Last updated: September 16, 2026. Every statement about a named vendor was read from that vendor's own published pages on that date.
The question each category answers
Speech analytics asks what was said across thousands of calls. Conversation intelligence asks which conversation patterns predict an outcome, usually a closed deal. Call QA scoring asks whether one call met a written standard. Buyers who shop by feature list end up owning the tool that answers a question they never had.
The unit of analysis follows from the question, and that one distinction survives a demo better than any feature grid does. Speech analytics works on a corpus, meaning every call in a period treated as one pile of searchable text. Conversation intelligence works on a deal, following an account across calls and whoever got pulled into the last meeting. Scoring works on one call against one scorecard, which is the smallest unit of the three and the only one that produces something an agent can argue with.
Getting sold the wrong one happens in a predictable way. A compliance manager walks into a demo needing proof that a disclosure was read on a particular Tuesday call, watches sentiment trend lines move across a quarter, and buys the pile of text. Two months later the platform can find every call where the disclosure came up and still cannot say whether that one call met the standard.
Conversation intelligence
The category grew out of sales coaching and still carries that shape. Gong describes itself as a revenue AI operating system, sells to leadership, RevOps, sales, customer success, and enablement, and ships named applications for engagement, forecasting, and enablement. Its pricing page describes per user licenses plus a platform fee based on how many users are supported, and publishes no number. The form sorts buyers into team size bands running from one to fifty people up to ten thousand and more.
What comes out of a platform like that is a read on a deal rather than a grade on an agent. Pipeline risk, accounts that have gone quiet, the difference between how the closed deals and the lost ones sounded in week three. A rep coached off that is being coached toward revenue, and nobody in the room is asking whether the greeting matched an account script.
Labels move, which is worth saying before this page gets read as a taxonomy. Observe.AI sat in this category for years and now sells purpose built AI agents on a single CX platform, with voice and chat agents that resolve interactions outright, next to agents named Companion, Insight and Performance. Nowhere on its homepage does it describe itself as conversation intelligence. The question a tool answers holds up longer than the shelf it gets filed on, so use the question.
Speech analytics
Speech analytics is the oldest of the three and the one built for volume. Transcribe everything, index it, then run queries across the pile. Which complaint categories climbed last quarter, and what people tend to say in the minute before they ask to cancel.
CallMiner is the reference point. Its Eureka platform ingests audio, screen recordings, and survey responses, analyzes across all of it, and publishes a Leader placement in a Forrester Wave for conversation intelligence solutions in contact centers, which tells you how blurry these two labels have gone at the top of the market. No price appears anywhere on the site, and the documented path to a figure is a demo request.
Value shows up at scale and almost never on a single call. A pattern needs volume before it counts as a pattern, and somebody has to own the queries, keep the categories current, and explain to an executive what moved and why. That is a role rather than a feature, and it is the part a small operation reliably underestimates when the demo looks this good.
Call QA scoring
Scoring grades one conversation against a standard your own team wrote. Greeting, caller capture, the recording disclosure, the account's triage question, the callback number read back digit by digit. Criteria carry points, a few of them zero the call outright as a critical fail, and the total attaches to an agent, a shift, and a client account.
The output is an artifact instead of an insight. A supervisor opens the call, hears the moment at the timestamp, and either agrees with the score or does not, and the disagreement is the useful part, because it usually means the criterion was written badly rather than that the agent failed. The mechanics of that are in how a call gets scored, and the definitions behind the totals are in QA metrics and what they measure.
CallSniper sits in this category and in no part of the other two. Recordings come in after the call ends, get transcribed and speaker separated, then scored against scorecards where each criterion carries its own points, with pass or fail flags and critical failures, with dashboards by agent and by client account and scorecards and vocabulary sets you edit yourself. It will not forecast a deal or explain why cancellations rose in one region, and it changes nothing while a call is live.
One table, three categories
| Category | The question it answers | Unit of analysis | Who uses the output | What it costs in effort | Typical buyer size |
|---|---|---|---|---|---|
| Speech analytics | What was said across thousands of calls | The corpus, a period of calls treated as one searchable body | Analysts, risk and compliance, operations leadership | Somebody owns the query categories and keeps them current | Large contact centers with an analytics function |
| Conversation intelligence | Which conversation patterns predict an outcome | The deal or the account, across every touch | Sales leadership, RevOps, enablement | CRM hygiene, plus a rollout the sales team actually adopts | Revenue teams at almost any size, with team tiers published from under fifty sellers to ten thousand plus |
| Call QA scoring | Whether this call met a written standard | One call against one scorecard | Supervisors, QA staff, and the client whose calls you answer | Writing the scorecard, then calibrating reviewers against it | Small and mid sized operations, from a couple dozen seats |
Buyer size and effort in that table are our read rather than a vendor statement, and the two pricing details come from published pages. Everything else describes what each category is for.
The cost of picking wrong runs in both directions and lands differently. A quality buyer who ends up in analytics pays for depth nobody on staff has time to use and still cannot evidence a single call. An analytics buyer who ends up in QA gets a clean scoreboard that never explains the trend that started the search.
Where the three overlap, and why that is not a trick
All three transcribe. Most will put a sentiment label on a call, and two will flag a keyword you supply. Feature grids therefore make the categories look like one product at three prices, which is how an enterprise analytics platform ends up in a sixty seat evaluation that nobody can close.
Overlap in features is genuine and it moves the purchase less than it looks like it should. Sentiment is the clearest case, since every category ships it and it is the weakest evidence any of them produce. A sentiment score says the call went badly. A matched criterion with a timestamp says the agent never read the disclosure at 0:14, and only the second one survives a conversation with the agent.
Machine scoring holds up on rule based criteria and gets shakier on tone and empathy, and that limit lands on all three categories including ours. Raising a confidence threshold does not fix it. A human still reads the calls where the judgment is the whole point.
Picking by the job you actually have
Three situations, because headcount on its own settles less than people expect.
Sales calls run long, the team is closing deals, and the question is why some reps win. Buy conversation intelligence. A scorecard applied to a discovery call measures process compliance and says nothing about why the deal went quiet in week three.
Calls are short, a client wants to know how their calls were handled, and somebody has to prove a disclosure was read on a specific one. Buy scoring, because the evidence has to attach to one call and a trend line cannot carry it.
Volume runs into the millions, an analyst already owns the reporting, and the open question is what changed across the corpus last quarter. Speech analytics, and budget for the person who will run it.
If you already own one and need the other
The common version is a company with a revenue platform on the sales floor and nothing at all on the service side. Rep calls are instrumented to the minute, while the support queue, where the complaints and most of the compliance exposure live, gets one supervisor listening to three calls a week against a form built in a spreadsheet.
Adding a scoring layer beside an existing platform takes less than a migration. The recordings already exist wherever your phone system keeps them, and a manual upload takes a batch of them without a telephony project in front of it. Your sales team keeps the tool it likes. Buyers running this comparison against one vendor at a time usually want how we compare to Voxjar next, and the broader definition sits in what call center QA is.
Buying them in order
Which question you can afford to answer first depends on what your team can already do with an answer.
Say a thirty seat floor takes 9,000 recorded calls a month and one supervisor scores four per agent per week. That is 120 scored calls, a little over one percent of the month. An operation at that size buying pattern analytics before it can score a single call has bought an answer it has no way to act on, since the fix for almost any pattern is coaching, and coaching runs off scored calls.
So the standard question comes first at that size. Write the scorecard, have two supervisors score the same ten calls separately and look at the calibration variance, then widen coverage until the scores mean something. The pattern question keeps. It also gets cheaper to answer later, once every call is already transcribed, searchable, and scored, which is most of the input an analytics program needs anyway.
A review of your own recordings runs your own recordings against a scorecard you wrote, which settles the category argument faster than a third demo will.
Frequently asked questions
What is the difference between conversation intelligence and quality assurance?
The unit of analysis. Conversation intelligence analyzes a deal or an account across many conversations to predict and improve a revenue outcome, while QA scores one call against a written standard to measure performance and produce evidence. Both start from a transcript and end somewhere completely different.
Is speech analytics the same as call QA?
No. Speech analytics searches a large volume of calls for patterns, trends, and risk language, and its value comes from scale. QA scoring evaluates individual calls against criteria your team wrote, and its value comes from the per call artifact a supervisor and an agent can both open.
Do I need both conversation intelligence and QA software?
Sometimes. A company with a real sales motion and a real service queue has both questions, and one tool answers each of them badly. A thirty seat operation answering inbound calls has one question, so buying both means paying twice to solve half of it.
Which is better for compliance?
Scoring, when you need to prove that a specific call met a specific requirement, since the evidence is a criterion tied to a timestamp. Speech analytics is stronger for surveillance across the whole corpus, such as finding every call last quarter that used a prohibited phrase. Neither one replaces the other, and regulated teams usually want the per call evidence first.
Can a QA tool do sentiment analysis?
Yes, at the level of a flag or a ticker on a call. Treat it as the weakest signal on the page, because sentiment describes a mood while a matched criterion names a behavior with a timestamp behind it. Score the behavior and use sentiment to decide which calls to open.
Sources
- Gong pricing page, fetched September 16, 2026, for the revenue AI operating system self description, the buyer roles, the per user plus platform fee pricing model with no published figure, and the team size bands on the form. Verified by fetch.
- Observe.AI home page, fetched September 16, 2026, for the named agents and the absence of conversation intelligence as a self description, with observe.ai/pricing returning 404 the same day. Verified by fetch.
- CallMiner Eureka product page and security page, fetched September 16, 2026, for omnichannel capture across audio, screen recordings and survey responses, the absence of any published price, and the pricing URL redirect. Verified by fetch.
- CallMiner's published Leader placement in The Forrester Wave: Conversation Intelligence Solutions for Contact Centers, Q2 2025, as stated on callminer.com. Placement checked September 16, 2026. The report itself was not read and nothing beyond the placement is characterized here.
- CallSniper product facts. Trinsic feature sheet and call notes, verified internally.
Gong, Observe.AI, CallMiner and Eureka are trademarks of their owners. Every statement about a named vendor comes from that vendor's own published materials, read on September 16, 2026. Vendor pages change without notice, so check them before you decide.
- Manual quality review reaching roughly one to three percent of calls. Verint and secondary summaries. Third party, unverified, used here as setup rather than as a claim about any product.
- Buyer size and effort columns in the comparison table. Editorial judgment, not a vendor statement.
- CallSniper product facts on post call ingest, speaker separation, scorecards with points per criterion, critical fails, per account dashboards, and manual upload. Trinsic feature sheet and call notes, verified internally.