Takeaways from Two Recent Rasa Production Stories

T-Mobile and nib share insights about building enterprise virtual assistants.

Visit the contact page for any of today’s digitally-savvy enterprises, and you just might find yourself talking to a virtual assistant. AI-powered virtual assistants have become a popular approach for companies seeking to drive innovation, deliver exceptional customer experience, and manage operational complexity.

We recently sat down with two Rasa customers, nib Group (nib) and T-Mobile, to learn about how they’re putting conversational AI in production.

nib provides health insurance for over 1.4 million members in Australia and New Zealand, and in 2017, the company launched nibby, a virtual assistant for customer service. In 2020, nibby served over 150,000 members, delivered product information in 4 languages, and provided critical information about Covid-19.

T-Mobile is the #2 wireless carrier in the US, with over 100 million customers. The AI @ T-Mobile team is responsible for using machine learning to address some of the most important challenges impacting customer experience, including reducing wait times. In July 2020, T-Mobile introduced a virtual assistant to assist Messaging customers, and 10% of T-Mobile customers now benefit from Messaging self-service.

Although T-Mobile and nib represent two very different industries, a few common themes emerged, highlighting trends and priorities that are shaping the way enterprises adopt conversational AI. In this post, we’ll take a look at 5 takeaways from these enterprise case studies.

Covid-19 accelerated the adoption of virtual assistants

Our first finding will come as little surprise for anyone who witnessed the turbulence of 2020. Covid-19 upended the status quo for nearly every type of business. One common challenge for businesses in the Covid-19 era has been responding to a sudden influx of contacts.  

For nib, this meant both an increase in member contacts as well as questions about a new topic: Covid-19. nib took a data-driven approach to help understand what members were asking about and formulate a strategy for handling a wave of Covid-related insurance questions.

T-Mobile also cited Covid-19 as a factor acutely highlighting the need for high-quality self-serve options. At the height of the pandemic, over 20,000 customers could be in queue to speak with a T-Mobile Expert, many with simple questions. T-Mobile’s virtual assistant allowed customers to get quick and personalized help for common questions, while allowing human T-Mobile Experts to focus on helping customers with higher-level requests.

Automation and customer experience go hand in hand

For companies like T-Mobile and nib, customer experience comes above all else. Self-serve options like virtual assistants hand control back to the customer, allowing them to complete tasks quickly, on-demand.

Lindsey McCarthy, Senior Technical Product Manager on the AI @ T-Mobile team, summarized: "We wanted to create value for our customers by giving them the option to complete simple tasks online or offer them information for simple questions."

nib’s virtual assistant, nibby, takes a similar strategy, increasing member satisfaction by pointing users to the answer they need. Over 50% of conversations with nibby are resolved by pointing the member to content or a self-service resource.

Contact routing adds significant value for users and businesses

For companies fielding millions of contacts, quickly understanding why the customer is calling and routing them where they need to go is vitally important.  

One of nibby's primary duties is ensuring members are routed to the appropriate team. This automated routing results in decreased handle times while eliminating the frustration of being transferred from call center to call center. nibby identifies members as international visitors, students, Australian residents, or healthcare providers during the first interaction, and nibby also sorts inquiries to sales or support.

T-Mobile’s virtual assistant automatically qualifies whether the customer’s reason for contact can be handled using self-service, using intent recognition. From there, the virtual assistant acts as a wayfinder, pointing the customer in the right direction. Some transactions can be fully resolved by the assistant, without transferring the customer to another channel.

Collaboration between design and development is key

Conversational AI teams are multi-disciplinary, even by the standards of modern software development. Data scientists, software engineers, conversation designers, product managers, and domain experts all work together to build virtual assistants.

For nib, Rasa offered a development flow that allowed developers and conversation designers to work together harmoniously. Conversation designers were able to write stories and create dialogues independently, without requiring developer resources. Dom Sammut, Engineering Manager - Conversational Experiences at nib noted, "With Rasa, it was super straightforward. I can give our conversation designer a list of the custom action names and they can drop them into the story."

Echoing the need for cross-team collaboration, Heather Nolis, Senior Machine Learning Engineer at T-Mobile, joined a recent episode of the Rasa Chats podcast to discuss how her team uses tools like Rasa Enterprise to create visibility between the development team and business stakeholders.

Transparency and customization top the list of priorities when selecting a conversational AI solution

Citing the ability to own and customize every aspect of their virtual assistant, T-Mobile emphasized the importance of bringing the development of their assistant fully in-house. The AI @ T-Mobile team had previously taken a virtual assistant to market built by an outside vendor, but found it expensive and difficult to manage. Taking full ownership of the project has paid off, and T-Mobile’s full-stack team has been able to increase velocity and drive improvement through continuous delivery, sometimes shipping updates 2-3 times per week.

nib’s conversational AI strategy took a similarly independent approach. Rasa's enterprise open source framework provided the freedom to build custom features on nib's timeline, rather than relying on a vendor's roadmap, and developers and data scientists had complete visibility into updates and functionality flowing into the product.

Conclusion

Customer experience today means giving customers personalized choices—about how and when they interact with a business. Companies like nib and T-Mobile illustrate the fact that natural language interfaces will continue to be a key part of how companies help customers solve problems.

Read the full case studies for nib and T-Mobile, and check out these additional resources to learn more about how enterprises are using conversational AI to shape customer experience: