Call centre speech analytics automatically transcribes recorded calls and scores every conversation against the quality, compliance and customer experience criteria you define. Instead of listening to a handful of randomly chosen recordings, the quality team focuses on the calls flagged as risky, and management sees why customers are calling based on the topics of all calls rather than on impressions.
What quality teams can actually see today
In a typical contact centre, a quality analyst reviews a few calls per agent per month. Listening, scoring and writing feedback takes longer than the call itself, so only a small sample of each day's recordings is ever reviewed. The rest goes to the archive unheard.
Sampling creates three problems:
- Missed risk. A compulsory disclosure left out, a price quoted incorrectly, a promise made to a customer but never logged — if it was not sampled, it surfaces only when the customer complains.
- Unfair evaluation. An agent is scored on three calls from a bad day. Coaching based on a few random recordings neither convinces the agent nor reveals the real trend.
- An invisible voice of the customer. "Why did call volume jump this month?" is often answered from agents' impressions. Confusion caused by a new campaign or delays at a courier are recognised late because there is no topic data.
The cost of standing still
There is no public, Türkiye-specific statistic on speech analytics in contact centres. There is, however, reliable data on where companies use AI and why others hold back.
According to TurkStat, 46.5% of Turkish enterprises using AI apply it to marketing or sales, and 41.1% to production or service processes. (TurkStat Artificial Intelligence Statistics 2025)
In other words, customer-facing processes are the most common home for AI in Türkiye — yet the contact centre, where customer contact is most intense, is often still quality-checked by listening to a small, hand-picked sample.
The barrier is rarely the technology itself; it is usually not knowing where to start with AI. In the same TurkStat release, 74.2% of enterprises that considered but did not adopt AI cited a lack of expertise, and 62.4% unclear legal consequences. Europe looks similar: Eurostat reports that in 2025, 70.89% of EU enterprises that considered but did not use AI cited a lack of expertise, and 48.83% data protection and privacy concerns. Because call recordings contain personal data, that concern is sharper in contact centres; we deal with it separately below.
Among adopters, the experience is positive: the OECD's 2025 D4SME survey found that 91% of responding SMEs using generative AI reported productivity boosts (the survey sample is not representative). We explain how to measure that gain in your own project in measuring AI project ROI.
How call centre speech analytics works
A call travels through four layers on its way from recording to management dashboard:
- Speech to text. The recording is transcribed with timestamps and speakers separated (agent / customer). For Turkish in particular, accuracy drops unless product names, industry terms and abbreviations are taught to the model; a mistranscribed product code undermines every analysis that follows. Uses of transcription beyond the contact centre are gathered in our guide to Turkish speech to text for business, while short spoken commands in a noisy warehouse are covered in voice picking in the warehouse.
- Topic and intent classification. Each call is assigned to topics you define, such as "billing dispute", "delivery delay" or "cancellation request".
- Criteria-based scoring. Every call is checked against the same list: were greeting and identity verification done, was the mandatory disclosure read, was any prohibited phrase used, did the customer get a resolution, what were the talk time and silence ratio?
- Flagging and reporting. Calls below the threshold or carrying risk go to the quality team's queue; topic distribution and trends go to management dashboards.
What to measure with speech analytics
| Criteria group | Example questions | Who benefits |
|---|---|---|
| Compliance | Was the legal disclosure and recording notice read? Was a prohibited promise made? | Legal, compliance, quality |
| Process | Was identity verified? Was the right ticket opened? Was the call routed correctly? | Operations |
| Customer experience | Is the customer repeating the same issue? Are there complaint or cancellation phrases? | CX, sales |
| Agent performance | Silence and hold ratio, interruptions, resolution offered | Team leaders, training |
| Topic trends | Which call reason grew most this week? | Product, marketing, management |
It is better to start with two or three critical criteria rather than the whole table. A system that begins with just "was the legal disclosure read?" and "is there cancellation intent?" produces a concrete work list from the first week.
Consider a concrete case: in an insurance agency's contact centre, a pre-contract disclosure must be read in every sales call. The speech analytics system checks each call for the key sentences of that script and, at the end of the day, puts calls where they are missing on the team leader's list. The team leader listens to those calls and speaks to the agent the same day. A problem that would otherwise surface months later in an audit or a complaint becomes visible within hours.
Six steps to implementation
- Review your recording infrastructure. In what format and quality does your PBX or call recorder store calls, and are agent and customer on separate channels? Mono, heavily compressed recordings reduce transcription accuracy.
- Write the criteria down. Formalise your quality form, prohibited phrase list and mandatory disclosure scripts. The system applies your rules; if they do not exist yet, they are written first.
- Build a glossary. Product and campaign names, branch and dealer names, technical abbreviations. This step has a visible effect on Turkish accuracy; where needed, the model is fine-tuned on your recordings. Sorting the resulting transcripts by topic is then a task for Turkish natural language processing.
- Calibrate on a small sample. Compare the system's scores with a set of calls your quality team has scored by hand. Understand the differences before opening it up to all recordings.
- Connect it to the workflow. Flagged calls go to the quality queue, calls with cancellation intent to the retention team, and the call summary to the customer record in your CRM.
- Share it with agents. Presented as a coaching tool rather than surveillance, the system is accepted. Agents should see their own scores and sample recordings, and be able to challenge them.
Call recordings and data protection
Call recordings are personal data: names, phone numbers, addresses and sometimes health or financial details. Under Türkiye's Personal Data Protection Law No. 6698 (KVKK, broadly comparable in spirit to the GDPR), customers must be informed that the call is recorded and for what purpose, retention periods must be defined, and recordings deleted or anonymised when that period ends. Sending recordings to a cloud AI service abroad for analysis raises separate cross-border transfer questions. This is why, in many speech analytics projects, running transcription and analysis on the organisation's own servers is the preferred option. Masking fields such as card numbers and national ID numbers in the transcript should also be part of the design.
How we approach it at Digital Bridge
- We connect to what you have. Our voice recognition and call analytics system connects to your existing PBX and call recording infrastructure; if you have no recording set-up, we install that too. Every recording is processed and scored against the same criteria.
- We teach it Turkish and your industry. Where your product names, abbreviations and frequent terms require it, we carry out custom AI model training, and we measure accuracy on a test set held back from your own recordings.
- We bring results into the screens you use. Writing call summaries and topic tags to the customer record in your CRM is part of our system integrations work; if you have no CRM, we can also address the CRM software side.
- We turn data into management information. Topic distribution, compliance rates and agent trends feed BI dashboards.
- We plan data protection from day one. Privacy notices, retention periods and masking rules are designed alongside our data protection compliance consultancy. Legal basis and transfers abroad for AI projects are covered in our guide to AI and KVKK.
Once calls become text, the most frequent questions become obvious, and they can later become the knowledge base of a customer service chatbot. We explain that link in our customer service chatbot guide. On your website, that knowledge base can feed SmartOperator, an AI customer agent: it answers visitors from your knowledge base and catalogue, and hands the conversation to your team when it is unsure, so fewer repetitive questions reach the phone line. The written side of customer contact can be organised in a similar way: SmartMail, part of the Smart360 family, automatically classifies emails arriving in shared mailboxes (enquiries, invoices and payments, quotes and tenders, official correspondence and so on) and summarises each message.
Next step
A few hundred anonymised recordings from the last month and your current quality form are enough to start. Get in touch and we will prepare a demo on your own recordings, showing the system's scores side by side with those your quality team gave by hand. When a model should be trained on your own terminology is covered in our guide to custom AI model training, and other AI uses in customer communication in our complete Artificial Intelligence guide.