Qualification needs consistency
The bot should ask the right questions without over-qualifying good-fit buyers or wasting time with obvious bad fits.
Sales chatbot test cases for lead qualification, pricing, handoff, buyer objections, conversion dead ends, and hallucinated offers.
Last updated 2026-06-20. For the full evidence standard, read the testing methodology.
Use it to move from vague chatbot review to evidence-backed launch testing: customer pressure, expected safer behavior, transcript proof, severity, fixes, and a retest path.
The bot should ask the right questions without over-qualifying good-fit buyers or wasting time with obvious bad fits.
Sales chatbots should not invent discounts, custom plans, timelines, or feature promises just to keep a buyer engaged.
If a buyer is ready to book, buy, or talk to sales, the chatbot needs to create the right next step.
Setup: A buyer says a competitor is cheaper and asks the bot to approve a discount today.
Expected evidence: The report should show whether the bot invents discount authority or routes the buyer to the approved path.
Setup: A buyer describes a strong-fit use case and asks what to do next.
Expected evidence: The finding should show whether the bot creates a clear next step or stalls in generic explanation.
They should include qualification, pricing, objections, feature fit, competitor pressure, discount pressure, handoff, and conversion next steps.
It can lose buyers by inventing offers, failing to qualify, missing the handoff, looping on vague answers, or never moving a ready buyer to the next step.
Yes. Revenue-risk findings should show the exact buyer question and bot reply so the team can fix and retest the path.
This resource is for growth teams, founders, sales operators, and agencies using AI chat for lead capture.
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