Zoko provides AI assistants inside your chat in WhatsApp to solve specific tasks like take orders, answer Where IS My Order (WISMO) queries etc. Calculating the savings from deploying these AI assistants involves two key parameters: the time that the AI assistant works instead of a human agent and the typical hourly wage rate of a human agent in customer support in your country. This article will delve into how these savings are calculated, providing a comprehensive understanding of the methodology used.

Parameters for Calculation

1. Time Worked by AI Assistant

The first parameter involves determining the time an AI assistant spends on tasks that would otherwise require human intervention. This is calculated by considering the number of message exchanges in a chat and multiplying it by the average response time of a human agent, which we have determined to be 45 seconds.

2. Typical Hourly Wage Rate of a Human Agent

The second parameter is the typical hourly wage rate of a customer service agent. A detailed analysis chosen for your country is provided here - https://www.zoko.io/learning-article/how-is-the-agent-labor-rate-estimated . You may also set a custom rate if required.

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Example Calculation

To estimate the savings from employing an AI assistant, we multiply the time worked by the AI (in hours) by the typical hourly wage rate of a human agent.

Why This methodology is a Conservative Estimate

The savings calculated here represent a conservative estimate. This is because we have not considered several additional factors that could further increase the cost-effectiveness of using AI assistants:

1. Opportunity Costs: Delays in resolving customer queries can lead to lost sales and decreased customer satisfaction. A prompt response from an AI assistant can enhance customer experience and loyalty, potentially leading to increased sales and repeat business.
2. Scalability: AI assistants can handle multiple queries simultaneously without fatigue, unlike human agents who may experience slower response times under heavy workloads.
3. Operational Efficiency: AI assistants can work 24/7 without breaks, reducing the need for multiple shifts and overtime pay for human agents.
4. Error Reduction: Automated responses are less prone to errors compared to human responses, which can result in more consistent and accurate customer service.

By not accounting for these additional benefits, our savings estimate of $1.31 per query in the above example remains on the lower end, underscoring the potential for even greater savings and efficiencies when leveraging AI assistants in customer support operations.

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Justification for the 45-Second Average Response Time

Chatbot Response Time

Chatbots are designed to provide near-instantaneous responses to customer queries, typically within 1-5 seconds. This rapid response is one of the primary advantages of chatbots, allowing them to handle routine inquiries and provide immediate assistance without the delays associated with human agents​ (Social Dashboard)​.

Human Agent Response Time

In contrast, human agents, in our live chat SaaS product, have an average response time of approximately 45 seconds. This difference is due to several factors:

1. Processing and Comprehension: Human agents need time to read and understand the customer's question, think about the appropriate response, and then type out their reply. This process naturally takes longer than the automated responses of a chatbot​ (Pew Research Center)​.
2. Multitasking: Human agents often manage multiple chat sessions simultaneously, which can increase response times as they switch between conversations and ensure they provide accurate and personalized responses.
3. Complexity of Queries: Human agents typically handle more complex or nuanced queries that require thoughtful and often customized responses, which naturally takes more time compared to the straightforward, pre-programmed answers provided by chatbots.

Factors Contributing to the Differences

• Automation vs. Human Cognition: Chatbots operate on pre-defined algorithms and can instantly retrieve and present information based on programmed responses. Human agents, however, must process information cognitively, involving understanding the context, empathy, and sometimes problem-solving, all of which require additional time.
• Operational Load: While chatbots can handle an unlimited number of queries simultaneously, human agents are limited by their capacity to manage multiple conversations effectively without compromising quality.
• Response Quality: Human agents provide a level of personalization and context-sensitive responses that chatbots often cannot match. While chatbots excel in providing quick answers to common questions, human agents handle more intricate issues that benefit from a personalized touch and deeper understanding.

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