Metrics Matter: 5 Vital Indicators to Ascertain your Virtual Assistants are Performing Well

As artificial intelligence (AI) gains a firm footing within the digital landscape, businesses are having to realign their strategies to keep up with the newest trends. One of the phenomena that has taken the world of e-commerce by storm is chatbots or virtual assistants (VAs). These innovative AI-powered solutions have quickly become a useful tool for improving customer experience.

But what makes VAs so popular? Quite a few:

• 24/7 customer support
• Instant answer
• Automated bookings of orders
• Lowered customer service costs
• Increased customer engagement
• Elimination of human errors

While VAs are quite amazing, just having them in your arsenal or embedding conversational AI software isn’t enough. In order for your business to thrive, you need to meticulously analyze the VA success rate and strategize their use accordingly.

Draw out your KPIs

If metrics is your measure of VAs’ performance, then some indicators that you should possibly pull out are:

• Customer retention rate
• Conversion rate
• Customer support savings
• Number of active users
• Number of new users using bots (per day, week, month)
• Number of initiated bot sessions
• Total sale value
• Cost of operations and maintenance

The average values will give you some indication of your current measurements, VAs’ performance, and customer engagement. However, a more valuable exercise would be of dedicatedly tracking down a few specific VA metrics.

1. Customer Satisfaction after Virtual Assistant Interaction

The end goal of any VA implementation is increase in customer satisfaction, and thereby customer experience. Therefore, this is the first metric you should focus on.

You need to determine to what extent the customers are satisfied with the service after their interaction with VA is concluded.

The best option to test this metric is to give customers a chance to rate the conversation. You can use a 5-star scoring system which makes the analysis of results pretty simple. However, if you want to get more specific feedback use a more complex survey where the customers can express their opinions and suggestions.

In order to motivate the customers to partake in the survey, you should offer them some sort of incentives such as a coupon or a discount for a future purchase.

The results will give you clear indications of opportunities for improving chatbot comprehension.

2. Comeback Rate

Besides the survey, there is another way to determine the effectiveness of VAs.

Identify the consumers who had used the VA and decided to reuse them on their second visit to your website. Also, identify those who had used the chatbot once but immediately turned to the live agent the next time.

“When a customer likes a certain service, they will instantly go back to it. If he doesn’t, he will avoid it. It is as simple as that. That is why you need to focus on whether the consumers go back to chatbots once they've already given it a try, or they default to an agent,” explains Johnathan Perry, the head of the customer service department at the legal translation agency TheWordPoint.

This is a good start to check if your customers have accepted VAs, especially if you aren’t ready to conduct a more elaborate survey.

3. Self-sufficiency

The more conversations virtual assistants solve on their own, higher will be the customer satisfaction. If the VAs provide customers with the right answers, they won’t need to be transferred to the live agent.

What you need to analyze is the self-service completion rate and the point at which the conversation is transferred to an agent.

If you recognize a pattern of situations where customers’ interactions with the VA are having to be transferred to a live agent, you can focus on changing that part of the script and providing a better solution.

4. Conversation Analytics

Pay attention to the flow of the conversation to examine the success metric of the VAs. If the user needs to repeat the same question several times, their satisfaction level will go down.

People like to get solutions to their problems fast, but if they need to repeat themselves and waste time on incomprehensible conversations they will quickly give up.

To measure the conversation breaking point, you can focus on the bounce rate or the prematurely ended conversations. Look back at the conversation’s history and try to find the weak points.

Those who use voice-activated VAs can even analyze the tone of the consumer's voice. Detect the consumer's comprehension of the VA’s responses and questions and identify if any frustration arises in the customer’s voice.

5. Chatbots as Sales Agents

Chatbots can be great sales agents. By initiating the conversation with the consumers, asking about their preferences, and making suggestions, they can encourage website visitors to become buyers.

Based on conversions that resulted after an interaction, you can have an understanding of the quality of sale service that VAs provide.

You can also match the product recommendations that the VA offered as an alternative to the customers’ chosen products. If it turns out that chatbot’s recommendations are effective, you are providing a satisfactory customer experience.

However, in case that chatbots don't have good sales results, you should think through this strategy.

Points to Ponder

The frequency at which the users turn to the conversational AI platform, the VAs’ effectiveness, and the overall user satisfaction are all indicators of VA success. The end goal is to perfect the VA to the extent that customers start using it by habit. This habit can only arise from consistently satisfactory interactions with the VA.

Metrics are an effective way of deciphering the VA performance, with huge implications for improving your overall customer experience. Considering the benefits that VA can bring to your business, investing time and effort into these processes will certainly be worth it. It bodes well to remember that AI is our future and in order to stand out among the competition you need to stay ahead of the innovation curve.

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