Why You Should Monitor These Important Chatbot KPIs

Key performance indicators (KPIs) are often used to monitor the success of software systems such as customer relationship management (CRM) applications and point-of-sale (POS) platforms. However, KPIs are just as critical when discussing the effectiveness of the latest generation of chatbots. So what are the most important chatbot KPIs to track, what insight can these analytics provide, and how can they lead to improved conversion and customer satisfaction?

Generative AI chatbots are quickly becoming table stakes within e-commerce customer service. In fact, nearly 70% of all conversations between a business and a shopper are now handled from start to finish with the help of these chat platforms.

As this figure represents data from 2019, the actual percentage is likely much higher. This is why appreciating the role of key performance indicators is critical for those who wish to enjoy long-term success, and to keep their shoppers happy.

Understanding Chatbot KPIs: Why They're Crucial for Success

What are Chatbot KPIs?

Chatbot KPIs (and KPIs in general) can be defined as quantitative indicators of how a piece of software is performing. The main intention is to identify what's working, what's not, what needs to be totally changed, and what specific features can be enhanced to improve the experience for users.

This is a two-fold strategy, as key performance indicators can help to enhance customer satisfaction while simultaneously enhancing in-house efficiency.

How KPIs Help Improve Generative AI Chatbot Performance

KPIs can be used to enhance performance in a number of ways. Common examples may include how often the bot is voluntarily used by shoppers, the proportion of customers who engage with a bot multiple times, the types of questions that the chatbot is capable of addressing, and overall resolution rates.

These, and other similar metrics, can be a greater indicator of performance for businesses, offering a level of oversight that isn't always possible with customer feedback alone.

LLM Chatbot Evaluation Metrics and Their Growing Importance

AI-enabled chatbots powered by large language models (LLMs) have taken center stage in terms of customer interactions, as they can generate and comprehend human-based text queries.

These tools are becoming more prevalent in the e-commerce industry because they allow a company to offer round-the-clock support in natural language, serving visitors inside and out of most businesses traditional hours.

However, even this level of advanced generative artificial intelligence (AI) requires intervention on occasion. Proactively evaluating the effectiveness of a generative AI chatbot is pivotal to realizing all of the benefits of this channel.

Chatbot Accuracy Rate: Ensuring Correct Responses

The success of any chatbot within the e-commerce industry is largely based on how accurately it can respond to human questions, and offer a positive resolution. How is this factor measured, and what variables might impact the results?

What Is Chatbot Accuracy Rate and How Is It Measured?

One of the best ways to determine the accuracy of a bot involves a KPI known as intent recognition. Although this term is based in psychology, it applies to customer service.

Intent recognition is a measurement of the rate at which a chatbot correctly interprets user intent. In other words, how frequently a bot can provide a targeted solution to a customer query.

Factors That Affect Chatbot Accuracy Rates in Generative AI Models

There are many variables that can influence the accuracy of chatbot responses. For instance, hallucinations can occur if the bot isn't fed its information from secure brand-owned sources. If it has access to the broader internet and can't find the answer from it's designated sources, it may attempt to incorrectly improvise, resulting in a poor end-user experience.

To ensure a high customer satisfaction rate, chatbots should be integrated with guardrails in place. These will decrease the frequency of inaccurate responses. Other factors could include:

• The sources of information provided by the business
• The set of instructions (prompts) that the bot can access
• The level of creativity (sometimes known as the temperature) of the generative AI model
• How often the data is proactively updated (such as information regarding a new product or service)

Containment Rate and Automation Rate: Reducing Human Intervention

Chatbot Containment Rate: Definition and Importance for Generative AI Chatbots

Automation is the name of the game when it comes to efficient user interactions and resolutions. This is why another important KPI is the containment rate. Containment rates are ways of determining the percentage of shoppers who will leave the conversation without requiring any type of assistance from a human agent (meaning the bot solved the problem).

This is in direct contrast to fallback rate (when the bot fails to provide a resolution), and the amount of instances when a shopper will need to be transferred to an agent for additional support.

What is Automation Rate?

Automation rates are also critical chatbot KPIs for e-commerce businesses to keep a close eye on. The automation rate measures how often a chatbot is able to successfully handle repetitive tasks and routine requests. This is another way to determine its level of automation and overall efficiency.

Industry Benchmark for Generative AI Chatbot Automation Rates

Higher chatbot automation rates will naturally equate to a greater return on investment. For example, fashion brand, IKKS, was tasked with finding a solution to handle their steadily increasing chat volume by providing accurate automated responses.

With the help of the latest generative AI chatbots, IKKS achieved 80% of all conversations being fully or partially automated, freeing up human agents to focus on more complex issues.

First Contact Resolution: Solving Issues in One Go

What is First Contact Resolution?

First contact resolution (FCR) is another chatbot KPI that can help determine channel efficiency, and be used to illustrate satisfaction rates. FCR is simply a way to describe how often a query or issue is resolved with a single chatbot interaction.

How FCR Affects User Satisfaction and Efficiency

The relationship between first-contact resolution and satisfaction is clear. Shoppers who are able to solve their problem or get an answer to their question quickly are more likely to complete their purchase. This increases the chances that they will continue to patronize the business in the future.

Response Time and Average Handling Time

Why Response Time and AHT Are Crucial KPIs

How many sessions has a chatbot handled, and what was the average time per session? This is another way of describing a metric known as average handling time (AHT). When combined with how long it took the bot to respond once a session was initiated, this KPI can shed further light on the efficiency of the software.

Balancing Speed and Quality in Chatbot Interactions

While AHT is obviously important, quick answers are of little use if they aren't accurate. If the quality of responses suffers, fallback rates and human intervention will inevitably rise, so it's important to strike a balance between speed and quality.

User Satisfaction and Sentiment Analysis

Measuring User Satisfaction Through CSAT and Positive Feedback

CSAT is an acronym for customer satisfaction score, and arguably one of the most important KPIs. Businesses will need to use analytics such as client feedback and sentiment analyses to better determine the percentage of shoppers who are satisfied with their automated interactions.

Note that CSAT scores and conversion rates are directly related to one another. For example, after pop culture brand, Sideshow, integrated generative AI chatbots into their customer service model, they reported a five-fold increase in conversion rates via automated shopper assistance.

How Generative AI Chatbots Adapt to User Feedback on Answers

One of the strengths of generative AI chatbots involves their ability to predict customer questions and actions based on previous data sets. In other words, these algorithms have developed the ability to learn from prior experiences.

When combined with the other KPIs mentioned above, businesses now have a powerful tool to further hone responses that can lead to higher resolution rates. If customers require further help, they can still be redirected to a human agent.

Business Impact KPIs

The following three KPIs have a direct impact on ongoing business operations, so it's important to be familiar with them.

Cost Per Conversation

The ROI of any chatbot will at least partially depend on how much each conversation costs. This metric will then be factored in so that future actions can be taken, such as providing a larger dataset to ensure accuracy or to reduce the average time per interaction.

Conversion Rate

How many successful resolutions resulted in a conversion? This sheds clear light on the effectiveness and revenue-generating power of the chatbot.

For instance, online eye wear retailer, Payne Glasses, increased their conversion rate by up to ten times after integrating the latest software solutions with their existing CRM and tech stack. 

Return on Investment

ROI can be considered the "golden rule" of any chatbot. Maximizing the percentage of interactions that are resolved by the bot without requiring human intervention can save a significant amount of time and money, resulting in a higher return on investment.

Embracing Generative AI Chatbots to Gain a Competitive Advantage

Current research indicates that the generative AI chatbot industry is expected to grow 23% between now and 2030. As the percentage of users who gravitate towards these automated solutions increases, businesses will need to remain well ahead of the competition.

Smart e-commerce shopping solutions, enhanced in-house efficiency, and personalized services at scale continue to define this industry. Online shoppers are simply no longer satisfied with anything less.