Feature

Einstein Article Recommendations is a Salesforce feature that saves agents time and improves accuracy with intelligent article recommendations for cases based on the case information. 

Challenge

I worked on developing the onboarding experience and dashboard that admins would set up and manage within their organizations. A key challenge was designing an intuitive interface that would streamline the problem-solving process for customer service agents, thereby increasing their productivity. Another major addition, informed by user testing, was incorporating different testing environments for admins to ensure the effective implementation of the feature. 

Outcome

This feature is now live and utilized by hundreds of companies, significantly enhancing the productivity of customer service agents. The different testing environments allow admins to fine-tune the implementation process, ensuring optimal performance. The project underscored the value of the design process in uncovering unknown user needs and shaping the design to meet those needs, resulting in a more efficient and user-centered solution.

What problem is EAR solving?

When agents assist customers with issues, there's often existing solutions or articles that can aid them. This feature utilizes AI to surface articles by proactively identifying key words, phrases and numeric fields in agent-customer communications to provide the agent with additional support. The admin can manage the AI model to test accuracy and refine the settings as needed. 

2019 Internship Project

Based on insights gathered from user persona interviews, I identified pain points and collected user needs. Understanding the priorities and goals of the admin, I designed an onboarding experience for setting up an AI model to recommend articles to agents. Additionally, I developed a dashboard that enables the admin to evaluate the model's effectiveness and adjust suggestion sensitivity.

Setting Up the Feature

When designing the onboarding flow, I focused on setting clear user expectations right from the start. This included outlining the time required, the necessary information from admins, and addressing potential points of hesitation directly on-screen with actionable feedback. 

Features from Insights

Based on insights from admin interviews, I developed a feature that addresses their desire for a zero-consequences testing environment. This allows the admin to thoroughly test the AI model without any real-world impact, ensuring it is properly set up before deploying it organization-wide.

Design Process