Customers come to you with questions.
We help you provide the right answers.
Let’s dive in and see how:
Let’s dive in and see how:
AnswerIQ’s technology identifies patterns in ticket classifications from the past and predicts which group or individual agent should receive each new ticket. Tickets are routed within minutes to the most highly-suited available agent. This way your best agents don’t have to spend their precious time on this process.
In the case of Thumbtack, AnswerIQ Triage reduced their assignment times from 3 hours down to 3 minutes while meeting similar confidence ratings as their support agents. Case study
As new support tickets arise, AnswerIQ uses artificial intelligence to recommend an appropriate response template and give customers the right answers with as little wait as possible. We provide agents with the top three macros and templates, agents pick the best one, personalize it, and send it along. Using Recommended Response, even your newest agents can deliver consistent solutions quickly and efficiently.
Product Madness leveraged Recommended Response to reduce their agent case handling time by nearly 40%, while reducing their time to full resolution by over 60%. Case study
AnswerIQ enables you to automatically respond to tickets once you deem it appropriate based on chosen confidence criteria and comfort level. Password resets, standard refunds, tracking packages and other regular resolutions are subject to automation. Automatic response allows agents to spend less time on recurring issues or repetitive tickets, and more time on problem solving for your customers. We also allow agents to bulk respond for repetitive tickets with a human touch.
AnswerIQ generates several key observations through its learning process to help you reflect on and inform changes. We show you what macros are a good candidate for response automation, provide a macro report and recommendation tool, and offer ticket routing and calssification suggestions.
AnswerIQ natively integrates directly into CXM systems and automatically generates machine learning models to mimic the decision paths of how agents handle cases. It works across the spectrum of automation needs from augmentation to full automation.