the previous models and APIs.
In conclusion, by leveraging the power of multiple APIs and GPT-4 models, we were able to automate the salesperson tasks in a recruiting firm and build a working prototype that generates highly personalized emails in a fraction of the time it would normally take.
Let's take an example of a recruiting firm that was hiring for senior software engineers with .Net experience. A salesperson found a job posting for this position on a job portal and was able to find the key contact, John, at the hiring company, X (the real name is removed). By using the framework described above, the salesperson was able to craft a personalized email for John.
The salesperson was able to highlight their pool of senior engineers with .Net experience, both in the US and remotely, and offer a free proposal if John was interested in scheduling a call.
This email was not only personalized but also accurate and up-to-date, as it was generated with the latest information from job portals and company websites.
Expanding the example to propose a framework for B2B companies
The results of this framework were significant and can be extended to many B2B businesses. It starts with fully understanding what a salesperson is doing to write her emails personalized to each buyer. And then look at internal apps' data, external APIs, and GPT-4 models that can automate it. The rest of the process is optimizing how these functions work together and send their inputs and outputs to each other.
Barriers for B2B companies to implement similar solutions with GPT-4 and APIs
While the potential benefits of combining GPT-4 models and APIs for business tasks are clear, implementing such a solution can also bring its own set of barriers and challenges.
Lack of Collaboration: Collaboration between creative people and non-technical users can be difficult, as it requires a deep understanding of both the business requirements and technical aspects. Bridging the gap between these two worlds can be challenging, but necessary to successfully implement a hybrid solution that utilizes GPT-4 and APIs.
Code-Based and Hard for Many Users: The process of implementing a solution using GPT-4 models and APIs is code-based and requires technical skills. This can be a barrier for non-technical users who may struggle to understand the code and how it works.
Set up Cloud Infrastructure: To leverage the full potential of GPT-4, companies will need to set up a cloud infrastructure. This can be a significant barrier for small companies that may not have the resources or expertise to set up and manage cloud infrastructure.
Difficulty in Experimentation: Experimenting with GPT-4 models and APIs can be challenging, as it requires a deep understanding of both the business requirements and the technology. Experimenting with different models and APIs can be time-consuming and can be difficult to find the right combination that works best for the company's needs.
Despite these barriers, companies who are willing to invest the time and resources into implementing a GPT-4 and API solution can see significant benefits. By combining the power of GPT-4 with the latest real-world data, companies can generate accurate and up-to-date content at scale, helping them make better decisions and drive better results.
Introduction to Personize Studio
I am going to use the opportunity to introduce our new and innovative solution that addresses some of the barriers that businesses face when trying to implement GPT-4 and API-powered solutions.
Our solution, Personize Studio, is a no-code platform in Google Sheets that allows businesses to build and fine-tune GPT-4 models without any coding skills, providing a collaborative environment that encourages teams to work together to achieve their goals.
The no-code solution of Personize Studio solves a major problem that businesses face, which is the lack of collaboration between technical and non-technical teams.
With Personize Studio, copywriters, sales teams, and marketing teams can all work together to design GPT-4 models that accurately reflect the voice and tone of the business, without relying on developers to make changes.
In addition to improving collaboration, Personize Studio also makes it easy for businesses to experiment with different GPT-4 models and prompts such as a model to summarize website content, another model to write the pitch tailored to the buyer, and another model to write the first email. Real-time testing and experimentation are essential for optimizing results, but this can be difficult when changes need to be made by developers.
With Personize Studio, teams can experiment with different models and prompts in real-time, allowing them to refine their pipelines and achieve optimal results. In the above example, Personize helped us to build and optimize a pipeline to write highly personalized emails for our recruiting client.
Personize Studio is built on the Google Sheets platform, making it easy for businesses to implement and integrate with their existing workflows. The no-code environment of Google Sheets means that teams can quickly design, build, and experiment with different GPT-4 models, without having to worry about the technical details.
Creative people and experienced business users can make GPT-4 become the engine of growth. They know customers and they have strong skills in communicating with them. It is critical to make it as easy as possible for them to leverage the power of GPT-4 and APIs to innovate and design solutions that can improve their productivity.
By removing the technical barriers that have historically hindered collaboration between technical and non-technical teams, Personize Studio is our solution that provides a collaborative environment that encourages teams to work together and design powerful tools with GPT-4 and ChatGPT.