Industry solution
Buyer-base reactivation for real estate agencies
Every agency accumulates a large base of buyers who actively viewed apartments a year or two ago but never bought: the price didn't work out, the mortgage wasn't approved, they postponed because of the rate, they were waiting to sell their own place. Formally the contacts "exist," but no one calls them — working a cold base by hand isn't worth it, and meanwhile the market has changed: rates, prices and supply are different now, and some of these people are ready to buy again. A voice AI agent works through such a segment from the CRM for you, holds a live personalized dialogue, and finds out whether the housing question is still live, whether the budget and area have changed, and whether the mortgage is approved now.
The quirk of real estate is that the reason for going quiet is often external and reversible: not rejection of the brand, but a high rate or a variant never found. The agent records into the record fields the relevance of the request, the new budget, the preferred area and the payment method, and immediately transfers a hot buyer whose question is live again to an agent with the context already collected. Every conversation goes into analytics, and for the first time the manager sees across the whole volume which share of the old base is actually returning to the market and which has already bought from a competitor or dropped the question.
Which industry pains this solves
A flood of portal leads
Requests from listing portals and ads drop in waves. At peak, agents physically can't call everyone back while the buyer is still hot.
A slow first call
The buyer left a request with five agencies at once. Whoever calls back first gets the conversation; the rest of the leads simply go cold.
How it works
A step-by-step scenario — the same steps as in the full use case, applied to your industry.
- 1
Build the dormant segment in your CRM
Select customers with no purchases over the period you care about in Bitrix24, amoCRM, RetailCRM or Megaplan. That's your call list — the agent works through it, and the results flow back into those same contact cards.
- 2
Describe the reactivation scenario
Set the call's goal, the agent persona, the greeting and the tone. The dialogue is natural: the customer answers in their own words, clarifies and interrupts (barge-in) rather than pressing buttons. The TTS voice, language and dialogue LLM are tuned to your market.
- 3
Configure the fields to collect
Set up fields with required flags and validation: is the offer of interest, the reason for leaving, a convenient time, readiness to return. The agent collects them right in the conversation and stores them in the record's fields.
- 4
Hand warm leads straight to a manager
Define the transfer rules: the moment a customer shows interest, the agent transfers the call to a live manager and stays on the line — it keeps recording and shows the operator the collected fields and on-screen hints.
- 5
Break down the reasons for leaving across the whole campaign
Every call is transcribed automatically and tagged with metrics, topics and auto-tags. Dashboards and semantic search surface the top churn reasons, the tone of conversations and which segments come back — across 100% of the campaign, not a sample.
Industry scenario
Working the base after a mortgage-rate cut
Mortgage rates have dropped, and the manager wants to call everyone who searched for an apartment over the past year and a half but fell through on the mortgage or the high price. In the CRM they select a segment: contacts with no deal in the period who have viewings or inquiries in their history. That's the calling list — the voice AI agent works through it by script: it reminds the person they were searching for housing through the agency and asks whether the question is still live now that mortgage terms have eased.
The dialogue is personalized: the client answers in their own words, and the agent collects fields — is the request still relevant, have the budget and area changed, what caused the pause (waiting on the rate, selling their own, no variant found), are they ready to view properties again. One says they already bought — the agent flags the contact closed; another answers that the loan payment works now and they're ready to resume the search — and the agent transfers them straight to an agent, staying on the line and showing the operator the collected fields and prompts.
The outcome of each call returns to the same contact's card in the CRM: updated budget, area, mortgage status, the reason for the past decline. From the calling results the dashboard shows which share of the old base is active again, which areas and price segments are pulled up by the new rate, and why those who won't return went quiet. Live agents work only with hot buyers the agent has already warmed up, instead of burning time on a monotonous cold sweep of the archive.
Key facts
- Where the list comes from
- The dormant segment from your CRM: Bitrix24, amoCRM/Kommo, RetailCRM, Megaplan.
- Who does the calling
- The voice AI agent — an outbound call and natural dialogue on your scenario, with barge-in.
- What gets collected
- Interest, the reason for leaving and a convenient time into configurable fields with required flags and validation.
- Transfer to a human
- Warm customers are handed to a manager by your rules; the agent stays on the line and prompts them.
- Churn analytics
- Metrics, topics and auto-tags on every call; dashboards and semantic search across the whole campaign.
Related scenarios for this industry
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