The majority of B2C enterprises are now working on deploying Conversational AI products, for both internal and external facing use cases. With the relative immaturity of the industry, companies are still being caught off guard with the amount of work that can be required to deliver a successful virtual assistant or chatbot solution.
To speed up sales, many vendors are selling on a message of “this isn’t any work at all” — which is obviously not true. This is the snake oil of Conversational AI and is hurting the entire industry. …
For starters, they’re not about uptime or other traditional SLA metrics. You’ve implemented a Conversational AI to satisfy customers, and that is what should be guaranteed. You want to measure the output of the AI and how well it performed at its intended task. In the virtual assistant world, this translates to how often it understands and satisfies your customers correctly. As AI continues to advance, these core metrics will continue to impress.
For NLU accuracy, the amount of time your system will correctly understand the customer, you should expect at least 80%. Now, don’t get caught by the sneaky tactics of some vendors who will only offer a performance number for specific “use cases” — this number should apply to absolutely every customer interaction with your brand about anything at all. The next metric is a percentage of resolved customer issues before a transfer to a live agent is required. This is case dependent but should fall between 25 and 35%. Again, don’t get stuck on specific “use cases”, your customers will come with all of their questions and your virtual assistant should perform well across them all. …
For starters, they’re not about uptime or other traditional SLA metrics. You’ve implemented a Conversational AI to satisfy customers, and that is what should be guaranteed. You want to measure the output of the AI and how well it performed at its intended task. In the virtual assistant world, this translates to how often it understands and satisfies your customers correctly. As AI continues to advance, these core metrics will continue to impress.
For NLU accuracy, the amount of time your system will correctly understand the customer, you should expect at least 80%. Now, don’t get caught by the sneaky tactics of some vendors who will only offer a performance number for specific “use cases” — this number should apply to absolutely every customer interaction with your brand about anything at all. The next metric is a percentage of resolved customer issues before a transfer to a live agent is required. This is case dependent but should fall between 25 and 35%. Again, don’t get stuck on specific “use cases”, your customers will come with all of their questions and your virtual assistant should perform well across them all. …
How to shoose the right one for your company
The ability for your customers to contact you and converse with an AI before automatically connecting them with a live person is now considered a best practice in B2C companies. This wasn’t the case just a few years ago, but the benefits have become crystal clear. They include:
- A fast response, any time of day, every day of the year
- The ability to connect through many channels, Facebook, WhatsApp, Webchat, Alexa, etc…
- A consistent answer regardless of channel
- Higher conversion rates and lower care costs
- Customer’s expect this option to start, even if they end up with live support
As this market emerged many vendors piled in to offer a virtual assistant toolkit to allow companies to build their own. This is typical in the early stages of a new technology cycle when it is immature. Vendors initially provide tools, but not solutions. Many brands rush to test the new technology, and then reality sets in. …
About
