Call Center Quality Assurance Software: What Teams Use

Call center quality assurance software is the tooling contact centres use to score customer conversations against a standard, at a scale spreadsheets and manual review can’t sustain. This article covers what it actually does, why teams start looking for it, and how to evaluate it against what you’re already using.

This is part of our series on contact centre quality assurance; read the full pillar guide for a broader look at how QA works.

What is call center quality assurance software?

Call center quality assurance software is a platform used to review, score, and track the quality of customer conversations (calls, chats, and emails) against a defined scorecard. It typically replaces manual, spreadsheet-based scoring with a structured system that stores recordings and transcripts, runs scorecards digitally, tracks agent performance over time, and (in AI-driven platforms) automatically scores conversations rather than relying entirely on a human reviewer.

At its simplest, QA software just digitises the spreadsheet: same manual sampling, same scorecards, but stored centrally instead of scattered across files. At the more advanced end, AI-assisted platforms analyse every conversation automatically, which is the distinction that matters most when comparing options (more on that below).

Why teams start looking for QA software

Most teams don’t set out to buy QA software but they get pushed into evaluating it because their current process is quietly failing in one of a few predictable ways:

• The sample is too small to trust: A supervisor reviewing a handful of calls per agent per month isn’t catching most of what’s actually happening, it’s catching whatever happened to be in that small sample.

• Scoring is inconsistent: Without a shared, enforced standard, one reviewer scores generously and another strictly, for the same behaviour, which makes agent comparisons unreliable and can feel arbitrary to agents being scored.

• Feedback arrives too late to matter: By the time a manually reviewed call reaches an agent as coaching, weeks may have passed and the habit it flagged is already established.

• Nothing connects to anything else: Scores sit in a spreadsheet, recordings sit in a separate system, and coaching notes sit in a third place, if they’re written down at all.

• It doesn’t scale: What works for reviewing a handful of calls a week for five agents falls apart completely at fifty agents and multiple channels.

These pressures usually build for months before a team actively starts evaluating software, which is why understanding where the current process is breaking down matters more than jumping straight to a feature comparison.

Where spreadsheets and manual review fall down 

Spreadsheets are still the default QA tool for a lot of contact centres; research cited by Scorebuddy found that close to half of quality managers use spreadsheets (Excel or Google Docs) to measure customer service quality, with a further share still using paper!

They’re easy to set up, which is exactly why they stick around long after they’ve stopped being adequate.

The problems tend to show up in the same order as a team grows:

• Coverage never moves. A spreadsheet doesn’t change how many calls a human can realistically review. Multiple industry sources put manual QA coverage at somewhere between 1% and 5% of total interactions; a spreadsheet just records that small sample more neatly than paper did.

• No calibration mechanism. Spreadsheets don’t flag when two reviewers are scoring the same behaviour differently. That drift has to be caught manually, in a separate meeting, using a separate process.

• Trends are invisible. Spotting that every agent is struggling with the same scorecard question requires someone to manually cross-reference rows and columns across months of data. Nobody does this reliably at scale.

• No link to coaching. A spreadsheet score is a static number. Whether that number ever turns into a coaching conversation, and whether that conversation actually happened, isn’t tracked anywhere.

• It breaks quietly. Spreadsheets don’t fail loudly, but they just slowly become less accurate as the team using them grows, until a compliance issue or a churn spike forces the question of what QA has actually been catching.

None of this means spreadsheets are a bad starting point; most contact centres begin here. It means they have a ceiling, and most teams evaluating QA software have already hit it.

What call center QA software actually does

Regardless of vendor, most QA software is built around the same core workflow:

Scorecard building   Creating and editing the criteria conversations are scored against, usually with different scorecards for different teams, languages, or interaction types.	Conversation storage and retrieval Centralising call recordings, chat transcripts, and email threads in one searchable place, rather than across separate systems.	Scoring and review  Either manual (a reviewer scores a selected conversation) or automated (AI scores some or all conversations against the scorecard).
Coaching workflows Turning a completed score into a feedback session, tracked over time, ideally visible to the agent themselves.	Reporting and trend analysis Surfacing patterns across agents, teams, or scorecard questions, rather than requiring someone to build this manually.	Integrations Connecting to the systems that already hold the conversation data: contact centre platforms like Zendesk, Salesforce, or LivePerson, plus CRM and workforce management tools.

Where platforms differ most is in how much of that scoring is automated, and how deep the coaching and reporting layer goes, which is exactly what to focus on when comparing options.

How teams evaluate call center QA software

Once a team is actively comparing platforms, a few criteria tend to separate the options that solve the underlying problem from the ones that just digitise it:

• Coverage: Does the platform still rely on a human selecting a small sample to review, or can it score every conversation automatically? This is the single biggest differentiator in the market today.

• Channel support: Some platforms are built primarily for voice and treat chat and email as an afterthought. If your contact centre runs across multiple channels, QA needs to as well, otherwise you end up running two QA processes instead of one.

• Scorecard flexibility: Can scorecards be built and adjusted by your own team, or does every change need vendor support? Teams with compliance requirements or multiple business units usually need more flexibility here than a generic template provides.

• Coaching workflow depth: Does the platform connect a score to an actual coaching conversation, tracked over time and visible to the agent? Or does it stop at producing a number?

• Calibration support: Is there a built-in way for reviewers to check and align their scoring, or is calibration still a separate, manual exercise?

• Time to value: How much setup, tagging, or configuration is required before the platform is producing usable output? This matters more than it sounds, since a QA tool that takes months to implement often loses momentum internally before it ever gets used properly.

• Where it fits alongside agent monitoring: QA software and agent monitoring software overlap but aren’t identical; it’s worth being clear on which problem you’re solving before comparing tools across both categories.

Types of call center QA software

Most of what’s on the market falls into a few broad categories:

1. Digitised scorecard tools 

These move manual QA off spreadsheets and into a dedicated system; better record-keeping and reporting, but a human still selects and scores each conversation, so coverage stays roughly where it was.

2. Voice-analytics-led platforms

Historically built around call recording and speech analytics, strongest on voice, often added chat and digital channels later as a secondary capability.

3. AI-native, full-coverage platforms 

Built to automatically score every conversation across every channel using AI, with human reviewers focused on calibration, priority conversations, and coaching rather than first-pass scoring.

The category names in this space are used loosely, and vendors span more than one of these at once; the more useful question is usually not “which category” but the two things covered above: how much of the scoring is automated, and how well the coaching loop actually works once a score exists.

Who’s in the Call Center QA Software Market

To make the categories above concrete, here’s how some of the more commonly evaluated platforms compare. This isn’t an exhaustive list, and vendors update their products regularly, so it’s worth confirming current capabilities directly before shortlisting.

Platform	What it is	What makes it different	Best fit for
EdgeTier Coach	AI-native QA platform that analyses 100% of chat, call, and email conversations automatically.	Full-channel coverage from day one, AI-completed scorecards, and a built-in coaching and calibration workflow agents can view and formally dispute their own scores.	Teams that want to move straight from a small manual sample to full coverage, across every channel, without bolting on a second tool for chat or email.
MaestroQA	A scorecard and coaching platform built around manual review, with an AutoQA layer added for automated scoring.	Strong coaching templates, gamification and leaderboards, and screen-capture review; automation sits alongside a manual-first foundation rather than being the default.	Teams whose main priority is a rich coaching and gamification layer, and who are comfortable running manual and automated scoring side by side.
CallMiner	An enterprise-grade speech and conversation analytics platform (Eureka), historically strongest in voice and since expanded into chat, email, and other channels.	Deep configurability and analytics depth; typically deployed alongside an existing CCaaS or telephony system rather than replacing it.	Large enterprises with dedicated analytics resource and complex compliance or risk-monitoring requirements.
Zendesk QA (formerly Klaus)	An AI-powered QA tool (AutoQA) built natively into Zendesk’s workforce engagement management suite.	Originated in digital, ticket-based support; tightest integration is with Zendesk itself, though it also works with other helpdesks.	Teams already standardised on Zendesk who want QA bundled into that ecosystem rather than run as a standalone platform.
Playvox (by NICE)	A quality management and workforce engagement platform, acquired by NICE.	Bundles QA with workforce management, scheduling, and gamification in one product; built primarily around digital channels (tickets, chat, email).	Teams that want QA and workforce management handled in a single platform rather than stitching two together.
Verint	A long-established enterprise workforce engagement management suite with a dedicated quality automation module.	Deep enterprise scale and compliance capability, positioned as part of a much broader WEM suite rather than a focused QA point solution.	Large, often regulated enterprises already invested in, or evaluating, a full WEM suite beyond QA alone.

FAQs

Where This Fits in the Wider QA Cluster

This article is part of a broader series on contact centre quality assurance:

• Contact Centre Quality Assurance: A Modern Guide — the full pillar overview
• Call Center Quality Monitoring: How It Works in Practice
• Quality Monitoring Scorecards for Contact Centres
• Automated Quality Management in Contact Centres

Call Center QA Software and EdgeTier

EdgeTier Coach is built around the distinction covered throughout this article: instead of digitising a manual sample, Coach uses AI to analyse 100% of chat, call, and email conversations automatically, completing scorecards, flagging priority conversations for human review, and surfacing performance trends across agents and teams as they happen.

Teams using Coach have seen QA coverage increase by more than 90%, CSAT scores rise by 16 points, and reviews completed 2.5x faster than manual processes. Electric Ireland moved from delayed, anecdotal sampling to full real-time coverage, driving a 21% increase in CSAT and a 19% improvement in first contact resolution.

Coach also keeps scorecards fully customisable, so teams moving off spreadsheets or a legacy tool aren’t forced into a rigid template, and it’s built to handle every channel from day one, rather than treating chat and email as an add-on to a voice-first system.