Volume trends, QA scorecards, sentiment analysis across every call, keyword tracking, agent performance leaderboards, bilingual EN/FR coverage. Reports that drive action, not just dashboards that look impressive in a demo.
Most voice analytics products are built to look impressive in a sales demo. Fifty widgets on a single screen, every chart type the designer could think of, drill-downs within drill-downs, color-coded everything. Supervisors look at them for about two weeks and then quietly stop opening the browser tab. The dashboards are not actually answering the questions those supervisors need to answer: why did Tuesday feel chaotic, which agent needs coaching this week, what are callers asking about that we have no good answer for, where is churn risk hiding in the conversations.
We build analytics around supervisor and manager workflows, not visualization taste. QA scorecards that get calibrated against manual review until they produce consistent, defensible scores. Sentiment and keyword tracking in Canadian English and French, with alerts when something trends off-baseline. Agent performance that combines productivity with quality so your best agents are not just your fastest ones. Reporting that exports to the tools your business already uses (Power BI, Looker, Tableau, scheduled CSV email) rather than making you log into another portal.
The output is a weekly operations review your supervisors can actually run, informed by data that reflects what happened on real calls, not sampled extrapolations. If your current analytics product cannot answer the questions you are already asking, that is the problem to solve, not whether the charts animate on hover.
Three feature groups covering real-time monitoring, quality and sentiment analysis, and reporting and export. Everything in the base engagement.
Volume, SLA, queue, abandon, and agent utilization dashboards that supervisors watch during live operations.
Automated QA scorecards, sentiment analysis, keyword and intent tracking across every call, in both official languages.
Scheduled reports, custom report builder, BI tool connectors, and API access for feeding analytics into your existing stack.
Not every operation needs deep call analytics. These are the situations where the output drives decisions and the investment is justified.
Financial advice, healthcare triage, legal intake, government service. Sampling 2 percent of calls for manual review used to be the standard; it is no longer defensible when every call can be transcribed and analyzed. Automated scorecards flag the calls that need manual attention, with the audit trail your regulators expect.
Coaching conversations without data devolve into opinion. QA scorecards per agent, sentiment trending, keyword patterns, specific-call references, the deliverable is a weekly coaching packet per agent that supervisors use directly rather than having to build themselves. Better coaching, less supervisor time spent prepping.
Call analytics is the richest source of voice-of-customer data most companies ignore. Keyword and intent tracking surfaces what customers are asking about, confused by, pushing back on, and praising. Product and marketing get aggregated insights monthly, with the ability to drill into specific call recordings when they need to hear the actual customer.
Mentions of competitors, "cancel", "disappointed", "last time I'm calling", sentiment drops across multiple calls from the same customer. Trained models surface these patterns, flag the accounts for retention outreach, and track whether the outreach actually worked. Better churn save rate, less reliance on exit surveys that only tell you after the fact.
Automated analytics only works if the scoring is calibrated against reality. We bake that calibration into every phase.
Define the questions your supervisors and managers need answered. QA scorecard categories, keyword lists, sentiment thresholds, report cadences, export targets. Deliverable is a measurement plan.
Analytics pipelines wired into your call recording and transcription layer, scorecards configured, dashboards built, reports scheduled, BI connectors wired up.
Run automated QA against manual reviewer scores on sample calls, tune thresholds until automated and manual agree within tolerance. Repeated monthly for ongoing drift control.
Monthly calibration refresh, quarterly scorecard review, new-keyword additions as product and operations evolve, supervisor training as the team changes.
Direct answers to the six questions we hear most often about call analytics specifically.
Thirty minutes with a practitioner, not a sales rep. We will walk through what your supervisors and managers are trying to figure out, then show you what the analytics layer would need to look like to answer those questions.