Enterprise contact centers and service desks face high operating expenses primarily due to enormous staff and training costs. Chatbots are a convenient option for them to support their help desk and other customer-facing teams. They can enable end-users with self-serve features, reducing help desk workloads and improving employee productivity. As customers demand faster resolutions, self-serve options become a necessity.
Advances in AI, particularly Natural Language Processing (NLP), have enhanced chat and voice bots convertibility, query resolution capabilities, and user experience. Today, chatbots have transitioned to intelligent, conversational-driven’ Virtual Assistants’ from command-driven apps which are adept at determining context and user intent.
Based on the degree of intelligence and use case, chatbots’ capabilities vary from answering questions to servicing an employee. Chatbots can be broadly classified as:
Before developing chatbots, enterprises need to define the bot’s functions. Architecting a chatbot requires an in-depth understanding of
The chatbot must be human-like to drive effective conversations and improve user experience. An industry-specific chatbot is an ensemble of multiple models working together to understand the language and context.
Chatbots have to do all of the above without giving themselves away to a troubled employee/customer reaching out to the help desk. This is a tough task but the right implementation partner can make it happen. Let’s understand it better with an implementation by Quantiphi for one of the largest energy holding companies in the United States.
One of our customers, a North Carolina-based Electric Utilities and Infrastructure business company that serves over 7 million customers, wanted to enhance their support user experience and the associated internal processes while reducing support headcount and redirecting its technology team.
Before chatbot implementation and help desk process modernization, the customer was dependent on the support team to manually resolve internal queries. It was a time-consuming process where all employees had to call a helpline number for support-related tasks such as resetting the password and opening an account. The agents then resolved the query on the call itself. For complex requests, they manually updated support forms with information gathered from the user and created a ticket.
Extensive manual work and voluminous support issues led to errors and affected operational efficiency. There was also a lack of correlation for real-time updates between various client internal systems, including their Azure environment, BMC Remedy and LDAP. Changing the existing process was tedious due to security concerns, with limited APIs available, and even then, it needed custom efforts and third-party services.
Quantiphi created a natural-language-powered conversational virtual assistant with text and voice capabilities using various AWS services to support the client’s Help Desk team.
The solution enabled the client to self-service its employees for support-related queries 24/7. It was scalable, consistent and cost-effective and reduced the need for support staff to address questions. Approximately, 33000 support queries are projected to be resolved by the bot with minimal intervention annually.
We continue to build further integrations and develop extensions with their other internal applications. We are also enhancing the existing solution to address additional use-cases for the Enterprise Helpdesk teams.
Quantiphi’s Contact Center Transformation practice transforms the client’s contact center landscape into a scalable cloud-based enterprise through the power of AI-driven Chatbots and Voicebots. Quantiphi’s solutions add value to the existing workforce by addressing multiple use-cases whilst seamlessly integrating with existing systems.
Get in touch with us to transform your helpdesk and maximize value with customer interactions with conversational AI.
Contributed By: Varun Tewari, Suraj Vantigodi