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Using Conversational AI for Customer Service

Conversational AI (aka Conversational Artificial Intelligence) typically refers to technology such as chatbots or voice assistants that are used to handle customer enquiries.

They use large volumes of data, machine learning, and natural language processing to help imitate human interactions, recognise speech and text inputs, and translate their meanings across various languages, eliminating or reducing the need to speak to a live agent. 

Conversational AI is increasingly being used to reduce costs in customer support.

When deployed correctly, it can enable customers to access quick answers and solutions to their issues 24/7.

This may not have been possible otherwise due to the cost limitations of 24/7 operations, especially the high salaries of Australian customer service staff and the penalty rates applicable to shifts outside of normal hours. 

As a good safety net to avoid bad customer experiences, if things get a little difficult, the customer enquiry can still be routed to a live agent to take over where applicable. 

How to get started with Conversation AI for Customer Support

First, you'll need the technology, and thankfully, you'll find a list of suppliers below that can provide and support the deployment of conversational AI technology in your business.

Then, one of the easiest steps for businesses to take is to create a list of frequently asked questions and start building an AI tool to handle those enquiries.

But it's not just customer support; there are many places conversational AI can be useful, including: 

  • Online customer support
  • Health Care
  • Computer software
  • HR Processes

Without getting too technical (we'll leave that to the experts), one exciting attribute of Conversational AI is that it can 'learn' and improve its successful resolution rate.

Metrics to Measure Conversational AI Success in Customer Service

Assessing the success of conversational AI, such as chatbots and virtual assistants, involves tracking a variety of metrics that reflect its performance, effectiveness, and impact on user experience.

Here are some key metrics used to evaluate conversational AI success:

1. Customer Satisfaction (CSAT)

  • Definition: Measures how satisfied users are with their interaction with the conversational AI.
  • Importance: High satisfaction scores indicate that the AI is effectively meeting user needs and providing a positive experience.

2. First Response Time (FRT)

  • Definition: The time it takes for the conversational AI to respond to the user's initial query.
  • Importance: A shorter first response time can improve user experience by providing immediate engagement and support.

3. Resolution Rate

  • Definition: The percentage of user queries or issues successfully resolved by the conversational AI without needing human intervention.
  • Importance: A high resolution rate indicates that the AI is capable of handling a large portion of inquiries independently, which reduces the workload on human agents.

4. Containment Rate

  • Definition: The percentage of interactions where the AI was able to resolve the issue without escalating it to a human agent.
  • Importance: A high containment rate suggests that the AI is effectively managing and resolving user requests, enhancing efficiency and cost-effectiveness.

5. Abandonment Rate

  • Definition: The percentage of interactions where users leave or stop engaging with the AI before their issue is resolved.
  • Importance: A low abandonment rate indicates that users are finding the AI helpful and are staying engaged until their query is resolved.

6. Intent Recognition Accuracy

  • Definition: The ability of the AI to correctly understand and identify the user’s intent based on their input.
  • Importance: High accuracy in intent recognition is crucial for delivering relevant and accurate responses, which enhances user satisfaction.

7. Engagement Rate

  • Definition: The frequency with which users interact with the AI, often measured by the number of sessions or the duration of interactions.
  • Importance: A high engagement rate suggests that users find the AI useful and are actively using it for assistance.

8. Session Length

  • Definition: The average duration of a user session with the AI.
  • Importance: Depending on the context, shorter session lengths may indicate efficient resolution, while longer sessions could suggest complex interactions or potential issues with the AI’s performance.

9. User Retention Rate

  • Definition: The percentage of users who return to use the AI after their initial interaction.
  • Importance: High retention rates indicate that users find the AI valuable and are likely to use it again, reflecting its effectiveness and user appeal.

10. Fallback Rate

  • Definition: The frequency with which the AI fails to understand a user query and has to trigger a fallback response or escalate to a human agent.
  • Importance: A low fallback rate indicates that the AI is effectively understanding and responding to user queries.

11. Escalation Rate

  • Definition: The percentage of interactions that are escalated from the AI to a human agent.
  • Importance: A balanced escalation rate shows that the AI handles straightforward queries effectively while appropriately escalating more complex issues.

12. Training Data Coverage

  • Definition: The extent to which the AI’s training data covers the range of queries it encounters in production.
  • Importance: High coverage ensures that the AI can handle diverse user inputs and scenarios, reducing errors and improving overall performance.

13. Conversion Rate

  • Definition: The percentage of interactions that result in a desired outcome, such as a sale, signup, or completion of a specific action.
  • Importance: A high conversion rate indicates that the AI is effective in guiding users towards completing key tasks or goals.

14. Error Rate

  • Definition: The frequency of incorrect or inappropriate responses generated by the AI.
  • Importance: A low error rate is critical for maintaining trust and delivering a smooth, accurate user experience.

15. Cost Savings

  • Definition: The financial savings achieved by using conversational AI instead of human agents for certain tasks.
  • Importance: Demonstrates the efficiency and return on investment (ROI) of the AI solution.

Search Suppliers of Conversational AI Technology

If you'd like to explore introducing Conversational AI into your Australian business, reach out to the suppliers below. 

Or, use the search filter to find suppliers of other CX automation tools and services.