Are you thinking about AI Chabot integration to your website to improve your customer care, enhance the availability of online support, or get to know your audience better? Companies all over the world have seen trends evolve and while user expectations have changed, performance metrics have remained quite constant.
Here are some of the top key metrics to monitor your Chabot’s performance.
Self-service resolution rate
Self-service is also known as the deflection rate. It measures the number of users who had a conversation with the AI and did not continue chatting with customer service agents. This also determines the number of users that had conversations with the AI. This can determine the number of chats that the AI handled on its own and that your customer service agents did not have to. This helps to free up your agents to handle other issues and shows a clear measurable automation rate of the smart AI chatbot.
Cost saved with conversational AI
Your average cost in USD to solve one customer chat session is multiplied by the total number of self-served chats by the AI. This metric shows that the savings come from using conversational marketing platforms without which this cost would have been spent on human labor to answer FAQs. This not only saves the agent’s time but is spent on solving more challenging and complex problems which in turn can boost efficiency.
Positive customer feedback
When it comes to the percentage to the number of positive feedback with AI for each conversation, customers can leave positive thumbs up feedback or negative thumbs down feedback. Based on this user AI chatbot conversation, the AI gets its feedback, which is then totaled to get the number of positive customer feedback. The more positive feedback, the higher the rating the customers have with the AI which is clearly defined and measured directly on how customers have rated each solution. While most companies did not originally need AI to develop optical customer relationships or customer journeys, it has helped improve the entire process.
Wrapping Up
These different KPIs are sufficient to evaluate the ROI and the added value of your chatbot according to your initial goals. A regular monitoring will help you improve the effectiveness of the solution. However, these KPIs should not be the only metrics taken into consideration when evaluating the overall impact of the solution. Companies are now correlating these metrics with their pre-chatbot indicators which can help generate more conversation than before. This can help improve your return on investment in the long run and bring in more customers along with allowing you to boost your brand loyalty and status.
Senseforth is an AI solutions provider.
Provides Conversational AI platform to all industries, powers pre-built, omnichannel, and platform-independent bots.
I think it’s time we start creating a standard language to speak about the structure of conversations with AI Chatbot assistants.Web app UX features a common languageFor instance , in Herbie.ai we recently made the domain file editable within the UI.
The challenge was that your domain file contains the templates your assistant uses to send messages.
The UI already features a separate tab for editing those, so we might be duplicating information.But as a community, we don’t yet have the vocabulary to possess an identical discussion about the weather of dialogue.
We mention intents, entities, and actions, but those concepts are too detailed.Conversational AI needs a standard languageWe think it’s helpful to separate conversations up into user goals, where each user goal can mix and match reusable dialogue elements.Know more - https://www.herbie.ai
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