Human-Centered Conversational AI Stories

Dental Provider IVR

This dental insurance customer had some specific requirements around the playbacks at this point in their provider IVR. They asked for particular fields to be played back, and wanted it as quick and painless as possible.

The dynamic playback was heavily dependent on the nature of the API returns and the table values.

This simple solution was exactly what the customer expected, a no-frills playback of dental history.

• Discuss the importance of tracking and analyzing video performance metrics, such as views, engagement, and conversions.

Fraud Case Study:

The problem

My large utilities customer had an issue with callers attempting to make check payments using fraudulent routing numbers. This needed to be fixed immediately. The customer provided a list of the routing numbers that needed to be blocked. They also needed a sternly worded message played when the fraud attempt was detected.

The team

We scrambled all the needed skill sets: a UI designer, a Spanish localization specialist, a developer, a QA lead, a speech scientist, and the recording studio. Very quickly, we identified all the details of the solution, laid out a plan for rapid design, implementation, testing, and deployment.

The solution

We added logic to the existing routing number collection to check the given routing number against the list of fraudulent numbers. We worked with the customer’s legal department to finalize the stern warning. We added logic to allow the caller another try to provide a valid routing number. We had all the prompts localized into Spanish, and had all the new prompts professionally recorded.

The outcomeThe turnaround time from reporting the problem to our team to the deployment of the solution, was 36 hours including all the change control restrictions on the customer side. The customer reports that there are no longer any instances of this type of fraud occurring on their automated system.

Life Insurance Case Study

The Problem

The customer, a large, well-known life insurance company, needed a complete overhaul of their antique automated phone system. They didn’t know where to begin.

Where to begin

I met with the stakeholders and we made a list of all the functionality they wanted, all their business rules around those functions, as well as details of the back-end values that were available for use. The customer had already a persona defined, as well as user archetypes. They also had a voice talent already contracted.

Whiteboard the design structure

Using the inherent structure of their business, we worked out a plan for the high level flow that would fit with the persona. Authentication determines the policy type, and each policy type has a unique subset of functionality. The idea was to build reusable functions that would be called when chosen from a given menu. After discussion and refinement, the business agreed on the approach.

Sample conversations

I put together sample conversations so the customer could get a feel for the overall experience. This allows the customer to see how their business rules would be implemented. This also gave us a chance to verify that the back-end variables were mapping to the right fields.

Complete the specification

Now that the customer liked the samples, the detailed specification could be written. While writing, I worked closely with the development team to ensure back-end accuracy, and with the QA team to ensure that all the use cases would be covered. The resulting documentation, including all the error handling and global behavior, was handed off to the development team.

Deployment

Because of the close communication between design and development, the project deployed without any major defects. The SIT and UAT passed without any problems. The first round of metrics showed that the containment rate doubled, reducing their call center expenses by several million dollars in the first year alone.

GoTo Chatbot

We lovingly refer to this bot as the MegaBot. It allows the same bot to be used across several domains.

It has been designed to handle requests for any of the 12 products in the portfolio, and retain the product context while it navigates the flows.

This bot also has answers for >80% of the support questions.One innovative feature is the automated help for joining a GoToMeeting or GoToWebinar.

The flow successfully talks the user through the steps to join a meeting or webinar, saving countless helpdesk tickets.Another innovative feature is the 'help me decide’ flow for prospects. GoTo has several products that have similar functionality, and users don’t always know what they want or need.

The user can select ‘help me decide’ from the product selection menu, and an alternate flow will ask more detailed questions to help narrow the product choice.

View Website - Tryout GoTo Chat Bot -https://www.goto.com/ 

Lastpass Chat bot

I designed the LastPass chatbot for 2 distinct audiences: prospects and existing customers. Prospects receive a light touch TOF flow, with larger businesses escalated to live chat sooner rather than later.

Existing customers get the support experience, covering >80% of support topics.This bot has several successful innovative features:

1) By far, ‘forgot master password’ is the most frequent request. The bot talks the user through 5 different methods of password recovery, leading to a 4-5% decrease in associated helpdesk tickets.

2) Data showed that users often asked to cancel their account, but only because of a forgotten master password.

The cancellation flow asks first if the reason for canceling is password related. If so, it runs the password recovery flow. Implementing this flow resulted in 8-9% decrease in cancellations.

3) Usability testing showed that new users frequently stopped using the product soon after installation because of a lack of instructions. The bot now has an on-boarding flow that talks users through several important features.

View Website - Tryout - https://www.lastpass.com/