Use case · reactivation
Reactivating dormant customers with a voice AI agent
Your base of dormant customers won't call itself, and calling it by hand is too expensive and too slow. The voice AI agent works through a segment from your CRM for you — it holds a natural, personalized conversation, captures interest and the reason for leaving straight into record fields, and hands warm leads to a manager on the spot. Every call flows into analytics, and you finally see why customers left — across the whole volume, not from a couple of listened-to calls.
The problem
Every customer base accumulates a layer of people who once bought and then went quiet. On paper they still "exist," but they generate no revenue: reactivating them is cheaper than acquiring new ones, yet calling everyone by hand doesn't pay off — operators go into the red working a long list of cold contacts.
So dormant customers are either left alone entirely or hit with the same mass email that nobody reads. A live conversation would work better, but putting managers on a monotonous run through the base means burning payroll on contacts most of whom won't answer or will decline.
And even when the outreach does happen, the company doesn't understand why customers left. The reasons for churn live in operators' memory and in a couple of randomly listened-to recordings. There's no objective picture of objections, no sense of which segment actually comes back and which is already lost for good.
How the platform solves it
- 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.
The result
The dormant base stops being dead weight: the agent works through it with no payroll growth, live managers handle only warm leads, and for the first time you see the real reasons for churn.
- The entire CRM segment is called with a personalized live dialogue instead of the same mass email.
- Managers spend time only on customers the agent has already warmed up and qualified.
- Interest and the reason for leaving are captured in each record's fields and pushed back to the CRM cards.
- An objective map of churn reasons across the whole campaign instead of guesswork and a couple of listened-to calls.
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.
FAQ
Frequently asked questions
How is this different from a regular auto-dialer running through a list?
The agent holds a natural conversation in the customer's language, understands free-form answers and lets itself be interrupted (barge-in) rather than pushing people through a tree of touch-tone menus. It personalizes the conversation to the customer, collects structured fields and decides on its own when to transfer a warm lead to a manager.
How does the agent know whom to call?
You build the dormant segment in your own CRM — for example, customers with no purchases over a chosen period. The platform integrates with Bitrix24, amoCRM/Kommo, RetailCRM and Megaplan, so the results of the campaign (interest, reason for leaving, fields) flow back into those same contact cards.
What happens when a customer shows interest?
You set the transfer rules, and the agent hands the call to a live manager accordingly. After the transfer the agent stays in the conversation: it keeps recording and shows the operator the collected fields, a summary and real-time hints on screen.
How do I learn the real reasons customers leave?
Every agent call is transcribed automatically and tagged with metrics, topics and auto-tags, then flows into your overall analytics. Dashboards and semantic search give you a map of churn reasons and conversation tone across the whole campaign — over 100% of contacts, not a few listened-to recordings.
Do I have to pay for managers to call the whole base?
No — the agent works through the base, and live managers only step in for warm customers it has already qualified. The campaign is billed by usage (speech-recognition and speech-synthesis minutes plus dialogue-model tokens), and payroll for monotonous cold calling doesn't grow.
Solutions
By industry
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Learn moreShopper reactivation for online stores
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