Echo Dot (3rd Gen) - Smart speaker with Alexa - Charcoal

Use your voice to play a song, artist, or genre through Amazon Music, Apple Music, Spotify, Pandora, and others. With compatible Echo devices in different rooms, you can fill your whole home with music.

Buy Now

Wireless Rechargeable Battery Powered WiFi Camera.

Wireless Rechargeable Battery Powered WiFi Camera is home security camera system lets you listen in and talk back through the built in speaker and microphone that work directly through your iPhone or Android Mic.

Buy Now

5 most important chatbot metrics to track

0
97


Source

Building and deploying a chatbot is one of the first steps towards providing better customer support and experience. After deployment, it is pertinent to track performance using metrics.

Beyond providing information about the performance of your chatbots, metrics help you to see shortcomings that could otherwise go unnoticed. For businesses intent on improving the performance of their chatbots, metrics uncover areas that need improvement.

While It is tempting to track as many metrics as possible, it is best to remember that data is useful only when it is indispensable in making a business decision. Taking this into account, what are the most important chatbot metrics to track?

  1. Total users: This metric offers insight into the popularity or acceptance of your chatbot among your customers. It is important to note that low usage does not always connote a chatbot failure. It could be as a result of several factors ranging from deploying your chatbot to platforms that your customers aren’t active on. It could also be a result of little or no awareness about your chatbot. To solve these, deploy your chatbots on messaging platforms popular amongst your customers. Afterwards, create a campaign promoting the use of your chatbot. When a leading bank deployed its Banking chatbot, they created campaigns that promoted its use amongst customers. It got to over one million users within 12 months.

2. Retention rate: While total users focus on the number of customers who have used a chatbot, retention rate deals with the number of customers who have interacted with your chatbot repeatedly within a time frame.

A high retention rate signals success. It means that your customers find your chatbot helpful in carrying out specific tasks. Nevertheless, this doesn’t eliminate efforts to make your chatbot better. Rather, it’s a call to find out what is working and continuously improve on it to prevent a drop in retention rate.

3. Goal completion rate(GCR): Every chatbot is designed to do a job. Goal completion rate measures if the chatbot is adept at performing the job it was designed for. If Goal completion rate is poor, it could mean that the process involved in completing the desired actions are lengthy or the chatbot’s design does not communicate the goal clearly to your customers. It is also wise to reassess the goal of the chatbot, to discern any weakness.

4. Conversation steps: Conversation steps reveals how many exchanges take place between your customers and chatbot before a goal is completed. Using this metric, you can provide better customer experience by removing unnecessary steps or conversations that slow down goal completion.

The ideal conversation length varies, influenced by the purpose of the chatbot. The conversation steps for an issue resolution chatbot is shorter compared to an eCommerce chatbot. Still, exchanges should be kept to a minimum, focusing on crucial steps that resolve customers’ issues.

When writing the conversation flow for each intent, focus only on the essential steps. The goal is to resolve a customer’s issue within the shortest time possible. Lengthy and unnecessary actions cause friction, making customers drop off before concluding the desired action.

5. Fallback rate (FBR): Fallback rate reveals a bot’s difficulties in answering a customer’s question. In its simplest form, it measures the number of times your bot displays “I don’t understand” or any similar phrase in response to a customer’s query. It also measures how often it hands over to a support agent when it is not expected to.

A high fall back rate indicates a shortcoming in the knowledge base. To solve this, refer to analytics and reporting for failed response and push the right answer to the knowledge base. Also, designing a bot that is supported by Natural Language Processing (NLP) reduces the fallback rate. It is able to recognize intents, regardless of how it is expressed, and deliver the right response.

For businesses focused only on mining essential data, these metrics provide an overview of your chatbot’s performance. They arm you with enough information to improve your chatbot’s performance, leading to higher engagement and return on investment.



Read More

LEAVE A REPLY

Please enter your comment!
Please enter your name here