Transforming HR function with Conversational AI & Measuring success

Welcome to the world of conversational AI, where intelligent bots can interact with users in natural and engaging ways. Conversational AI is transforming the way businesses communicate with their customers, employees, and partners, providing personalized and convenient solutions across various domains and platforms.

The value of conversational AI is not just an outcome but a carefully designed process. But how do we really measure their performance? That's where key metrics come in - where every conversation and every question hold a lot of information.

According to Juniper Research, did you know that using conversational AI powered bots in businesses can save up to 2.5 billion hours by year 2024? That's because, on average, a customer support representative deals with around 17 interactions every day. These stats highlight the huge impact of AI-powered conversational agents in improving operations and customer experiences.

Moreover, the abilities of these bots are impressive - they're able to complete 70% of conversations, showing their skill and effectiveness in handling a wide range of tasks. It's no wonder that 74% of customers actually prefer talking to chatbots over a human corporate agent to solve their problems.

In this exploration of conversational AI metrics, we reveal the important insights and implications they have in shaping user interactions and business efficiency.

Conversational AI can help transform HR functions in many ways. Some of the benefits are:

• Automating repetitive and mundane tasks such as scheduling interviews, answering FAQs, collecting feedback, sending reminders, and updating employee records.
• Enhancing employee engagement and satisfaction by providing personalized and timely support, recognition, and guidance.
• Improving talent acquisition and retention by creating a positive and seamless candidate experience, reducing bias and human errors, and matching the best candidates with the right roles.
• Increasing productivity and efficiency by reducing the workload and costs of HR staff, enabling them to focus on more strategic and creative tasks.
• Providing data-driven insights and analytics by collecting and analyzing conversational data, identifying trends and patterns, and generating actionable recommendations.

"Conversational AI is not only a tool but a partner for HR professionals, helping them to optimize their processes, deliver better outcomes, and create a happier and more engaged workforce."

But how do we measure the value of these conversational AI Bot and the impact they have on user satisfaction and business outcomes? How do we ensure that they are delivering consistent and high-quality experiences that meet the expectations and needs of the users? How do we optimize and improve their performance over time to keep up with the changing demands and preferences of the users?

Certainly! When evaluating generative AI-powered conversational bots, there are several measurable metrics to consider. Let’s explore some of them:

1. Self-Service Rate: This metric measures the percentage of user sessions that did not end with a contact action after interacting with the bot. A higher self-service rate indicates successful resolution without human intervention.
2. Performance Rate: The performance rate assesses the effectiveness of the bot in providing accurate and relevant responses. It considers factors such as correctness, coherence, and helpfulness.
3. Duration of Calls: For customer care bots, tracking the duration of calls generated by the chatbot (via web callback) provides insights into efficiency and user satisfaction.
4. Email Reduction Rate: This metric evaluates how effectively the bot reduces the need for users to send follow-up emails or seek additional assistance via email channels.
5. Escalation Rate: The escalation rate measures the frequency at which users escalate their queries from the bot to human agents. Lower escalation rates indicate better bot performance.
6. Conversion Rate: In HR contexts, this metric focuses on users who interacted with the bot and got their issue resolved with the self services. (e.g., less dependency on the human aspect).
7. Average Duration of Sessions: For users who engaged with the bot, tracking the average duration of their sessions provides insights into engagement and user experience.
8. Goal Completion Rate: In conversational AI, this metric reflects the percentage of interactions where the bot successfully achieves the user’s goal. For an HR bot, this could mean resolving an employee query or providing relevant information.
9. Customer Satisfaction (CSAT) Scores: Collecting feedback from users about their satisfaction with the bot’s responses is crucial. High CSAT scores indicate positive user experiences.
10. Contentment Levels: This encompasses various metrics, including successful interactions, lead generation, and contentment levels, providing an overall view of the AI’s performance.

While chatbots offer many benefits, such as speed, scalability, and consistency, they also have some drawbacks. They cannot emulate the human qualities of empathy, emotion, or innovation that are often needed for creating trust, managing complexity, or taking action. So, you have to be careful not to rely too much on chatbots and keep a balance between them and human agents to deliver quality service.