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In-session hesitation rescue

A shopper is stuck on "will this fit, cover me, work for me?" — the agent steps in at that exact moment and turns the doubt into an order.

Next best thingGet aheadBehavioral triggerChatIn-app

What it does

Pre-purchase doubt is where carts die. A visitor lingers on a product page, flips between two tabs comparing options, or goes idle right at the decision point — and then leaves, because the one question holding them back never got answered. The agent reads those live on-page signals, steps in unprompted with help that speaks to the specific doubt, and guides the visitor to the right purchase while the intent is still warm.

How it works

  1. 1
    Trigger. in-session behavioral signals arrive from the page — long dwell on a product, idle at a decision point, repeated switching between comparison tabs, or arrival on a high-intent page.
  2. 2
    Decision. the agent weighs the session context (what the visitor has looked at, what is in the cart, persistent memory if the customer is known) and a quality judge gates whether stepping in is genuinely helpful here — no scripted 15-second popups.
  3. 3
    Action. a short, specific opener appears in the on-page chat, addressed to the doubt the behavior suggests, and the conversation continues toward the purchase.
  4. 4
    Follow-through. if the visitor buys or leaves, any pending nudge is cancelled; if the question needs a human, it escalates with full context; the conversation and outcome are written back for attribution.
Example
AgentI see you're comparing the two trail runners. The main difference is the waterproof membrane on the second pair — are you mostly running in wet conditions?
CustomerMostly dry trails, but I have wide feet and I'm not sure about sizing.
AgentThen the first pair is the better fit — it comes in a wide version, and the size guide recommends sizing up for wide feet. Want me to add the wide version in your size to your cart?

Configuration

How the agent is wired for this use case.

Triggerin-session behavioral signal from the page — long dwell, idle-at-decision-point, repeated comparison-tab switching, or high-intent page arrival — from the web event stream.
Tools & actions
  • Web analytics / event stream · read live page and session signals (dwell, idle, tab switches, cart contents).
  • Knowledge base · pull product attributes and policies (sizing, shipping, returns) to ground the opener.
  • CRM · read customer memory and order history for known visitors.
  • Messaging channel · render the unprompted opener and run the conversation in on-page chat.
  • Cart API · add the recommended item in the right variant and size.
Autonomythe opener fires unattended only after a quality judge clears it; the add-to-cart and the purchase itself stay with the customer.
Channelschat · in-app
Escalationa question the agent can't ground hands off to a human with full session context.

What you need

The inputs this use case runs on. Your channels stay yours; the agent supplies the judgment.

Signals

page and session events — page_view, dwell time, idle-at-decision-point, tab switches, cart contents.

Data

product catalog with attributes and policies (sizing, shipping, returns); customer memory and order history for known visitors.

Guardrails

every unprompted message passes a quality judge before it renders; frequency caps per session; suppression for visitors who dismissed the widget; holdout group to prove the lift is real.

Metrics it moves

  • conversion-rateup: doubts get answered at the moment they form, before the visitor abandons.
  • revenue-per-conversationup: each conversation is anchored to a live purchase decision, not a support queue.
  • aovup: guided answers steer visitors to the right product instead of the cheapest safe choice.

Related use cases

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Checkout-stall rescue

same rescue, one step later in the funnel

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Guided selling concierge

when the visitor states the need outright

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Pre-sale product Q&A and objection handling

the reactive twin on the product page

See it on your own customer journey

Bring one drop-off, one churn cliff, or one silent segment. We will show you what a proactive agent with memory and judgment does with it.

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