Industry solution
Shopper reactivation for online stores
For an online store a dormant shopper isn't an abstract contact but a person with an order history, an average check and a favorite category who simply stopped buying. Selling to them again costs a fraction of acquiring new traffic, yet the promo-code email lands unread in the Promotions tab and the push is muted. A voice AI agent calls the "no orders in 90 days" segment from RetailCRM with a real conversation, reminds them of an abandoned cart or a past purchase, and handles the specific objection — too expensive, delivery too slow, the right size wasn't in stock.
Dialing the base by hand always runs into the math for a store: reps go into the red on a long cold list where most either don't pick up or say "thanks, not now." The agent works the whole segment on one script with no added payroll and brings in a live rep only for those ready to buy again. The result of each call — interest, reason for leaving, convenient time — flows back into the same contact's card instead of staying in the rep's memory.
Which industry pains this solves
Abandoned carts go cold
The shopper reached checkout and left. Dialing everyone who didn't finish by hand is impossible — and the shopper never reads the email sequence.
"Where's my order" eats up support
Half of all contacts are about status, timelines and tracking numbers. Agents answer the same thing over and over instead of handling real problems.
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
Calling shoppers after a seasonal sale
After a seasonal sale the store selects, in RetailCRM, shoppers who bought actively last season but haven't placed a single order since. A rule launches an outbound campaign: the voice AI agent reminds them of the store, names the category of their past purchases and invites them back, and if the person mentions an abandoned cart it asks what stopped them — price, the delivery cost, or the right size being out of stock.
The answers land in the record's fields: whether the offer is of interest, the reason for leaving, a convenient time for a follow-up. The moment the shopper says "yes, I'll take a look," the agent transfers the call to a live rep by rule and stays on the line — the rep sees the collected fields and the order history from the CRM on screen and continues with a warm shopper instead of starting from a cold "hello."
Those who decline the agent tags with a status and reason so the store doesn't bother them again and burn budget. The dashboard shows how many dormant shoppers were revived, which objections come up most, and which segments come back better — for example, that apparel buyers leave over sizing rather than price. That's no longer a guess from a couple of calls but a churn map across the whole campaign.
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
Ready to start?
Turn every conversation into data, knowledge and action
Start by analyzing your conversations — no risk, no bots required. The platform turns your archive into data and a knowledge base, and voice agents plug in when you're ready.
Free plan, no card required.