The Power of AI and How To Use It In Customer Success
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Every week, I chat with Customer Success Managers and leaders. I love these conversations because hearing the different strategies used to help customers achieve their goals is always inspiring.
But two questions keep popping up:
This article will explore the development of AI from the 1950s, the factors behind its widespread accessibility, and how various AI components like Machine Learning and Generative AI work together.
In this article, you will learn:
Let's explore the fascinating world of AI and discover how it can transform our work, personal projects, and customer success strategies.
Let's start by exploring the history of AI and answering why it’s suddenly everywhere.
AI has a long history, starting in the 1950s with Alan Turing’s idea of the Turing Test, which measured whether machines could think like humans. Turing asked one bold question, "Can machines think?"
This caught people's attention and sparked interest in artificial intelligence. Why? By asking this question, he implied that the machine was considered intelligent if a human couldn't distinguish between a machine's response and another person's. This concept became the cornerstone for AI researchers moving forward.
Early AI relied on strict rule-based systems, meaning computers were programmed with "if-then" statements to solve problems like equations or play simple games like chess. In the 1980s, expert systems were created for specialized fields like medicine. These systems mimicked the human decision-making abilities of experts in those specific fields, but they couldn’t handle tasks they weren’t designed for, making them “limited.”
Then came Machine Learning. By the 2000s, researchers focused on algorithms that could learn from data instead of relying on fixed rules. You might have heard about Deep Blue by IBM beating the world chess champion, Gary Kasparov, in chess. This period also saw the creation of Kismet, the first robot capable of social interactions, the release of the Roomba to the public, and companies beginning to leverage AI to influence consumer decisions and enhance their products.
Which brings us to today. The explosion of AI across multiple industries can be attributed to several key factors:
In fact, hundreds, if not thousands, of AI tools and technology are being developed.
You may have heard of these words, as they are common in the SaaS world nowadays.
But how do they fit together?
Well, they are all interrelated concepts within the broader field of AI. Each serves distinct functions and is built upon different principles and technologies. And it all starts with Machine Learning!
The backbone of many AI systems, including Generative AI and Conversational AI. It's a method where computers learn from data. You feed them many examples, and they identify patterns that help them make predictions or decisions. The more data they are exposed to over time, the better their performance. Think of it as teaching kids to recognize animals by showing them many different pictures.
That's where ChatGPT and chatbot fit in. It's a specific type of ML model trained on large amounts of text to understand and generate human-like responses. It's like giving a machine an all-access pass to every available book, article, and webpage, then asking it to help you write or answer questions.
Another type of ML that uses neural networks to simulate the human brain, allowing it to “learn” from large amounts of data.
This form of AI creates new content, such as text, images, or music, based on patterns it learns from LLMs. It can generate anything that didn’t exist before based on learned data patterns.
These are your virtual assistants like Siri or Alexa. They use LLMs to chat with you and respond to your commands, but they're specialized. They use natural language processing (NLP) to understand what you're asking and generate human-like responses in a conversational format.
Now that you have a foundational understanding of AI, let’s talk about ChatGPT and custom GPTs.
In November 2022, a company called OpenAI released a chatbot called ChatGPT (GPT-3) trained on 175 billion parameters for social media users. According to Forbes, the chatbot had gathered over 1 million users in just a matter of days.
But what is ChatGPT?
It is a type of Generative AI model that uses LLM to provide users with everything from dinner recipes to learning about the history of the Galapagos Islands! ChatGPT-3 was the first version of ChatGPT that allowed users to directly ask the chatbot (a.k.a LLM) questions and get answers. Because of this model’s success, there have been huge impacts across multiple industries, such as customer service, healthcare, finance, marketing, sales, and content creation.
Custom GPT, or just GPT, is another feature of OpenAI that lets anyone build on this by personalizing the LLM (without knowing any coding language) to meet specific needs and then sharing it with others. You can create GPTs for your business brand or personal projects. This requires a GPT-4 or GPT-4o account on the OpenAI platform.
In summary, think of ChatGPT as a writer with general knowledge and Custom GPT as a writer specifically trained to handle your specific requests. Remember, the more data you feed into the GPT for training, the better it will be at answering questions with exact answers.
Depending on your tech stack, you may already have access to AI tools that allow you to personalize customer communication and predict the chances of an account churning.
But here are some use cases where ChatGPT and Custom GPTs can be helpful. Note that some of these use cases can be created by yourself or with the help of your IT or Engineering teams by simply uploading knowledge sources and writing instructions. In contrast, others may require integration with other tools.
Finding the right voice for your brand can be tricky on social media and blogs.
Whether you need to write emails, organize notes, or brainstorm creative projects, a Custom GPT can be a useful digital assistant.
When using AI tools like ChatGPT and Custom GPT, it's crucial to safeguard sensitive information. Always avoid entering sensitive or confidential information into these AI platforms to protect your and your company's interests.
This includes, but is not limited to:
Sharing such details could lead to unauthorized access, data breaches, and severe legal consequences for you and your organization.
Suppose your company needs to reference sensitive data in responses. In that case, it should create chatbots that safely use this information by integrating a Retrieval-Augmented Generation (RAG) system with current data setups.
This approach allows powerful AI language tools to be used while keeping sensitive information secure and compliant with data privacy regulations. Combining RAG with tools like Snowflake Cortex can create AI chatbots capable of securely accessing critical information, customer details, and company documents. These chatbots can control information visibility and protect data effectively.
Pretty exciting, huh? The possibilities are endless!
Want to get started with creating your own Custom GPT? Follow these steps:
Here’s an easy-to-follow six-step framework to build your GPT instructions!
Clearly define the specific role your GPT will play. For example, it could act as:
Outline the specific outputs you expect from the GPT. This might include:
Identify your target audience's demographics, backgrounds, roles, and knowledge levels. Be as specific as possible to ensure your GPT communicates effectively with them.
Consider the following:
Select and compile the sources that your GPT will use for training. These sources should provide comprehensive and accurate information to ensure the GPT can respond effectively. Consider the following sources:
Decide on the tone of your GPT to match your brand and audience expectations.
Options include:
Next, choose how the GPT will interact with users.
Options include:
Ensure all user data is handled confidentially and securely by detailing your security measures. Key points to consider:
Once you’ve begun to use this GPT, establish methods for collecting user feedback and outline a process for iteratively improving your GPT.
Consider the following:
⭐ Note: For more complex actions like integrations via APIs to get data from other systems, visit Medium’s article here.
Here is a quick video showing how to create a Custom GPT to analyze customer health scores and determine which accounts will likely churn.
Other large language models and AI tools are available, each with different features. Here’s an overview of the other popular options.
Using AI at work and home offers many advantages. The SaaS business is very competitive, and staying ahead requires technology that simplifies tasks, streamlines processes, improves customer interactions, and provides personalized content to drive engagement and adoption.
This can free up time to focus on important activities like building customer relationships or analyzing data to track renewal rates and churn. At home, AI like ChatGPT can help automate time-consuming tasks and help you discover new hobbies, improving your overall well-being.