Call Center Automation Using Artificial Intelligence, decoded!
The process behind Call Center Automation is indeed no piece of cake. But, knowledge of certain concepts, in brief, can be pretty helpful to get quite a gist of behind-the-scenes action for better customer engagement. AND, all the performance parameters can be automated using Machine Learning/Artificial Intelligence (ML/AI). Here’s a list to bring forth those related concepts in a short, uncomplicated (or maybe just a little complicated) manner:
- Dead Air Time or Non-Talk Time: It’s a common term used to define intervals of quiet during a customer-agent conversation due to reasons like; slow functioning of the software, knowledge gap, complicated queries, etc.
- Cross Talk Time or Overlap Time: It is the term used for the time lost when either the agent or the customer talks over another, bringing down the efficiency of a call. Broadly speaking, it happens usually when the customer is frustrated or angry.
- Net Promoter Score (NPS): NPS refers to the metric which aids the assessment of customer satisfaction and gauges how likely a customer is to recommend a product or service to another customer.
- Call Driver: It’s the reason why a certain call lands in a call-center.
- Rate of Speech: It is how swiftly or slowly one talks per minute. A rate of speech that matches that of a customer on the line, coupled with a complementary pitch and tone is ideal for better interaction.
- Average Handling Time (AHT): AHT is the metric used in call centers to time the average duration of one exchange or transaction from the beginning of the call to its end. It’s inclusive of holding time and any other follow-up tasks post-transaction.
- Speech To Text (STT): It is the capability of software that transcribes the audio composition of calls into worded content. It spares the hassle of manually writing or typing by quite a margin.
- Natural Language Understanding (NLU): NLU is a domain of Natural Language Interpretation (NLI) that conveys the ability of software to understand and draw insights out of human conversations.
- Text to Speech (TTS): It is a feature of the software that aids the conversion of written text into speech output.
- Quality Monitoring Process: It’s the process of evaluating the telephonic conversations and grading them on the basis of related criteria (time taken to address the issue, overall tone of interaction, etc.) to improve customer service.
- Objection Handling: Often a challenging issue, it is when the prospective customer brings forth a concern about the product or service and how the agent handles it for the deal to progress further.
- Script Adherence: It’s how well the agents or the bots comply with the most potent script and responses to custom situations in order to boost customer engagement.
- Speaker Diarization: It’s the process of fragmenting input audio content into corresponding fragments in accordance with the speakers’ turns.
- Speaker Identification: It’s a feature of the Speech Recognition Software which helps identify a certain speaker’s voice and related data for better analysis.
- Voice Analytics: It is the use of a certain voice recognition tool in order to analyze a recorded spoken conversation.