Uniphore conversational AI is often researched by teams who want to understand what their customers are saying across thousands of calls and messages. The underlying job is conversation intelligence: turning conversations into data you can act on. This article does not review that vendor. It shows a firm of 10 to 250 people how to get the practical version of that job on the platform: every conversation summarised, tagged by intent and outcome, and fed into reports that tell you why deals stall.
Your conversations already contain your sales diagnosis
When revenue flattens, most owners look at the pipeline report. The pipeline shows where deals stop but not why. The why is in the conversations: the objection raised on the first call, the question nobody answered, the competitor mentioned twice a week. In most firms that information lives in people's heads and disappears when they leave.
Conversation intelligence captures it systematically. Each call or message thread gets a short summary and a small set of tags, such as the reason for contact, the objection raised and whether a next step was agreed. After a month you can count them. After a quarter you can see patterns, and you can see which salespeople handle each objection well.
Before you start
- Calls recorded through the platform's phone numbers, with call recording disclosed to callers as your local rules require.
- Messaging channels connected, so chat, SMS and email threads sit in Conversations.
- A short tag list you agree on in advance: five to eight reasons for contact and five to eight common objections.
- A pipeline with stages that mean the same thing to everyone.
- An owner for the monthly review, usually the sales lead.
How to set it up
- Go to AI Agents > Conversation AI and click Agents List.
- Click the agent, then Bot Goals.
- In Information Collection, select the standard fields you need.
- Add custom questions for call reason and main objection.
- Open Update Contact Field and save each answer to a field.
- Select Trigger Workflow to tag contacts from those answers.
- Turn on Enable Conversation Summary for the sales team.
- Click Save and test with sample messages.
What to do with the data
The point is not a dashboard. The point is three decisions a month. Which objection is growing, and does your website or proposal answer it? Which reason for contact converts best, and are you spending to attract more of it? Which person handles the hardest objection well, and can they teach the rest of the team?
The same tags make your AI agents better. When you know prospects keep asking about contract length, you add a clear answer to the agent's knowledge base, and the question stops reaching your team. For examples of how tagged data sits in the CRM, read AI sales assistant.
Suppose the brokerage tags 900 calls and message threads over three months. The report might show that "already has a broker" is the most common objection at first contact, and that one adviser books follow-up meetings from that objection far more often than the others. The obvious next move would be for the team to adopt her approach and add a short page explaining how a second opinion works.
What it replaces
It replaces manual call notes that vary by person, the occasional call-listening session that never becomes a habit, and win-loss reviews built from memory. It does not replace a manager listening to calls; it tells the manager which calls are worth listening to.
Running time: thirty minutes a month for the review once tagging is reliable, plus about an hour in the first fortnight checking accuracy.
Most teams create too many tags. With forty tags, each one appears a handful of times and no pattern is visible. Keep the list short enough to fit on one screen. Merge tags that lead to the same decision.
When not to use it
If you have fewer than a hundred conversations a month, patterns will be thin, and a weekly team discussion may teach you more. If calls are not recorded, start with messaging threads only rather than asking people to type up calls. And do not use summaries as the only record for regulated advice; keep the full recording or transcript where your obligations require it.
Troubleshooting
Tags are applied inconsistently. The definitions are vague. Rewrite each as a test, such as "use this tag only when the contact names another firm".
Summaries miss the key point. A general summary buries the detail you count. Add custom questions for the reason, the objection and the agreed next step, so each answer lands in its own field.
Reports show nothing. Tags are written to contacts but the report counts opportunities. Apply the tags to both, or change the report.
Salespeople ignore the notes. The notes are long. Keep them to three lines, placed at the top of the contact.
Frequently asked questions
Is Uniphore conversational AI necessary for conversation analytics?
Not for a firm of this size. Specialist analytics products are built for very large volumes and contact-centre operations. Summaries, tags and reports inside the CRM give a smaller firm the same core insight without a separate system to integrate.
Can I analyse conversations handled by AI agents as well as people?
Yes, and you should. Tagging AI-handled conversations shows which questions the agent struggles with. The guide to AI customer support covers using that feedback.
How is this different from call recording alone?
Recording keeps the evidence. Summaries and tags make it searchable and countable, so you can find patterns without listening to hundreds of hours. See conversational AI for sales for how that feeds coaching.