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Speech analytics starts not with buying a “smart” engine but with your own calls. The right order is this: first collect and tag the archive of conversations, then define metrics and quality criteria, and only then automate. Below are the seven steps a team goes through as it moves from listening to 2–3% of calls blind to continuous control over every conversation.
Step 1. Collect and upload the call archive
Start with what you already have — the recordings. You don’t need to integrate telephony to get a first result: upload the call archive as files or by forwarding to an ingest email address, and the platform starts working with them. This is the safest entry: you change nothing in your current processes and immediately see what really happens in conversations.
At this step it’s worth deciding on volume: take a representative slice of the last weeks or months so the picture is honest, not assembled from lucky examples. The fuller the archive, the more accurate every downstream metric and the more meaningful the retro-analysis.
Step 2. Transcribe and tag conversations
Next, every call is automatically transcribed and split by speaker (diarization): you can see who spoke and when, where the pauses, interruptions and overlaps were. This is the foundation — without an accurate transcript and role separation no metric means anything.
For more on how this layer works, see the automatic call analysis page. Here one thing matters: tagging runs across all calls at once and uniformly, not on a random sample, so from now on you work with continuous, homogeneous data.
Step 3. Define metrics and quality criteria
Now decide what exactly you want to measure. The system computes some metrics itself — silence share, speech pace, interruptions, response delay, tone, monologue length. On top of them, define your own quality criteria for your process: was contact established, was the need discovered, was the mandatory phrase said, was the next step booked.
Don’t try to describe everything at once. Take three to five criteria that truly affect the outcome and calibrate them on familiar records, comparing the auto-score with a manual one. Good criteria are those two people would score the same way; refine vague wording until it becomes checkable.
Step 4. Tag topics and extract data
Once the basic metrics work, add a semantic layer: auto-tags for topics and contact reasons by the meaning of the conversation, plus extraction of structured data — amount, product, region, promise to pay. This turns the flow of calls into a table you can slice and find patterns in.
This is where speech analytics starts answering business questions, not just technical ones: what people call about most, which objections recur, where deals are lost. Semantic search in advanced analytics lets you find conversations by meaning — “the client was ready to buy but the rep didn’t close” — not by word match.
Step 5. Build dashboards and find patterns
Assemble dashboards from the tagged data: topic dynamics over time, the distribution of quality scores by operator and stage, sentiment trends, period comparison. The goal of this step is to move from individual calls to the whole picture and see what a sample can’t show.
This is usually where the first discoveries happen: a topic thought to be rare turns out to be in the top; one operator consistently sags on a specific stage; a spike in negativity coincides with a product change. This is exactly continuous quality control across 100% of calls — more in the quality control use case.
Step 6. Set up automations
Once you understand your conversations, hand the routine to rules. An automation can, for each new call, transcribe it, apply tags, extract metrics, run it against quality criteria, send a notification to the owner, or push data to the CRM and over a webhook. This way analytics stops being a report someone opens once a week and becomes part of the operational process.
Start with one or two rules that remove the most visible manual work — for example, an auto-tag “complaint” with an alert to the manager. Expand the set gradually as you trust the tagging; rules are easy to disable and rebuild.
Step 7. Close the loop and improve
Speech analytics isn’t a one-off project but a loop that improves itself. New calls keep getting tagged, criteria get refined on disputed cases, dashboards show the effect of changes, and the best conversations become a benchmark for training the team. Every so often, revisit the criteria and metrics: what mattered at the start may have shifted.
By this step you no longer hold “call recordings” but a living body of tagged data about your conversations — coverage, topics, quality, languages, risky moments.
What’s next
That body of data is the entry point to AI transformation. Before deploying a voice bot or a secretary, you need to understand your calls and build knowledge from them; speech analytics gives you exactly that. The logical next step is to assess which scenarios to automate first and where the real ROI is: see the preparing for AI transformation piece.