Project Overview

Leverage Artificial Intelligence to reduce the overhead of complex routing configurations to achieve target KPIs through an easy adoption method (Discover, Try, Buy) for a revenue-generating feature while demonstrating value.

DESIGN CHALLENGES

• Limited resources for gaining insights and conducting analysis
• Visualizing complex data in a simple, digestible manner
• Demonstrating the feature’s value
• Building confidence in the product without revealing the data modeling process

Business Value

Adoption of the predictive routing depends on the customer’s ability to draw continuous business value from it. Customers get a 30-Day Free Trial to test and establish the benefits of Predictive Routing before purchasing it.

The customer should be able to establish if the benefits identified during the onboarding phase are sustained once predictive routing is operationalized in their organization.

Onboarding

The team was able to analyze the current experience to find opportunities to enhance the Onboarding Process:

• Discover: Promote Predictive Routing both within the app and externally to potential clients
• Try: Provide a step-by-step trial of Predictive Routing
• Buy: Notify users of trial completion to generate purchase leads

(1) Promotion (2) Onboarding (3) Trial

Ongoing Value

There was a tremendous amount of value to offer a dashboard that provides global insights, with the capability to drill down to the queue level for more detailed data analysis in one view. This would help the routing administrator determine how successful the use of predictive routing has impacted the efficiency of their interaction volume.

Impact

Our ability to showcase how Key Performance Indicators (KPIs) allowed admins to see how predictive routing has progressed over time.

Explainability

An enhancement adding to the foundation of the predictive was the ability share insight in the model details . This also showed features of the AI model and how it has impact that on routing decisions.

Results and Key Takeaways

• With tight turn-around times to meet market demands for AI solutions, close collaboration among Product, Design, Design System, Analytics, and Engineering was essential.
• Understand the infrastructure that the experience is impacting
• Understand the infrastructure affected by the experience.
• Balance between providing “enough, but not too much” data to customers, demonstrating value without exposing the underlying framework.