An AI tool that transcribes and analyzes every customer support call. It uses NLP to identify common complaint themes, agent performance metrics (e.g., empathy score), and emerging product issues mentioned by customers.
Support calls contain some of the richest customer feedback a business gets, yet almost none of it is captured. Team leaders listen to a handful of recordings each week for quality checks, and product teams hear about problems only when complaints escalate. A recurring fault or a confusing billing change might come up in many calls before anyone joins the dots, because each agent only sees their own conversations.
AI call analytics transcribes every recorded support call and analyses the text. It groups calls by reason, detects sentiment and frustration, spots emerging terms such as a product name paired with a fault, and measures agent behaviours like greeting, verification and empathy. Product teams get early warnings, operations can see why customers are calling, and team leaders can coach using real examples. Quality assurance shifts from random sampling to looking at the calls that matter.
Recorded calls are pulled from the phone system or contact centre platform, along with metadata such as queue, agent, duration and outcome.
Speech-to-text converts each call into a transcript with speaker separation, tuned for Australian accents and your product vocabulary.
NLP models classify call reasons, extract products and issues mentioned, score sentiment across the call and check for required process steps.
Dashboards show call drivers, trending issues and agent metrics, with alerts when a new issue appears or a topic spikes.
Team leaders and product managers review sample calls behind each insight before acting, and coaching notes link directly to the relevant moments.
An electronics company's AI analyzes thousands of support calls and discovers that 'battery life' comes up with negative sentiment far more often for a specific new product, providing an early warning to the product team.
Speech-to-Text, NLP, Sentiment Analysis, Conversation Intelligence Platforms (Gong, Chorus)
Recording calls is regulated by state and territory listening and surveillance devices laws, and consent requirements vary, so a clear message at the start of each call that it may be recorded and analysed is the standard safeguard. Transcripts contain personal information under the Privacy Act, and payment card details should be redacted or never recorded, in line with PCI DSS obligations. Using transcripts for agent performance management affects employees; explain the program openly and follow any enterprise agreement consultation obligations. In New South Wales and the ACT, workplace surveillance laws may also require notice to staff. Prefer vendors with Australian data hosting.
This solution requires careful evaluation of cost-benefit ratio. Consider implementation only after thorough analysis and risk assessment.
Rules vary by state and territory, and advice specific to your setup is worthwhile. The common practice is a message at the start of each call that it may be recorded for quality and training, extended to mention analysis. Your privacy policy should also explain how recordings and transcripts are used, stored and deleted.
The technology overlaps with tools like Gong or Chorus, which focus on sales calls and deal outcomes. Support call analytics is tuned for complaint themes, call reasons, resolution and service quality. Some platforms cover both; others specialise. Choose based on whether your main goal is sales coaching or understanding service and product issues.
The one-to-two-month band covers connecting recordings, tuning transcription for your vocabulary, setting up categories and building dashboards. Useful trend insights appear once a few weeks of calls have been processed, and historical recordings can be backfilled to give an earlier baseline if they are available.
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