Automatically resolve customers' issues
Prioritize urgent cases
Decrease waiting time and abandon rate
Detect sales opportunities
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On average, customers wait more than 2.4 minutes on the line before they are connected to a customer service agent. Only after reaching the call center representative, customers start describing their problem. This might lead to a further call transfer to another contact center department, thus creating a frustrating customer experience.
Call Automation bot uses an enhanced speech recognition model and machine learning that enables clients to describe issues in their own words and get a solution automatically without any human assistance.
Call Automation bot can be used with most of the languages and dialects. API connection and robotic process automation allow integration with any existing call center software and backend system, allowing to perform complex customer service processes.
Call automation bot can resolve more than 20% of customer service inquiries without any human assistance. The rate can even be higher depending on the industry and type of customer inquiries.
As customer service executives are under pressure to decrease their overall call center expenses, automation of calls helps them reduce operational costs in contact center.
Retention is a concern, especially for a customer service business. Take care of those who want to cut off the contract, and forward them to the right agent before they switch to your competitors.
Fine-tuned advanced NLP algorithms detect buying intent or clues thereof. AI forwards the call to the right agent or saves buyer data for a follow-up.
These contacts are either given higher priority when salespeople are available or added to a hot lead list so that salespeople can contact these potential customers as soon as possible. For example, AI will most probably pick up a fair amount of sales opportunities after regular working hours when people are free.
This component interfaces with call center software. It behaves as an ordinary software phone and communicates with the call center software over the SIP protocol.
As this is the component that handles the call sessions, it also coordinates everything that happens during the call, such as routing the sound to the ASR component, sending the text to the classifier and playing prompts and responses, forwarding calls, requesting information from the client’s systems, and performing other actions as required.
The AI component consists of speech-to-text and intent classifier.The speech-to-text system is a real-time speech recognition system which converts sound to text.
The classifier deals with intent classification – using the voice and text it helps to understand why the customer called so that we would know what needs to be done with the specific call. The identified intent is returned to the coordinated component which handles the call according to the defined business logic.
Technology is based on self-learning machine learning and AI becomes smarter and more accurate as more information is labeled.
Solution flows are the knowledge base for solving a customer contact. These enable defining what needs to be done with every intent. Every solution flow has a business owner who is responsible for creating, improving, and maintaining it.
Metrics give full transparency into the efficiency of the AI voice IVR
“MindTitan has been Elisa’s partner in developing several AI-powered solutions. What we value most about MindTitan is their enthusiasm about bringing maximum business value for Elisa by using the full potential of our data. From consultation to the presentation of a completed solution, the team always delivers on promises.”
“MindTitan is involved in shaping the long-term strategy for Estonia, giving an expert opinion about AI capabilities and possibilities for multiple public sector organizations and being a partner for developing the defined AI use cases.”
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