As we approach 2024, the predictions of Gartner, Inc. have boldly outlined the trajectory for chatbots, projecting them to become the predominant customer service channel for approximately a quarter of organisations by 2027. This forecast isn’t just speculative; it emerges from the evolution of chatbots and virtual customer assistants over the past decade into vital components of service organisations’ strategies. According to Uma Challa, Sr Director Analyst at Gartner Customer Service & Support practice, when thoughtfully designed, chatbots can significantly enhance customer experience by fostering positive interactions at a reduced cost compared to traditional live interactions. Gartner CSS survey conducted in early 2022 supported this view. It found that 54% of respondents already leverage chatbot, VCA, or other conversational AI platforms for customer-facing utilities. This data highlights the current adoption rates and underscores the importance of measuring chatbot effectiveness through relevant KPIs to harness their full potential for improving customer service.
Since the benefits of chatbots are already proven and more and more businesses are adopting AI technologies every year, a new question arises. How do you measure chatbot effectiveness? What are the top chatbot KPIs? Let’s figure this out in the article.
All of us have probably communicated with a chatbot on our own, but how good was your experience? What about your company chatbot? How do you know if your bot is really helping customers? What are the main chatbot performance metrics?
1. Total number of users. If these numbers seem lower than expected, there may be a technical problem with your bot that prevents users from using it.
2. Number of new users. If the number of new users is decreasing, you may need to reconfigure the chatbot to make it more useful.
3. Goal completion rate. This number measures how successful your chatbot is at accomplishing its goal. If the coefficient is low and/or falling, you should reconsider the chatbot’s scripts.
4. Number of sessions initiated. This number refers to the number of times someone has initiated a conversation with a chatbot. Compare this number to the number of sessions completed, and you can see how useful the chatbot is to users.
5. The average number of daily sessions. Comparing this metric to other metrics, such as average daily traffic to your website, gives you unique data on how needed the bot is.
1. Total number of conversations. This number enables businesses to measure customer satisfaction level. We know by experience that the number should also increase with the growth of website traffic.
2. Agent Request Frequency. Monitor this data to determine whether the chatbot is succeeding in reducing the number of interactions with support agents.
3. Average Conversation Time. If this indicator is low or decreasing, then users probably consider chatbots ineffective.
4. Bounce rate. Sometimes the chatbot can’t understand a user’s request or answer a question. If this rate is high, perhaps you need to review your chatbot scripts or NLP parameters. For example, AI bots may have a non-response rate of 10-20%.
5. Interaction rate. This rate is the average number of messages exchanged during each conversation between your chatbot and a user. This is a key metric for understanding overall engagement.
The key performance indicators listed above are the main ones for tracking the success of any chatbot. In this article, we want to look not only at the basics but also at the more advanced chatbot metrics for each type of bot. Let’s go!
According to a study by Harvard Business Review and InsideSales.com, if a customer doesn’t get a response within the first five minutes, the likelihood of a lead being qualified as relevant drops by 400%.
However, with a chatbot, not a single lead will be missed. To assess the effectiveness of a chatbot as a lead generation tool, you need to compare the cost per lead and the conversion rate before and after running the bot. For fair results, compare the number of leads received from chatbots and registration forms.
X = (traffic cost + chatbot platform subscription cost) / number of leads
X — cost per lead after chatbot launch
% — conversion of traffic to lead after chatbot launch
% = (Leads / Traffic) * 100
If X is less than before the launch of the chatbot, it should be scaled to other channels such as Instagram DMs, WhatsApp, WeChat, etc.
If X is larger, consider increasing traffic and equalising the cost of subscribing to the chatbot platform. If additional traffic isn’t bringing in more money, it’s likely that the chatbot isn’t an effective tool for generating leads in a particular business case.
To assess whether it is profitable or not, you need to compare CAC (customer acquisition cost) with LTV (Lifetime Value, the profit a customer will make over the time he or she uses a company’s products or services).
However, the LTV and CAC are also useful in other cases because comparing them shows the return on investment and the effectiveness of the channel.
n = (cost of subscribing to the chatbot platform + cost of traffic) / number of leads for the reporting period) / (% of conversion from lead to payment)
n – CAC
LTV = average number of payments per life cycle * average bill
If n is less than LTV, that’s great, keep using the chatbot to generate leads.
If n is greater than LTV, you should increase traffic to recoup the cost of subscribing to the chatbot platform. If chatbot economics don’t match the additional traffic, it may be worth finding alternative channels for additional lead generation.
Any company’s hotline receives countless routine inquiries every day. But with an FAQ bot answering them, agents will have more time to deal with more complex questions. For example, The Cardiff insurance company’s bot automates 56% of incoming calls in the first line of support.
To calculate how much time a bot saves for agents, you need to use the following formula.
Savings = (number of correctly answered questions * agent’s average response time * agent’s average rate per hour) — (bot creation cost + service subscription cost)
If the value is positive, the chatbot can be scaled to other channels and expand the questionnaire.
If the value is negative, you should increase the number of questions that the chatbot answers and check the correctness of the answers.
Before hiring a new customer support person, consider automating the process. A metrology service call centre that provides residents with water metre calibration and replacement services decided to take the latter route.
That metrology service call agents handled 35-50 inquiries a day, while the bot handled more than 200 inquiries. The average agent’s salary is £2,300 while maintaining the bot costs about £200 a month. The bot, unlike employees, can also answer questions 24/7, and it never asks for a day off or goes on vacation.
Savings can be calculated using the same formula as for time
If the cost turns out to be greater than the cost of hiring, it makes sense to use a chatbot.
If the savings turn out to be less than the cost of hiring, you should also remember about staff turnover. Note, that the bot can pay off in 12 months.
It is crucial to establish KPIs to evaluate the performance of chatbots used for customer support. The following indicators can help identify areas for improvement and optimise the overall performance of the bot.
Among the most important KPIs for a customer support chatbot is the rate of successfully resolved issues without human intervention. A high issue resolution rate indicates that the chatbot is effectively functioning, and clients are satisfied with the level of support they receive.
Another significant indicator is the chatbot’s response time in processing customer requests. The bot must respond within a few seconds to avoid customers feeling neglected or disappointed.
Aside from issue resolution and response time, customer satisfaction is also a critical KPI. This metric can be measured through post-chat surveys or by analysing customer feedback and helps evaluate the overall effectiveness of the bot’s performance.
Measuring chatbot effectiveness is critical to evaluating their performance and ensuring they deliver value to both businesses and users.
While there is no one-size-fits-all approach to assessing chatbot success, several key considerations must be taken into account to make informed judgments and improvements.
1. Set a goal in numbers and measure performance before you start the chatbot so you have data to compare.
2. The money saved by using a chatbot is worth spending on improving the product or user experience.
3. Measure UX. In Google Analytics, you can see how many users come back to use the bot.
4. Provide the chatbot with traffic, such as through emails and Ads. This is the only way to measure effectiveness and make your business profitable.
5. Don’t overlook ethical and data security considerations. Ensuring that the chatbot operates in an ethical and responsible manner, respects user privacy, and avoids bias in responses is fundamental to its long-term success and user trust.
Regular monitoring and continuous improvement are essential to ensure that the chatbot remains current with evolving customer needs and expectations. As technology and conversational AI models advance, it’s imperative that the chatbot’s capabilities and performance evolve accordingly.
Measuring chatbot effectiveness is a multifaceted process encompassing various aspects, from goal alignment and user experience to data analysis and ethical considerations.
By taking these considerations into account and adopting a holistic approach to evaluation, organisations can develop chatbots that meet their objectives and build lasting relationships with their users in an ever-evolving digital landscape.
FAQs
How effective are chatbots?
Chatbots effectively handle routine inquiries, offer 24/7 support, and manage high query volumes, improving operational efficiency and customer satisfaction. Their effectiveness improves over time with advancements in AI and ongoing training.
How do we measure chatbot effectiveness?
Measure chatbot effectiveness by analysing response accuracy, user satisfaction ratings, response time, problem resolution rate, and the reduction in human-assisted inquiries. Continuous monitoring and user feedback are crucial for ongoing improvement.
What determines the success of interaction in a chatbot?
The success of a chatbot interaction is determined by its ability to accurately understand and respond to user queries, swiftly resolve issues, maintain user engagement, and ultimately enhance customer satisfaction and task completion efficiency.