A NEW WAY TO TROUBLESHOOT
In 2008, I worked as part of a cross-functional team dedicated to resolving weaknesses in the troubleshooting process in Verizon retail stores. We created a troubleshooting wizard to replace a representative's need to understand all potential technical issues and solutions for defective devices.
My role within this large design team was to act as the expert on the employee and customer experience, partner with other stakeholders to develop enhancement requirements, and test all changes prior to release. I led a team of consultants through this process for our area (12-state region with 400+ stores).
When a customer's device stops working correctly, they visit a store for assistance. A customer's store experience would vary, depending on the knowledge level of the rep who helped them. If a rep was experienced, the troubleshooting process was fairly quick, and the customer was on their way without much of a wait and likely had their root problem solved. If a rep was inexperienced, the troubleshooting process could be quite lengthy—potentially involving a lot of reading or asking other employees for assistance.
The challenge was to imagine a system solution that would guide the user through the troubleshooting process, reducing the need for external assistance—from knowledge management system articles to sourcing help from other employees—and increasing the accuracy of device problem diagnostics.
The constraints for the project were:
to align the new functionality design with the pre-existing design system;
to ensure the system-provided course of action aligned with all existing policies and procedures and compliance requirements; and
to create a product that was simple and easy for inexperienced reps to use.
To fully understand our customers and representative end users, I used a combination of quantitative and qualitative research methodologies. My team mined NPS data to understand customer pain points. We conducted regular visits to retail locations to interview employees and observe their application of the troubleshooting process with customers on-site.
Key Insights from Our Research
If the rep had to ask another employee for assistance, this tied up two employees helping one customer, impacting wait times.
If the rep tried to find the solution on their own, they had to spend significant time reading through technical bulletins and troubleshooting steps.
Customers were often sent away with a solution, only to find out after leaving that the problem persisted.
The list of problem codes for reps to select in the POS software required pre-existing mental models, which new employees did not have.
The transition for a rep to go from inexperienced to experienced in troubleshooting could take a year or more.
IMAGINING A NEW WAY
When envisioning a new flow for the troubleshooting process, we wanted to make sure that reps who were brand new could assist customers without much prior troubleshooting knowledge. The answer to this problem was to create a troubleshooting wizard that would guide users through assisting a customer. We began by taking the list of codes used when performing a warranty exchange and working backward from there to create a database of if/then scenarios. A list of potential solutions to each customer problem was identified, and the solutions were ranked by order of what you should try first.
**The following are not actual pictures from the software but give you an idea of what the employee experience is like pre- and post-change.**
Before the software change, several tasks must be completed accurately, from memory.
The rep must:
Understand the problem based on prior knowledge or from knowledge gained from reading through the Knowledge Management System articles. If they can't find the solution, they either ask another employee or switch out the phone under a warranty exchange (whether or not that would actually fix the problem).
Choose the correct course of action based on the data given to them by the customer, the results of tests performed, and their understanding of the problem.
If switching out the device for a warranty replacement, they must select the correct exchange code from a list. The code selection required the user to have an existing mental model for each item.
After the software change, the user simply had to walk through a question and answer wizard to determine the correct course of action. Beginning on day one of employment, a representative could walk through the troubleshooting steps based on the customer's problem and arrive at the correct course of action. Based on the answers the user selected, the system codes for the exchange (if needed) would be automatically selected, reducing human error. Automatic comments to the billing system saved reps time from having manually enter visit information.
USER ACCEPTANCE TESTING
After developers coded the software release and before it went live, my team conducted User Acceptance Testing (UAT). UAT involved completing all possible transaction types in a test environment to identify issues with the expected performance of the troubleshooting wizard and problems with the code. Once UAT was complete, the code went live in the point-of-sale environment. On the morning of the release, my team performed the same tests in the live environment to ensure the code was copied over correctly.
The results of the troubleshooting wizard enhancement were that representatives could effectively and efficiently help customers from day one on the job, with little training as to the technical functioning of devices. We solved a major problem that contributed to a negative customer experience in our retail stores.
Our project was projected to save the company $16M per year*
With this change, we created a concise path for all employees to assist with technical issues.
Benefits of the change
Consistent experience regardless of the experience level of your representative
Reduced wait times
More accurate resolutions
Improved accuracy of coding of returned, defective devices
Less ramp time needed for new employees
*Projected savings vs. actual numbers because I left Verizon shortly after the project launched and before we had real results. Projected savings were calculated based off of predicted support cycle time savings, anticipated reduction in incorrect warranty exchange device costs, and labor savings from duplicated efforts.