A bit ago we did some scream therapy and admitted to ourselves that everyone hates our chatbots (and we do, too). With that refreshing realization, we eked out a map to something better, including the critical features and capabilities to get us there.
As more and more companies bring chatbots, virtual assistants, and other conversational apps into their business processes and workflows, it will become increasingly important to show how these technologies are providing the desired return on investment and increase in metrics.
How do you start to measure the success of a conversational application?
Here’s a quick rundown (and a final warning) of what to include as you start to prove the ROI of conversational apps.
Customer-Facing Metrics for Conversational Applications
Conversational apps that are customer-facing have to measure specific outcomes, often related to existing metrics for conversion and customer satisfaction. Tracking across the entire customer journey: browse, search, cart, purchase, delivery, service, support, and beyond.
- Customer acquisition cost can be improved by automated technologies that get customers engaged faster without needing a salesperson to get involved. Chatbots can be the first line of offense in getting frequently asked questions answered and pointing customers to recommended products and services. This can improve sales velocity and conversion rates.
- Call deflection rate is probably already being measured by your customer support organization. This measures how well applications and resources outside the contact center help customers resolve their own issues without engaging an agent. You’ll want to measure overall volume in support tickets before and after you launch or upgrade any chatbot or conversational apps. The percentage of tickets that end up going to the support center for a human to work on, versus how well the conversational app helps customers answer their own questions.
- Resolution time is probably also already on the list of support metrics you track, so you’ll want to be sure to measure how resolution time changes as customers use chatbots or other apps in the support process.
- Customer satisfaction scores should be integrated into your metrics and measures. Many companies use the popular Net Promoter Score as a starting place and this survey can be implemented into the chat stream experience.
Measuring Converational App ROI Inside the Organization
And similarly, you will need to measure the impact of employee-facing conversational apps to see how they improve existing internal metrics on how they make employees more efficient.
- In addition to borrowing from the contact-center related metrics above—call deflection and resolution time—internal IT departments and HR teams should measure how well the conversation apps are answering questions from employees.
- App utilization is another important metric. Internal employee-facing applications have to prove their worth and make the case for their original budget and future investment. Utilization is usually measured by the number of active users in the last 30 days. You can also report this as a percentage of a department or the entire organization for mandated applications and solutions.
And a quick warning:
Don’t forget to baseline! As you deploy conversational technologies, be sure you have at least a year of baseline data—your “before state”—so you can measure the success of your upgraded conversational app and compare it to the previous solution.
By integrating with existing metrics and measures in your customer support and IT organization, you should be able to establish a strong case for the success or your conversational apps and how they make life better for your customers and employees.
Contact us today to learn how Lucidworks can help your team create powerful search and discovery applications for your customers and employees.