Adapt Your Marketing Chatbot to Feedback with Evolutionary Design


ChatGPT has been on everyone’s radar over the past year.

The same goes for Google Bard, a conversational chatbot that can create, edit, and translate text; brainstorm ideas; and write code.

These technologies wouldn’t exist without generative artificial intelligence (AI), or GenAI. 

Companies can use GenAI to analyze data, streamline customer service, design products, automate workflows, and much more. Because of this, AI-powered chatbots have applications in marketing and sales, healthcare, hospitality, and other verticals. 

There’s no denying that generative AI has the potential to disrupt entire industries. What’s even more impressive is its ability to continuously learn from new data and become smarter over time. 

In fact, you can train your AI-enabled chatbot to improve its performance and conversational skills over time — and, as a result, boost your marketing efforts.

Here’s what you need to know. 

How Does a Generative AI Chatbot Work? 

Generative AI models use machine learning, a type of artificial intelligence, to learn from data and create content based on that data. 

These systems also rely on natural language processing (NLP), which allows them to understand and interpret human language so they can generate contextually relevant responses. Moreover, they can be trained for specific tasks and applications like market research, customer support, or process automation. 

GenAI underlies the functionality of chatbots like Google Bard, ChatGPT, and Replika. These generative AI chatbots are software applications capable of creating human-like conversations  and other content from the data and chats they’re trained on. The more you talk to them, the smarter they become.  

From a business perspective, chatbots can streamline manual or repetitive tasks and reduce the need for human intervention. 

Let’s talk about how they do just that in marketing.

The Role of AI-Powered Chatbots in Modern Marketing 

Marketers use generative AI in the form of chatbots to analyze data from conversations with customers, offering insights into their behavior so they can create better offerings. These systems can also handle customer inquiries and act as virtual assistants. 

For example, marketing departments within banks already leverage this technology to generate leads and upsell or cross-sell financial products. GenAI also allows them to learn more about their target customers so they can deliver personalized experiences.

Let’s say you want to learn more about the credit cards offered by a bank. You visit its website or download its app and type your question (e.g., “What type of credit card is best for a startup with less than $50,000 in annual revenue?”) in the chat window.

The bank’s chatbot will ask further questions to better understand your needs. After that, it will make personalized product recommendations and provide you with the information you need to make a decision. And most of that it can do without human help. 

A surprising 37% of U.S. consumers interacted with a banking chatbot in 2022. And, this technology can streamline marketing operations in most industries, not just banking and finance. 

Depending on your business needs, you can use generative AI to:

  • Collect customer feedback
  • Gain insights into customer needs 
  • Deliver personalized shopping experiences at the perfect time
  • Automate your marketing campaigns
  • Nurture potential and existing buyers
  • Create more engaging website content and more deliverable email messages
  • Analyze market intelligence

It’s also worth mentioning the impact of NLP on digital marketing, which spans customer service, lead generation, content creation, and other areas. 

This technology also enables more effective communication through chatbots, allowing marketers to tailor their messaging to individual customers or groups. 

For instance, brands can deploy chatbots to brainstorm ideas for landing pages, advertising campaigns, or email newsletters and create content that converts. 

Understanding Evolutionary Design and the Value of Feedback

As mentioned earlier, generative AI models learn from user feedback and other data. Since we create about 328 million terabytes of data each day, these systems are able to constantly learn and improve themselves. 

Their ability to evaluate their own output and become more intelligent over time is what makes them a critical tool for implementing evolutionary design.

At the core of evolutionary design is a continual cycle of feedback, which in a marketing context can continue to make your chatbot more and more user-centric when fed with user data. 

Think of evolutionary design as a building plan that’s flexible and open to changes. As a result, you can adapt it to your needs while keeping its primary features intact. 

Successfully adapting your chatbot to align with customer needs requires embracing evolutionary design to continuously train the bot on your business data, such as customer feedback and sample conversations.

Bringing It Together: Applying User Feedback to Continually Improve Your Marketing Chatbot 

The accuracy of generative AI chatbots depends on the quality of the data they’ve been trained on. For this reason, it’s essential to define your chatbot’s purpose and then “feed” it with the right data, on an ongoing basis. 

With that in mind, let’s discuss some of the best practices for continuously training a chatbot using feedback. 

Collect the Feedback

First, gather user input through surveys, polls, direct messages, or reviews. Use this data to understand what users like or dislike about your chatbot — and the challenges they encounter. 

Another option is to embed feedback prompts into the chatbot’s interface. Let’s see a few examples:

  • Rating system (e.g., thumbs up/thumbs down)
  • Feedback forms 
  • Specific feedback queries
  • Contextual queries
  • Exit questions

Place these prompts strategically within the conversation flow in a way that doesn’t negatively impact the user experience. 

Companies can also collect feedback via social media, forums, or live training sessions. 

Look for Patterns and Trends

Next, look for trends and patterns in the feedback received. 

For example, you may notice that more than half of users report inconsistencies in the chatbot’s responses. Or, on the contrary, they might consistently praise specific features, such as the chatbot’s ability to help them with seemingly complex customer support questions. 

These patterns can help you determine where the system is falling short and what improvements are needed to boost its performance. 

Analyze User Feedback 

Go one step further and organize the feedback into categories like technical issues, security, personalization, or user-friendliness. 

After that, analyze each category to determine which aspects require immediate attention. 

For instance, some issues, such as repetitive or limited responses, might have a greater impact on the user experience than others (e.g., a slow response time). Therefore, you’ll want to address the former as soon as possible. 

You must also prioritize security and privacy. These aspects are of paramount importance, given that chatbots can collect customer data. 

Make Iterative Changes

Once you have identified areas for improvement, make the necessary changes while continuously monitoring and testing your chatbot. 

Let’s say the chatbot generates repetitive responses. This problem indicates the need to diversify its vocabulary and conversational skills. 

But if your chatbot provides inaccurate information, you’ll want to enrich its knowledge base. You might also need to improve its ability to retrieve and present data. 

Implement these changes on a planned timeline, so you can best measure their impact. Meanwhile, continue to test the chatbot and collect that all-important user feedback. 

Measure the Results 

Set key performance metrics (KPIs) to assess the impact of each iteration. These may include:

  • User satisfaction scores
  • Engagement rates
  • Task success rate
  • Bounce rate
  • Goal completion rate
  • Retention rates
  • Conversion rates 
  • Response time
  • Average conversation length 
  • Missed messages

For example, retention rates indicate the percentage of consumers who return to engage with the chatbot in a given period. The higher this number, the better.

Some users will leave your website shortly after interacting with the chatbot. They may initiate a conversation and exit the chat window a few seconds later. In such cases, you’re dealing with a high bounce rate, which requires further investigation. 

Missed messages, on the other hand, suggest that your chatbot can’t fully comprehend customers’ queries. Therefore, you’ll want to focus on improving its conversational abilities. 

Use Chatbot Analytics and Testing Tools

Some chatbots have built-in analytics which allow users to easily monitor and analyze their performance, such as IBM’s watsonx Assistant

If your chatbot lacks this feature, use third-party analytics and testing tools like QBox or BotCore. These apps allow you to track all the right metrics, run simulations, and test the chatbot across multiple platforms and devices. 

Alternatively, you can connect your chatbot to Google Analytics. However, this option is more limited than dedicated tools.

Get the Most Out of Your Marketing Bot

With proper training, generative AI chatbots can become part of your marketing team. In some cases, these systems are just as helpful and knowledgeable as humans.  

You can use them to generate and qualify leads, market your products, automate tedious tasks, and increase sales. Plus, they can streamline time-consuming activities like content creation, website optimization, and customer service. 

But, maintaining a high level of performance via evolutionary design requires ongoing training. These AI models are capable of continuous learning, so you need to constantly feed them new data and monitor the results. 

The first step is to collect user feedback. After that, you’ll analyze the feedback received, look for patterns, and seek areas of improvement. 

Make gradual changes, test the results, and continue to feed data into your bot. In the meantime, use analytics tools to track your chatbot’s performance and its impact on the user experience.