Evaluating a conversation intelligence vendor requires more than a demo. The questions you ask before signing determine whether you get a platform that generates actionable insights or one that produces reports no one reads. This guide covers the key questions to ask vendors, organized by the capabilities that separate genuine conversation intelligence platforms from repurposed transcription tools.
How to Structure Your Vendor Evaluation
Before running demos, define the two or three outcomes your team actually needs: reducing QA manual review time, improving coaching specificity, identifying product feedback at scale, or tracking compliance adherence. Vendors who cannot speak directly to your use case during evaluation will not deliver on it after contract. Use the questions below to test whether a platform is built for your needs or built for a generic pitch.
What questions should you ask a conversation intelligence vendor?
Start with how the platform generates insights, not just what it captures. A strong vendor can explain: how themes are identified across calls (keyword matching vs. semantic analysis), how scoring accuracy was validated against human judgment, what the process is for tuning evaluation criteria to your specific business, and how long that tuning typically takes. Vendors who deflect these questions with features are hiding limitations.
What is the difference between transcription and conversation intelligence?
Transcription converts audio to text. Conversation intelligence analyzes that text to extract structured insights: performance scores, behavioral patterns, themes, objections, and sentiment trends. Many tools sold as conversation intelligence are primarily transcription with basic summary features. True conversation intelligence platforms aggregate across thousands of calls to surface patterns — not just what was said in one call, but what is being said consistently across your operation.
Technical Capability Questions
Ask vendors to explain the architecture behind their scoring. Key questions:
How does your platform determine call type and apply the right scorecard?
Platforms with auto-detection route calls to the correct evaluation criteria based on conversation content, not manual tagging. This matters in operations with multiple call types — sales, support, onboarding, collections. Insight7's dynamic evaluation system auto-detects call type and supports 150+ scenario types. If a vendor requires manual categorization for every call, ask what that workflow looks like at 10,000 calls per month.
How do you handle intent vs. script compliance evaluation?
Some criteria require verbatim script adherence (legal disclosures, compliance statements). Others require intent evaluation — did the agent convey the right message even if not word-for-word? Strong platforms allow per-criteria configuration of verbatim vs. intent-based evaluation. Ask for a live demonstration of both modes.
Can you show me evidence-backed scoring?
Every score should link to a specific quote or moment in the transcript. Ask the vendor to demonstrate this during the demo — click a score, see the exact passage that drove it. If the platform cannot do this, you cannot audit its accuracy.
Data Quality and Accuracy Questions
What is your transcription accuracy benchmark and how was it measured?
Insight7 benchmarks transcription accuracy at 95%, with LLM-generated insight accuracy in the 90%+ range. Ask vendors for their accuracy benchmarks, the methodology behind those numbers, and specifically how accuracy performs on accented speech, technical terminology, and your industry's vocabulary.
How much does the platform change its output when you provide business context?
Out-of-box scoring without business-specific context frequently diverges from human judgment. Ask what happens when you add descriptions of what "excellent" and "poor" performance looks like. Vendors who cannot explain this process are likely to produce scores that confuse your team. Tuning typically takes 4-6 weeks for enterprise deployments.
How do you handle multi-agent calls or calls where agent identification fails?
When direct system integration isn't available, some platforms identify agents from name mentions in transcripts — which can create attribution errors. Ask specifically how the platform handles edge cases in agent attribution.
Reporting and Analytics Questions
What does an aggregate view across 1,000 calls show?
A vendor should be able to demonstrate, not describe, what analysis across a large call set looks like. For Voice of Customer analysis, this means theme extraction with frequency percentages, quote extraction by semantic meaning, and cross-call pattern identification. For QA, it means per-agent scorecards and team-level performance views.
Can your reports be shared with stakeholders who don't log in to the platform?
Executive teams need insights without requiring platform access. Ask about branded report export, shareable dashboards, and Slack or Teams notification workflows for alerts.
How do you surface changes in customer sentiment or behavior over time?
Point-in-time analysis is less valuable than trend detection. Ask how the platform presents changes: what's improving, what's degrading, and what's newly emerging across customer conversations.
Integration and Security Questions
What integrations are available and how long does setup take?
Insight7 integrates with Zoom, Google Meet, Microsoft Teams, RingCentral, Vonage, Amazon Connect, Five9, and Avaya, plus CRMs (Salesforce, HubSpot) and storage (Dropbox, Google Drive). Ask for the full integration list and confirm whether the integrations you need are native or require API development. Also ask: how long from contract to first analyzed calls? Fast deployments should complete within two weeks.
What are your security certifications and where is data stored?
The minimum acceptable for enterprise deployments: SOC 2 Type II, GDPR compliance, and explicit confirmation of data residency. Ask whether the vendor trains models on your data — this is a critical data governance question. Confirm your data is stored in your region.
If/Then Decision Framework
| If your evaluation priority is… | Then ask specifically about… |
|---|---|
| QA automation at scale | Auto-detection, evidence-backed scoring, agent attribution accuracy |
| Coaching specificity | How scenarios are generated from actual calls, post-session coach feature |
| Voice of customer | Thematic analysis methodology, cross-call aggregation, marketing dashboard |
| Compliance monitoring | Verbatim vs. intent modes, alert configurations, audit trail |
| Enterprise security | SOC 2, data residency, model training policy |
FAQ
How long should a conversation intelligence vendor evaluation take?
A thorough vendor evaluation for a conversation intelligence platform typically takes 4-6 weeks: one week for internal needs assessment and RFP drafting, one to two weeks for demos and technical questions, and two to three weeks for a pilot on your actual call data. Do not sign based on demos alone — insist on a pilot with your calls and your evaluation criteria before committing.
What red flags indicate a weak conversation intelligence platform?
Three common red flags: the vendor cannot demonstrate accuracy benchmarks specific to your call type or industry vocabulary; scoring is not linked to specific transcript evidence (you cannot audit a score); and the vendor cannot explain the process for aligning AI scores with human judgment before going live. Any of these indicates a platform that will require significant workaround or produce outputs your team will not trust.
