Designing an Effective Chatbot for Online Marketing: A Practical Guide

Hi Marketers,

This summer, I dedicated my time to creating a few different chatbots for my clients, a trend that has become increasingly ubiquitous. Integrating OpenAI technology into ChatGPT has added a new level of excitement to this field.

I want to share my learnings and observations from the project in simple English to help online marketers design and implement chatbots for their websites and other messaging channels. I want to express profound gratitude to my mentor Jim Sterne, a prominent figure in the Generative AI Transformation space, whose invaluable thoughts and feedback have greatly influenced my projects.

Here are the top questions I will address in this blog:

• What are my top learnings from the chatbot design project?
• What can a chatbot do in 2023, and why should you care?
• What is the core concept of a Chatbot?
• What is the difference between ChatGPT, IBM Watson, Amazon Lex, Google Dialougflow, and Microsoft Conversation AI?
• How to choose a platform?
• How do you get started implementing a chatbot?
• How should you consider the cost?
• How to train the Chatbot over time?

What are my top learnings from the chatbot design project?

My top takeaways from the project:

• Hold high ethics for your content: the essential factor in designing a chatbot is only allowing Chatbot to manage approved actions/content. (Your legal team will thank you as well.)
• Don’t rely on Chatbot to provide random solutions. As Jim said,” …ChatGPT is not your doctor nor your factor checker…” Using Chatbot without human supervision can be dangerous.
• Place Chatbot in the front end to interpret the user intent is the best usage of generative AI; interpreting meaning from inputs is at the wheelhouse of the Chatbot (see chart below)

• Explicitly inform users about the question the bot is addressing. The Chatbot should gracefully transition to human support if it cannot handle a user’s query.
• Build emergency event handlers that can promptly transfer urgent queries to human agents.
• Bot training is critical. Collecting human feedback continuously improving the Chatbot’s knowledge base over time is essential for enhancing its performance and accuracy.

What can a chatbot do in 2023, and why should you care?

In 2023, chatbots have evolved significantly and now offer a wide range of functionalities, especially as tools for lead generation and supporting human representatives in providing efficient customer service.

Here are some key actions to consider:

1. Lead generation: Chatbots can actively engage with website visitors, collect relevant information, and guide them through the sales funnel. This automation can help generate potential leads and increase conversion rates.
2. Addressing FAQs: Chatbots equipped with knowledge can provide instant answers to frequently asked questions about products or services, improving customer experience by providing quick and accurate information.
3. Task automation: Chatbots can assist with various tasks, such as scheduling appointments, sending emails, or fulfilling other customer needs. This automation streamlines processes and improves efficiency.
4. Seamless human interaction: Chatbots can seamlessly connect with human representatives when needed with a smooth transition from automated interactions to personalized assistance, providing a better customer service experience.
5. Supporting human representatives: Integration of chatbots into the customer support ecosystem enhances the capabilities of human representatives. By handling routine queries, chatbots free up human resources to focus on more complex issues, leading to more efficient customer support.

What is the core concept of a Chatbot?

Below is a list of the critical components of a typical Chatbot:

1. Intent Recognition: Understanding the user’s purpose or intention.
2. Entity Extraction: Identifying relevant information from the user’s message.
3. Context Tracking: Keeping track of the conversation history and user preferences.
4. State Management: Managing the flow and current stage of the conversation.
5. Response Generation: Generating appropriate and meaningful responses.
6. Error Handling: Handling situations where the Chatbot fails to understand or encounters errors.

These components work together to enable the Chatbot to understand user input, maintain context, and provide relevant responses.

What is the difference between ChatGPT, IBM Watson, Amazon Lex, Google Dialogflow, and Microsoft Conversation AI?

When comparing ChatGPT with IBM Watson, Amazon Lex, Google Dialogflow, and Microsoft Conversation AI, it’s important to note that they all serve as Chatbot and conversational AI solutions. While they share similarities in setting up the bots and achieving your goals, they have distinct focuses and capabilities.

• ChatGPT excels in text generation and natural language processing.
• IBM Watson offers a comprehensive AI platform with many functionalities beyond chatbot capabilities.
• Amazon Lex is designed for voice interactions, enabling voice-based chatbot interactions.
• Google Dialogflow emphasizes integrating various messaging platforms, facilitating seamless interactions across channels.
• Microsoft Conversation AI specializes in multi-channel interactions, allowing chatbots to engage with users across different platforms.

How do you choose a platform?

While many of these platforms can accomplish your desired objectives, it’s essential to consider factors such as cost, ease of use, and scalability. Exploring trial packages and samplers each platform offers may be beneficial to get a hands-on experience and evaluate their usability for your specific product or business needs.

Additionally, it’s worth mentioning that you can even combine multiple platforms for enhanced functionality. For instance, you can integrate Google Dialogflow with ChatGPT, passing intents from Dialogflow to ChatGPT for generating accurate responses.

Ultimately, rather than getting overwhelmed by the differences, it is recommended to try out the platforms and focuses on their usability, cost-effectiveness, ease of training, and extendability to make an informed decision based on your specific requirements and objectives.

How do you get started implementing a chatbot?

To start implementing a chatbot, defining its scope and objectives is essential. Begin with a simple approach and gradually expand the Chatbot’s capabilities based on user feedback and requirements.

Step 1 Define the chatbot scope/goal.

The Chatbot’s primary goal should align with the marketing objectives, for example, collecting information, redirecting users to relevant teams, or addressing basic customer questions.

Step 2 Try out some platforms before expanding.

Once you have tried building and testing some of the tools, you can commit to expanding more on chatbot tasks and training for the bot.

How should you consider the cost?

When implementing a chatbot, there are several essential items to consider:

1. Technology costs: Assess the expenses associated with the necessary technology infrastructure, such as hosting costs for platforms like Google Cloud, Google Dialogflow, or OpenAI API. Consider the pricing models, subscription plans, and potential usage fees to ensure they align with your budget.
2. Training, maintenance, and extension efforts: evaluate the resources required to train and maintain the Chatbot and factor in the effort needed to extend its capabilities as your business requirements evolve.
3. Knowledge management: Establish a process for managing the Chatbot’s knowledge base. Consider how you will curate and update the information it relies on to provide accurate responses. Allocate resources for content creation, review, and approval to ensure the Chatbot has access to up-to-date and reliable information.
4. Development cost: Evaluate the costs associated with developing the Chatbot itself, including the time in designing, coding, and testing. Consider whether you will build the chatbot in-house or engage external resources and budget accordingly.

How to train the Chatbot over time?

Training is essential. Below are some areas to consider for training:

Knowledge Base: 

• Improve the knowledge base by adding new and removing outdated content. The knowledge base is a repository of information or data the Chatbot can access to respond. It can include structured data, pre-defined answers, frequently asked questions (FAQs), or connections to external systems or databases.
• Ensure the Chatbot’s knowledge aligns with legal and approved guidelines to avoid legal complications.
• Expert review is required for expired content to ensure accuracy.

Feedbacks:

• Integrate feedback loops to identify areas of improvement and refine the Chatbot’s responses.
• Review chatbot performance over time to improve areas (for example, how many iterations did it take to get to the intent (candidates for future training).

A few last words

While Chatbot can do a lot, start with a simple task. Trial and test products out to get a jump start. Most of your learning can be done while testing the tool. Always ensure your Chatbot performs approved actions.

I hope this blog is helpful to you. At YourDataMom, we love data. We constantly focused on exploring innovative ways to optimize marketing campaigns with data and AI. Please feel free to DM me for any questions or marketing projects.