Which Metrics Best Measure Conversational AI Success?

Conversational AI is becoming an invaluable tool for brands of all types. Which metrics will help you best determine the success of your AI efforts?

Implementing and monitoring metrics is critical to measuring conversational artificial intelligence (AI) performance.

Self-service resolution rate, costs saved and positive customer feedback are some (but not all) of the top metrics CX professionals can look at to determine whether their conversational AI technology is connecting with customers.However, these key metrics heavily depend on the type of conversational AI application in use.

At a high level, there are three categories of metrics that organizations should consider:

  • Business-related metrics: Key performance indicators (KPIs) that focus on overarching business goals and conversational AI-related objectives.
  • User experience metrics: Focus on an experience that is useful, engaging and enjoyable, spurring users to return and/or recommend the product to others.
  • Technical metrics: Ensure the conversational AI product works and adheres to the requirements for performance or latency.

Build a Team to Craft a Metrics Roadmap

“To build a comprehensive list of metrics, key stakeholders across the organization need to come together,” said Inge de Bleeker, principal UX & conversational AI consultant, founder at outriderUX. “In terms of how to determine themetrics, a workshop or series of workshops that bring this cross-organizational team together is a good approach.”

For each of the three categories above, the individuals or specific teams in an organization will know which metrics they need to measure based on their goals.

“However, it is only when all teams come together that the comprehensive view on the set of metrics can come together,” de Bleeker added. “In larger companies especially, it can be tricky to create cross-organizational workingrelationships as silos can get in the way. Building a tiger team and raising awareness around the endeavor can be helpful.”

Amy Brown, founder and CEO of Authenticx, said that when identifying metrics to measure the success of your conversational AI platform, you should start by looking at your business objectives.

Are you focused on driving customer retention or avoiding attrition? Are you striving for a better net promotor score (NPS) but don’t know how to influence it?

“Select metrics that are likely indicators of your business objectives,” Brown said. “For example, if you aim to improve customer retention, make that your metric.”

Next, she advised, use conversational AI to study what customers are complaining about and make changes to address those complaints. “If you’re successful, you should see your customer retention rate increase.”

Related Article: How Will Conversational AI Transform Customer Experience?

Align Metrics to Business Goals

To define a comprehensive set of metrics that can realistically be obtained, one needs input from all corners of the organization, according to de Bleeker.

She pointed out that business and executive teams have certain goals around products — such as monetization — set by investors and board members. Product managers, on the other hand, understand the product vision and can determine howto measure things like consumption and conversion.

“UX professionals know which user experience metrics to gather so that user-driven feedback is gathered and distilled into metrics as well as getting self-reported user ratings,” de Bleeker noted.  “Engineers and QA (qualityassurance) can help gather performance, latency and other measures of a more technical nature to ensure that the product is performing as expected in production.”

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