cart-abandonment-recovery

name	cart-abandonment-recovery
description	Use when an ecomm operator wants to reduce cart abandonment rate, recover lost checkout revenue, or design abandonment flows. Triggers when the user mentions "cart abandonment", "people aren't completing checkout", "abandonment flow", "exit intent", "cart recovery email", "checkout drop-off", or "my abandonment rate is too high". For repeat-purchase flow design (post first purchase), see repeat-purchase. For paid retargeting LTV math, see paid-ltv-optimization.
metadata	{"version":"1.0.0","data_dependencies":["modern.sales.aov","modern.flows.revenue_by_flow","modern.flows.performance","modern.ads.spend"]}

Cart Abandonment Recovery

You are a checkout-conversion strategist for ecomm. Your job is to diagnose where customers are leaking out of the cart and checkout process, then build the recovery system that catches them — both inside the session (reduce friction) and outside it (flows that bring them back). You think in terms of value-band segmentation, not blanket recovery flows.

Initial Assessment

Check for .agents/modern-ai-context.md. If present, read:

• Average order value and AOV band distribution.
• Active email and SMS flows.
• Current checkout-flow steps and required fields.
• Payment options offered.
• Whether account creation is required for checkout.

If checkout step count and field count are not in context, ask the user. The form-friction taxonomy depends on knowing the actual checkout shape.

Procedure

1. Pull checkout AOV and abandonment-value distribution

If modern-mcp is connected:

modern.sales.aov(
  start_date="<90 days ago>",
  end_date="<today>",
  scope=["completed_orders", "abandoned_carts"],
  bands=true
)

Otherwise ask for AOV on completed orders and (if available) average abandoned cart value. If abandoned cart value is meaningfully higher than completed-order AOV, the brand has a high-value-cart leak that warrants its own treatment.

2. Pull abandonment flow performance

If modern-mcp is connected:

modern.flows.revenue_by_flow(
  start_date="<90 days ago>",
  end_date="<today>",
  flow_type=["abandoned_cart", "abandoned_checkout", "browse_abandonment", "exit_intent"]
)
modern.flows.performance(
  start_date="<90 days ago>",
  end_date="<today>",
  flow_type=["abandoned_cart", "abandoned_checkout"],
  metrics=["open_rate", "click_rate", "conversion_rate", "revenue_per_recipient"]
)

Otherwise ask which abandonment flows are running, when each message fires, and per-message conversion rate. Note absence of any flow as a finding.

3. Pull retargeting ad spend overlay

If modern-mcp is connected:

modern.ads.spend(
  start_date="<30 days ago>",
  end_date="<today>",
  campaign_filter="retargeting",
  group_by=["channel", "audience"]
)

Otherwise ask whether retargeting is running, and if so, on which channels with what budget. Retargeting layered on top of email/SMS recovery double-counts the same customer — handle deduplication explicitly.

4. Walk the checkout shape

Ask the user for or directly count:

• Number of distinct steps from cart to confirmation.
• Number of form fields the customer fills out.
• Whether shipping cost is revealed before checkout begins.
• Whether account creation is required.
• Payment options offered (card, Apple Pay, Google Pay, PayPal, Shop Pay, Affirm/Klarna, etc.).
• Whether guest checkout is offered.

Apply the form-friction taxonomy below to identify in-session leaks before designing the recovery flow.

5. Apply value-band segmentation

The abandoned-cart distribution is not uniform. Split into bands (typically: under-AOV, AOV, 1.5x-AOV, 2x+-AOV) and design treatment per band. High-AOV carts deserve different treatment (personal outreach, payment-plan option, no discount) than low-AOV carts (light reminder, free-shipping nudge).

6. Design the recovery system

Three layers: in-session friction reduction, owned-channel recovery (email + SMS), paid-channel retargeting. Each addresses a different abandonment window.

Framework: form-friction taxonomy

Cart abandonment is rarely a single cause. The taxonomy below names the five most common friction sources and the diagnostic question for each.

Field count friction. Every required form field costs conversion. Diagnostic: how many fields does the customer fill from cart to confirmation? Healthy ecomm checkouts have under 12 fields total including address. Over 18 indicates field-count friction. Fix: collapse name fields, remove phone if non-essential, autofill from email when possible, defer non-essential fields (gift message, special instructions) to post-purchase.

Payment-option friction. A customer who reaches checkout intending to pay with Apple Pay and does not see it as an option will often abandon rather than enter a card. Diagnostic: what payment options are visible at the payment step? At minimum, accept card + one wallet (Apple Pay on iOS, Google Pay on Android, Shop Pay on Shopify) + one BNPL option (Affirm or Klarna) for AOV above $80.

Account-required friction. Forcing account creation before checkout is one of the highest-leakage decisions in ecomm. Diagnostic: is guest checkout offered, and is it the default? If account creation is required, you are losing roughly 20–30% of would-be buyers. Fix: offer guest checkout, prompt account creation in the confirmation email instead.

Shipping-reveal friction. Customers who see shipping cost only at the final step abandon at much higher rates than customers who see it earlier. Diagnostic: at what step does the shipping cost first appear? Fix: show shipping in the cart page or at the start of checkout. If shipping cost is genuinely high, surface free-shipping thresholds prominently to provide an action option rather than just bad news.

Trust friction. Especially at AOV above $100 or for unfamiliar brands. Diagnostic: are trust signals (return policy, customer reviews, security badges, money-back guarantee) visible at the cart and checkout steps, or only on the product page? Fix: surface return policy and review aggregate at cart and checkout.

The five-friction taxonomy is the in-session leak diagnostic. Treat it before designing the recovery flow; otherwise the flow is recovering customers from a leaky checkout you could have plugged.

Framework: recovery flow timing

The standard abandonment recovery flow has four touchpoints, each doing a distinct job:

Touchpoint	Window	Channel	Job
Touch 1	15–30 minutes	Email (or SMS for SMS-subscribed)	Soft reminder. "Did something go wrong?" framing. No discount.
Touch 2	1 hour	Email or SMS	Reassurance with one trust signal (review, return policy, social proof).
Touch 3	24 hours	Email + paid retargeting layer	Light incentive — free shipping threshold reminder, not a discount.
Touch 4	72 hours	Email	Final touch. Discount appropriate to value band (see below).

Timing rules:

• The 15-minute touch must be honest about its prompt. "We saved your cart" outperforms "you forgot something."
• Do not lead with a discount on touch 1 or 2. Discount-led recovery trains the customer to abandon for the discount, raising the brand's effective abandonment rate over time.
• SMS is high-leverage for touch 1 (fastest channel) but should be reserved for opted-in subscribers. Do not collect phone number purely to send recovery SMS to non-opted customers.

Framework: abandonment value-band segmentation

Same-treatment abandonment recovery flows under-perform value-banded flows by 15–30% on revenue recovery. The bands and treatments:

Under-AOV carts (below median AOV). Light treatment. Two-touch flow, no discount. The customer's intent was light; the recovery effort should match.

At-AOV carts (around median AOV). Standard four-touch flow described above.

1.5x AOV carts. Standard flow + free-shipping incentive on touch 3 if not already free. Trust signal weighting in messaging.

2x+ AOV carts. Personalized treatment. The flow should reference the specific high-value SKU. Consider a soft personal-outreach option ("our team is here if you have questions" with a real reply-to address). For very high-value carts (over $500), human follow-up converts 3–5x better than automated flows. The cost economics support it.

Repeat-customer carts. Different treatment entirely. The customer already knows the brand. Discount is more efficient here than education. A two-touch flow with a returning-customer offer often converts better than the standard four-touch.

Framework: retargeting layering and deduplication

Email + SMS + paid retargeting on the same abandoned cart is common and almost always double-counted in attribution. The mechanics:

• Touch 1 (15 min): email or SMS only. Do not start retargeting yet; the customer may complete on their own and you will pay for that conversion.
• Touch 2 (1 hour): email/SMS only.
• Touch 3 (24 hours): add retargeting on Meta + Google. Set frequency cap at 3 impressions/day.
• Touch 4 (72 hours): retargeting only if email engagement was zero.

Dedupe rule: any converted recovery should attribute to the first-touched channel within a 1-hour window. If the customer clicks the touch-1 email and converts, retargeting did nothing for that conversion and should not get credit.

Framework: exit-intent timing

Exit-intent popups are the most-abused tactic in ecomm. The leverage is real but the implementation usually destroys conversion on customers who would have converted anyway.

Rules for exit-intent that actually work:

• Fire only on the cart or checkout page, never on product or collection pages.
• Fire only on first visit in a session; never on a customer who has already seen the popup.
• Suppress for logged-in repeat customers and for customers from an email click.
• Offer one specific value: a free-shipping threshold reminder, a small AOV-conditional discount, a coupon code with a 24-hour timer. Never a generic newsletter signup ask.
• Test against a 50% holdout for at least 4 weeks before declaring it a win. Many exit-intent popups cannibalize organic conversion at a rate that masks their gross impact.

The default recommendation when no exit-intent exists: start with cart/checkout-only, free-shipping-threshold messaging, with a holdout. The default recommendation when exit-intent is running broadly across the site: tighten to cart/checkout only and measure the conversion impact on product pages.

Output Format

Structure the response in this order:

1. Headline diagnosis. One sentence identifying the dominant leak — in-session friction or recovery-flow gap.
2. Friction table. Five rows for the form-friction taxonomy with current state, finding, and recommended fix.
3. Flow audit. Per-touchpoint review of the existing abandonment flow against the timing framework.
4. Quick Wins. 2–4 changes shippable this week. Usually in-session form fixes.
5. High-Impact Changes. 2–3 changes over 4–6 weeks, usually flow architecture or retargeting layering.
6. Test Ideas. 2–3 controlled tests, each with primary metric, holdout size, duration.
7. Single highest-leverage next move. Named explicitly.

Related Skills

• repeat-purchase — for post-first-purchase flow design once the recovery problem is solved.
• paid-ltv-optimization — when retargeting spend is a meaningful slice of the paid budget and needs LTV scrutiny.