Conversation context
Read the customer's needs and commitments alongside their history. Summaries help with long threads and handovers; the original messages remain important evidence.Explore conversation intelligence
A conversational CRM uses customer conversations as context for managing a relationship and a sale. Messages inform contacts, service interest, opportunities, and follow-ups instead of remaining a disconnected chat history.
A request for a price might be a new purchase, a question about an existing order, or an enquiry about the wrong service. Converting every message into a deal inflates the pipeline. Leaving every message in the inbox hides the work that does deserve a next step.
Qonvera connects conversation understanding with deliberate CRM actions. A teammate can inspect context, confirm the contact and service, and create an opportunity with a responsible owner. AI supports that judgment with summaries, intent signals, and suggested actions.
Read the customer's needs and commitments alongside their history. Summaries help with long threads and handovers; the original messages remain important evidence.Explore conversation intelligence
Use the configured catalogue and matching suggestions to identify an offering. The teammate confirms the choice; uncertain suggestions can require clarification.
Keep a sale separate from the conversation. A contact can have further enquiries, and an opportunity needs its own owner, value, stage, and outcome.Explore the conversational sales pipeline
Translate a promise into an owned task with a due time. An AI suggestion is not a completed action, and a reminder is not proof that a message was sent.Organize next actions and follow-ups
Illustrative example: a customer asks about a recurring maintenance service. The goal is to preserve the meaning of the enquiry without letting an automated interpretation become an unsupported commitment.
Read or summarize the thread, then clarify the service scope. A vague message needs a question, not a guessed quote.
Review identity evidence and choose the active catalogue item that actually matches the request.
Use the permitted confirmation or manual creation flow. Select the owner and appropriate pipeline; check the saved value and service details.
Schedule the quote follow-up and preserve a handover note. Review today's queue and overdue commitments.
Record the outcome when the customer makes a decision. An enthusiastic reply or high intent score alone is not won revenue.
Conversational CRM fits consultative sales, quotes, repeat enquiries, and team handovers. It does not remove the need to maintain a catalogue or agree how opportunities should progress.
Check summaries, service suggestions, and drafted replies before relying on them. Automatic reply modes require separate configuration and human handoff rules.
Unclear catalogue items, missing owners, or inconsistent pipeline stages remain operational problems. A conversational interface cannot resolve them by itself.
Conversational describes how sales work starts; omnichannel describes how different sources are connected. A CRM may do both, as Qonvera does.
No. A chatbot handles a conversation or an automated response. A conversational CRM connects the interaction to customer and opportunity records, ownership, follow-up, and outcomes. It can include a chatbot without being limited to one.
No. The essential requirement is that conversation context informs CRM work. AI can help summarize messages or suggest a next step, but clear records and human responsibility are still necessary.
Qonvera supports controlled Draft and Automatic reply modes for eligible configured conversations. This is distinct from confirming a service or recording a sale.Read about AI reply controls
An omnichannel inbox centralizes replies across supported channels. Conversational CRM goes further into what the conversation means for a contact, opportunity, next action, and recorded outcome.Compare inbox and CRM responsibilities
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